diff --git a/Dockerfile b/Dockerfile
index 8eba743..aa8afe7 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -4,6 +4,6 @@ WORKDIR /code
 
 COPY ./requirements.txt /code/requirements.txt
 
-RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt -v
 
 CMD [ "uvicorn", "main:app","--proxy-headers", "--host", "0.0.0.0", "--port","80" ]
diff --git a/docker-compose.yml b/docker-compose.yml
index 7dcb6d2..631e0e6 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -12,4 +12,3 @@ services:
       - .:/code
     environment:
       - ENVIRONMENT=production
-
diff --git a/m4a2wav.py b/m4a2wav.py
new file mode 100644
index 0000000..338f9bb
--- /dev/null
+++ b/m4a2wav.py
@@ -0,0 +1,27 @@
+import os
+from pydub import AudioSegment
+
+def convert_m4a_to_wav(input_path):
+    # 입력 경로의 모든 파일 검색
+    for root, dirs, files in os.walk(input_path):
+        for file in files:
+            # m4a 파일만 처리
+            if file.endswith('.m4a'):
+                # 파일의 전체 경로
+                m4a_path = os.path.join(root, file)
+                # wav 파일명 생성 (확장자만 변경)
+                wav_path = os.path.splitext(m4a_path)[0] + '.wav'
+                
+                try:
+                    # m4a 파일 로드
+                    audio = AudioSegment.from_file(m4a_path, format='m4a')
+                    # wav 파일로 저장
+                    audio.export(wav_path, format='wav')
+                    print(f'변환 완료: {file}')
+                except Exception as e:
+                    print(f'변환 실패: {file} - {str(e)}')
+
+if __name__ == "__main__":
+    # 변환하고자 하는 폴더 경로 지정
+    input_folder = "/Users/bangbyeonghun/Documents/projects/grad/ai-server/tortoise/tortoise/voices/rose"
+    convert_m4a_to_wav(input_folder)
diff --git a/main.py b/main.py
index 7481767..9fd392d 100644
--- a/main.py
+++ b/main.py
@@ -1,11 +1,13 @@
 from typing import Union
-from fastapi import FastAPI
+from fastapi import FastAPI, UploadFile, File, Form
 from routers import websocket
 from fastapi.responses import FileResponse
+import os
+import shutil
 
 
 app = FastAPI()
-app.include_router(websocket.router)
+#app.include_router(websocket.router)
 path = './templates/index.html'
 audio_path = './templates/audio.html'
 audio_file_path = './templates/audio_file.html'
@@ -15,7 +17,18 @@
 async def get():
     return FileResponse(text_to_audio_path)
 
-@app.get("/items/{item_id}")
-def read_item(item_id : int, q:Union[str, None] = None):
-    return {"item_id" : item_id, "q" : q}
-    
\ No newline at end of file
+@app.post("/audio")
+async def upload_audio(audio_file: UploadFile = File(...), user_id: str = Form(...)):
+    # 사용자 ID를 기반으로 저장 디렉토리 설정
+    save_directory = f"tortoise/tortoise/voices/{user_id}"
+    
+    # 디렉토리가 없으면 생성
+    os.makedirs(save_directory, exist_ok=True)
+    
+    # 파일 저장
+    file_path = os.path.join(save_directory, audio_file.filename)
+    with open(file_path, "wb") as buffer:
+        shutil.copyfileobj(audio_file.file, buffer)
+    
+    return {"message": f"사용자 {user_id}의 오디오 파일이 성공적으로 업로드되었습니다.", "file_name": audio_file.filename, "user_id": user_id}
+
diff --git a/requirements2.txt b/requirements2.txt
new file mode 100644
index 0000000..3df7aea
--- /dev/null
+++ b/requirements2.txt
@@ -0,0 +1,5 @@
+uvicorn
+fastapi
+python-multipart
+websockets
+spacy
\ No newline at end of file
diff --git a/routers/websocket.py b/routers/websocket.py
index 06aef40..32f3773 100644
--- a/routers/websocket.py
+++ b/routers/websocket.py
@@ -1,217 +1,73 @@
-from fastapi import APIRouter, WebSocket, WebSocketDisconnect
-import asyncio
-import os
-import uuid
-import json
-from queue import Queue
-from datetime import datetime, timedelta
-from time import sleep
-import threading
-from typing import List
-import logging
-import wave # <- 임시로 import하는거. 추후 삭제 필요.
-
-router = APIRouter()
-
-# Thread safe Queue for passing data from the threaded recording callback.
-text_queue = Queue()
-
-# Thread safe Queue for passing result from STT Thread
-result_queue = Queue()
-
-# EventObject for Stopping Thread
-stop_event = threading.Event()
-
-# ======================== new project 08/23 ======================== #
-# 로그 확인용 <- 추후 삭제
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger("websocket")
-
-async def process_tts(text: str) -> bytes:
-    # TTS 처리 (여기서는 단순히 바이너리 데이터를 반환하는 예시)
-    # 실제로는 TTS 엔진을 사용하여 음성 데이터를 생성
-    await asyncio.sleep(1)  # TTS 처리 시간 시뮬레이션
-    response = b"dummy binary audio data"
-    logger.info(f"Sending message: {response}")
-    return b"dummy binary audio data"  # 추후 실제 음성 데이터로 대체
-
-@router.websocket("/ws/new")
-async def websocket_TTS(websocket: WebSocket):
-    await websocket.accept()
-    try:
-        # Worker 코루틴을 별도로 실행하여 Queue 처리
-        asyncio.create_task(worker(websocket))
-
-        while True:
-            data = await websocket.receive_text()
-            logger.info(f"Received message: {data}")
-            words = data.split()
-            
-            chunk_size = 7 # 이 부분은 성능에 따라서 변경 가능
-            chunks = [words[i:i+chunk_size] for i in range(0,len(words), chunk_size)]
-            for chunk in chunks:
-                text_queue.put(chunk)
-
-    except WebSocketDisconnect:
-        print("WebSocket connection closed")
-
-# Queue를 처리하는 작업자 함수
-async def worker(websocket : WebSocket):
-    while True:
-        if not text_queue.empty():
-            text = text_queue.get()
-            audio_data = await process_tts(text)
-            logger.info("----input----")
-            await websocket.send_bytes(audio_data)
-            logger.info("----output-----")
-        await asyncio.sleep(0.1)  # Queue가 비어있는지 확인하는 주기
-
-# ================================================================================================ #
-def read_wav(file_path):
-    with wave.open(file_path, 'rb') as wav_file:
-        frames = wav_file.readframes(wav_file.getnframes())
-    return 
-
-async def textToSpeech(stop_event):
-    print("[TTSThread] TTS Thread Executed")
-
-    while not stop_event.is_set():
-        sleep(3)
-        try:
-            now = datetime.utcnow()
-            # pop text from the queue.
-            if not text_queue.empty():
-                audio_file_path = "./data/received_audio.wav"
-                processed_result = read_wav(audio_file_path)
-                if processed_result != None:
-                    result_queue.put(processed_result)
-
-        except Exception as e:
-            print(f"[TTSThread] Error processing text data: {e}")
-            break
-
-    print("[TTSThread] TTS Thread Destroyed")
-
-async def process_thread(websocket: WebSocket):
-    print("[ProcessTask] Process Task Initiated")
-    while not stop_event.is_set():
-        await asyncio.sleep(0.25)
-        try:
-            if not result_queue.empty():
-                audio_result = result_queue.get()
-                while chunk := audio_result.read(1024):
-                    await websocket.send_bytes(chunk)
-                
-        except Exception as e:
-            print(f"[ProcessTask] Error : {e}")
-            break
+# from fastapi import APIRouter, WebSocket, WebSocketDisconnect
+# from whisper_live import WhisperModel
+# import asyncio
+# import tempfile
+
+# router = APIRouter()
+
+# # Initialize Whisper Model (Specify the desired model name)
+# model_name = "small"  # You can use "tiny", "small", "medium", or "large"
+# whisper_model = WhisperModel(model_name=model_name)
+
+# @router.websocket("/ws")
+# async def websocket_endpoint(websocket: WebSocket):
+#     print("Configuring BE Socket")
+#     await websocket.accept()
+#     print("BE Socket Accepted")
     
-    print("[ProcessTask] Process Task Destroyed")
-
-async def websocket_task(websocket: WebSocket):
-    # Load Model Here 
-    device = 'cuda'
-    print("[WebSocketTask]-[****] Model Loaded Successfully with", device)
-
-    # Accept WebSocket
-    #connection_uuid = await websocket.receive_bytes()
-    #print("[WebSocketTask] Connection [" + connection_uuid + "] Accepted")
-
-    # Execute TTS Thread until WebSocket Disconnected
-    
-    ttsThread = threading.Thread(target=textToSpeech, args=(stop_event,))
-    ttsThread.start()
-
-    processTask = asyncio.create_task(process_thread(websocket))
-
-    # Receive Text
-    try:
-        while True:
-            text_data = await websocket.receive_text()
-            text_queue.put(text_data)
-
-            # Sleep for other async functions
-            await asyncio.sleep(0)
-
-    except Exception as e:
-        print(f"[WebSocketTask] WebSocket error: {e}")
-    finally:
-        stop_event.set()
-        ttsThread.join()
-        processTask.cancel()
-
-        # clear stop_event for next Socket Connection
-        stop_event.clear()
-
-        while not text_queue.empty():
-            text_queue.get()
-
-        while not result_queue.empty():
-            result_queue.get()
-
-        if websocket.client_state.name != "DISCONNECTED":
-            await websocket.close()
-
-        print("[WebSocketTask] Connection Closed")
-
-#======= WebSocket ========#
-@router.websocket("/tts/ws")
-async def tts_endpoint(websocket: WebSocket):
-    print("Configuring BE Socket")
-    await websocket.accept()
-    print("BE Socket Accepted")
-    
-    await asyncio.create_task(websocket_task(websocket))
+#     while True:
+#         data = await websocket.receive_text()
+#         await websocket.send_text(f"Echo: {data}")
+
+# @router.websocket("/audio/ws")
+# async def websocket_audio_endpoint(websocket: WebSocket):
+#     await websocket.accept()
+#     print("WebSocket connection accepted.")
+
+#     try:
+#         # Prepare an async queue to hold incoming audio data
+#         audio_queue = asyncio.Queue()
+
+#         # Function to process audio data from the queue
+#         async def process_audio_data():
+#             # Use a temporary file to write the received audio
+#             with tempfile.NamedTemporaryFile(suffix=".wav") as audio_file:
+#                 while True:
+#                     # Retrieve audio data from the queue
+#                     data = await audio_queue.get()
+#                     if data is None:
+#                         break
+
+#                     # Write the data to the temporary file
+#                     audio_file.write(data)
+#                     audio_file.flush()
+
+#                     # Perform the transcription
+#                     transcription = whisper_model.transcribe(audio_file.name)
+
+#                     # Send the transcription back to the client
+#                     await websocket.send_text(transcription)
+#                     print(f"Transcribed text: {transcription}")
+
+#         # Start the audio processing task
+#         audio_task = asyncio.create_task(process_audio_data())
+
+#         while True:
+#             # Receive audio data as bytes
+#             data = await websocket.receive_bytes()
+            
+#             # Add the received audio data to the queue for processing
+#             await audio_queue.put(data)
 
-@router.websocket("/ws")
-async def websocket_test_endpoint(websocket: WebSocket):
-    print("Configuring BE Socket")
-    await websocket.accept()
-    print("BE Socket Accepted")
-    
-    while True:
-        data = await websocket.receive_text()
-        await websocket.send_text(f"Echo: {data}")
+#     except WebSocketDisconnect:
+#         print("WebSocket connection closed by client.")
 
-@router.websocket("/audio/ws")
-async def websocket_audio_test_endpoint(websocket: WebSocket):
-    await websocket.accept()
-    print("WebSocket connection accepted.")
-    
-    try:
-        while True:
-            # 음성 데이터를 바이너리로 수신
-            data = await websocket.receive_bytes()
-            # 데이터 처리 예: 파일로 저장
-            with open("received_audio.wav", "wb") as f:
-                f.write(data)
-            # 클라이언트에 데이터 전송 (예: 수신 완료 알림)
-            await websocket.send_text("Audio received and saved.")
-    except Exception as e:
-        print("Error:", e)
-    finally:
-        print("WebSocket connection closed.")
+#     except Exception as e:
+#         print(f"Error: {e}")
 
-#======= WebSocket ========#
-@router.websocket("/tts")
-async def websocket_tts_test_endpoint(websocket: WebSocket):
-    print("Configuring BE Socket")
-    await websocket.accept()
-    print("BE Socket Accepted")
-    try:
-        while True:
-            data = await websocket.receive_text()
-            print(f"Received text data: {data}")
-            audio_file_path = "./data/received_audio.wav"
-            if os.path.exists(audio_file_path):
-                with open(audio_file_path, "rb") as audio_file:
-                    while chunk := audio_file.read(1024):
-                        await websocket.send_bytes(chunk)
-                    #processed_result = read_wav(audio_file_path)
-                    #await websocket.send_bytes(processed_result)
-            else:
-                    await websocket.send_text("Audio file not found.")
-    except Exception as e:
-        print("Exception as",e)
-    finally:
-        print("WebSocket connection closed.")
\ No newline at end of file
+#     finally:
+#         # Notify the audio processing task to exit
+#         await audio_queue.put(None)
+#         # Wait for the audio processing task to complete
+#         await audio_task
+#         print("WebSocket connection closed.")
\ No newline at end of file
diff --git a/tortoise/.github/workflows b/tortoise/.github/workflows
new file mode 100644
index 0000000..bdaab28
--- /dev/null
+++ b/tortoise/.github/workflows
@@ -0,0 +1,39 @@
+# This workflow will upload a Python Package using Twine when a release is created
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries
+
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+name: Upload Python Package
+
+on:
+  release:
+    types: [published]
+
+permissions:
+  contents: read
+
+jobs:
+  deploy:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+    - name: Set up Python
+      uses: actions/setup-python@v3
+      with:
+        python-version: '3.x'
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install build
+    - name: Build package
+      run: python -m build
+    - name: Publish package
+      uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
+      with:
+        user: __token__
+        password: ${{ secrets.PYPI_API_TOKEN }}
diff --git a/tortoise/.gitignore b/tortoise/.gitignore
new file mode 100644
index 0000000..7693938
--- /dev/null
+++ b/tortoise/.gitignore
@@ -0,0 +1,135 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+.idea/*
+.models/*
+.custom/*
+results/*
+debug_states/*
\ No newline at end of file
diff --git a/tortoise/Advanced_Usage.md b/tortoise/Advanced_Usage.md
new file mode 100644
index 0000000..94059f0
--- /dev/null
+++ b/tortoise/Advanced_Usage.md
@@ -0,0 +1,103 @@
+## Advanced Usage
+
+### Generation settings
+
+Tortoise is primarily an autoregressive decoder model combined with a diffusion model. Both of these have a lot of knobs
+that can be turned that I've abstracted away for the sake of ease of use. I did this by generating thousands of clips using
+various permutations of the settings and using a metric for voice realism and intelligibility to measure their effects. I've
+set the defaults to the best overall settings I was able to find. For specific use-cases, it might be effective to play with
+these settings (and it's very likely that I missed something!)
+
+These settings are not available in the normal scripts packaged with Tortoise. They are available, however, in the API. See
+```api.tts``` for a full list.
+
+### Prompt engineering
+
+Some people have discovered that it is possible to do prompt engineering with Tortoise! For example, you can evoke emotion
+by including things like "I am really sad," before your text. I've built an automated redaction system that you can use to
+take advantage of this. It works by attempting to redact any text in the prompt surrounded by brackets. For example, the
+prompt "\[I am really sad,\] Please feed me." will only speak the words "Please feed me" (with a sad tonality).
+
+### Playing with the voice latent
+
+Tortoise ingests reference clips by feeding them through individually through a small submodel that produces a point latent,
+then taking the mean of all of the produced latents. The experimentation I have done has indicated that these point latents
+are quite expressive, affecting everything from tone to speaking rate to speech abnormalities.
+
+This lends itself to some neat tricks. For example, you can combine feed two different voices to tortoise and it will output
+what it thinks the "average" of those two voices sounds like.
+
+#### Generating conditioning latents from voices
+
+Use the script `get_conditioning_latents.py` to extract conditioning latents for a voice you have installed. This script
+will dump the latents to a .pth pickle file. The file will contain a single tuple, (autoregressive_latent, diffusion_latent).
+
+Alternatively, use the api.TextToSpeech.get_conditioning_latents() to fetch the latents.
+
+#### Using raw conditioning latents to generate speech
+
+After you've played with them, you can use them to generate speech by creating a subdirectory in voices/ with a single
+".pth" file containing the pickled conditioning latents as a tuple (autoregressive_latent, diffusion_latent).
+
+## Tortoise-detect
+
+Out of concerns that this model might be misused, I've built a classifier that tells the likelihood that an audio clip
+came from Tortoise.
+
+This classifier can be run on any computer, usage is as follows:
+
+```commandline
+python tortoise/is_this_from_tortoise.py --clip=<path_to_suspicious_audio_file>
+```
+
+This model has 100% accuracy on the contents of the results/ and voices/ folders in this repo. Still, treat this classifier
+as a "strong signal". Classifiers can be fooled and it is likewise not impossible for this classifier to exhibit false
+positives.
+
+## Model architecture
+
+Tortoise TTS is inspired by OpenAI's DALLE, applied to speech data and using a better decoder. It is made up of 5 separate
+models that work together. I've assembled a write-up of the system architecture here:
+[https://nonint.com/2022/04/25/tortoise-architectural-design-doc/](https://nonint.com/2022/04/25/tortoise-architectural-design-doc/)
+
+## Training
+
+These models were trained on my "homelab" server with 8 RTX 3090s over the course of several months. They were trained on a dataset consisting of
+~50k hours of speech data, most of which was transcribed by [ocotillo](http://www.github.com/neonbjb/ocotillo). Training was done on my own
+[DLAS](https://github.com/neonbjb/DL-Art-School) trainer.
+
+I currently do not have plans to release the training configurations or methodology. See the next section..
+
+## Ethical Considerations
+
+Tortoise v2 works considerably better than I had planned. When I began hearing some of the outputs of the last few versions, I began
+wondering whether or not I had an ethically unsound project on my hands. The ways in which a voice-cloning text-to-speech system
+could be misused are many. It doesn't take much creativity to think up how.
+
+After some thought, I have decided to go forward with releasing this. Following are the reasons for this choice:
+
+1. It is primarily good at reading books and speaking poetry. Other forms of speech do not work well.
+2. It was trained on a dataset which does not have the voices of public figures. While it will attempt to mimic these voices if they are provided as references, it does not do so in such a way that most humans would be fooled.
+3. The above points could likely be resolved by scaling up the model and the dataset. For this reason, I am currently withholding details on how I trained the model, pending community feedback.
+4. I am releasing a separate classifier model which will tell you whether a given audio clip was generated by Tortoise or not. See `tortoise-detect` above.
+5. If I, a tinkerer with a BS in computer science with a ~$15k computer can build this, then any motivated corporation or state can as well. I would prefer that it be in the open and everyone know the kinds of things ML can do.
+
+### Diversity
+
+The diversity expressed by ML models is strongly tied to the datasets they were trained on.
+
+Tortoise was trained primarily on a dataset consisting of audiobooks. I made no effort to
+balance diversity in this dataset. For this reason, Tortoise will be particularly poor at generating the voices of minorities
+or of people who speak with strong accents.
+
+## Looking forward
+
+Tortoise v2 is about as good as I think I can do in the TTS world with the resources I have access to. A phenomenon that happens when
+training very large models is that as parameter count increases, the communication bandwidth needed to support distributed training
+of the model increases multiplicatively. On enterprise-grade hardware, this is not an issue: GPUs are attached together with
+exceptionally wide buses that can accommodate this bandwidth. I cannot afford enterprise hardware, though, so I am stuck.
+
+I want to mention here
+that I think Tortoise could be a **lot** better. The three major components of Tortoise are either vanilla Transformer Encoder stacks
+or Decoder stacks. Both of these types of models have a rich experimental history with scaling in the NLP realm. I see no reason
+to believe that the same is not true of TTS.
diff --git a/tortoise/CHANGELOG.md b/tortoise/CHANGELOG.md
new file mode 100644
index 0000000..df437c2
--- /dev/null
+++ b/tortoise/CHANGELOG.md
@@ -0,0 +1,36 @@
+## Changelog
+#### v3.0.0; 2023/10/18
+- Added fast inference for tortoise with HiFi Decoder (inspired by xtts by [coquiTTS](https://github.com/coqui-ai/TTS) 🐸, check out their multilingual model for noncommercial uses)
+#### v2.8.0; 2023/9/13
+- Added custom tokenizer for non-english models
+#### v2.7.0; 2023/7/26
+- Bug fixes
+- Added Apple Silicon Support
+- Updated Transformer version
+#### v2.6.0; 2023/7/26
+- Bug fixes
+
+#### v2.5.0; 2023/7/09
+- Added kv_cache support 5x faster
+- Added deepspeed support 10x faster
+- Added half precision support
+  
+#### v2.4.0; 2022/5/17
+- Removed CVVP model. Found that it does not, in fact, make an appreciable difference in the output.
+- Add better debugging support; existing tools now spit out debug files which can be used to reproduce bad runs.
+
+#### v2.3.0; 2022/5/12
+- New CLVP-large model for further improved decoding guidance.
+- Improvements to read.py and do_tts.py (new options)
+
+#### v2.2.0; 2022/5/5
+- Added several new voices from the training set.
+- Automated redaction. Wrap the text you want to use to prompt the model but not be spoken in brackets.
+- Bug fixes
+
+#### v2.1.0; 2022/5/2
+- Added ability to produce totally random voices.
+- Added ability to download voice conditioning latent via a script, and then use a user-provided conditioning latent.
+- Added ability to use your own pretrained models.
+- Refactored directory structures.
+- Performance improvements & bug fixes.
diff --git a/tortoise/CITATION.cff b/tortoise/CITATION.cff
new file mode 100644
index 0000000..e155703
--- /dev/null
+++ b/tortoise/CITATION.cff
@@ -0,0 +1,10 @@
+cff-version: 1.3.0
+message: "If you use this software, please cite it as below."
+authors:
+- family-names: "Betker"
+  given-names: "James"
+  orcid: "https://orcid.org/my-orcid?orcid=0000-0003-3259-4862"
+title: "TorToiSe text-to-speech"
+version: 2.0
+date-released: 2022-04-28
+url: "https://github.com/neonbjb/tortoise-tts"
\ No newline at end of file
diff --git a/tortoise/Dockerfile b/tortoise/Dockerfile
new file mode 100644
index 0000000..bf5d59e
--- /dev/null
+++ b/tortoise/Dockerfile
@@ -0,0 +1,34 @@
+FROM nvidia/cuda:12.2.0-base-ubuntu22.04
+
+COPY . /app
+
+RUN apt-get update && \
+    apt-get install -y --allow-unauthenticated --no-install-recommends \
+    wget \
+    git \
+    && apt-get autoremove -y \
+    && apt-get clean -y \
+    && rm -rf /var/lib/apt/lists/*
+
+ENV HOME "/root"
+ENV CONDA_DIR "${HOME}/miniconda"
+ENV PATH="$CONDA_DIR/bin":$PATH
+ENV CONDA_AUTO_UPDATE_CONDA=false
+ENV PIP_DOWNLOAD_CACHE="$HOME/.pip/cache"
+ENV TORTOISE_MODELS_DIR="$HOME/tortoise-tts/build/lib/tortoise/models"
+
+RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O /tmp/miniconda3.sh \
+    && bash /tmp/miniconda3.sh -b -p "${CONDA_DIR}" -f -u \
+    && "${CONDA_DIR}/bin/conda" init bash \
+    && rm -f /tmp/miniconda3.sh \
+    && echo ". '${CONDA_DIR}/etc/profile.d/conda.sh'" >> "${HOME}/.profile"
+
+# --login option used to source bashrc (thus activating conda env) at every RUN statement
+SHELL ["/bin/bash", "--login", "-c"]
+
+RUN conda create --name tortoise python=3.9 numba inflect \
+    && conda activate tortoise \
+    && conda install pytorch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 pytorch-cuda=12.1 -c pytorch -c nvidia \
+    && conda install transformers=4.31.0 \
+    && cd /app \
+    && python setup.py install
diff --git a/tortoise/LICENSE b/tortoise/LICENSE
new file mode 100644
index 0000000..261eeb9
--- /dev/null
+++ b/tortoise/LICENSE
@@ -0,0 +1,201 @@
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diff --git a/tortoise/MANIFEST.in b/tortoise/MANIFEST.in
new file mode 100644
index 0000000..d19c969
--- /dev/null
+++ b/tortoise/MANIFEST.in
@@ -0,0 +1,2 @@
+recursive-include tortoise/data *
+recursive-include tortoise/voices *
diff --git a/tortoise/README.md b/tortoise/README.md
new file mode 100644
index 0000000..6f82564
--- /dev/null
+++ b/tortoise/README.md
@@ -0,0 +1,234 @@
+# TorToiSe
+
+Tortoise is a text-to-speech program built with the following priorities:
+
+1. Strong multi-voice capabilities.
+2. Highly realistic prosody and intonation.
+   
+This repo contains all the code needed to run Tortoise TTS in inference mode.
+
+Manuscript: https://arxiv.org/abs/2305.07243
+## Hugging Face space
+
+A live demo is hosted on Hugging Face Spaces. If you'd like to avoid a queue, please duplicate the Space and add a GPU. Please note that CPU-only spaces do not work for this demo.
+
+https://huggingface.co/spaces/Manmay/tortoise-tts
+
+## Install via pip
+```bash
+pip install tortoise-tts
+```
+
+If you would like to install the latest development version, you can also install it directly from the git repository:
+
+```bash
+pip install git+https://github.com/neonbjb/tortoise-tts
+```
+
+## What's in a name?
+
+I'm naming my speech-related repos after Mojave desert flora and fauna. Tortoise is a bit tongue in cheek: this model
+is insanely slow. It leverages both an autoregressive decoder **and** a diffusion decoder; both known for their low
+sampling rates. On a K80, expect to generate a medium sized sentence every 2 minutes.
+
+well..... not so slow anymore now we can get a **0.25-0.3 RTF** on 4GB vram and with streaming we can get < **500 ms** latency !!! 
+
+## Demos
+
+See [this page](http://nonint.com/static/tortoise_v2_examples.html) for a large list of example outputs.
+
+A cool application of Tortoise + GPT-3 (not affiliated with this repository): https://twitter.com/lexman_ai. Unfortunately, this project seems no longer to be active.
+
+## Usage guide
+
+### Local installation
+
+If you want to use this on your own computer, you must have an NVIDIA GPU.
+
+On Windows, I **highly** recommend using the Conda installation method. I have been told that if you do not do this, you
+will spend a lot of time chasing dependency problems.
+
+First, install miniconda: https://docs.conda.io/en/latest/miniconda.html
+
+Then run the following commands, using anaconda prompt as the terminal (or any other terminal configured to work with conda)
+
+This will:
+1. create conda environment with minimal dependencies specified
+1. activate the environment
+1. install pytorch with the command provided here: https://pytorch.org/get-started/locally/
+1. clone tortoise-tts
+1. change the current directory to tortoise-tts
+1. run tortoise python setup install script
+
+```shell
+conda create --name tortoise python=3.9 numba inflect
+conda activate tortoise
+conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
+conda install transformers=4.29.2
+git clone https://github.com/neonbjb/tortoise-tts.git
+cd tortoise-tts
+python setup.py install
+```
+
+Optionally, pytorch can be installed in the base environment, so that other conda environments can use it too. To do this, simply send the `conda install pytorch...` line before activating the tortoise environment.
+
+> **Note:** When you want to use tortoise-tts, you will always have to ensure the `tortoise` conda environment is activated.
+
+If you are on windows, you may also need to install pysoundfile: `conda install -c conda-forge pysoundfile`
+
+### Docker
+
+An easy way to hit the ground running and a good jumping off point depending on your use case.
+
+```sh
+git clone https://github.com/neonbjb/tortoise-tts.git
+cd tortoise-tts
+
+docker build . -t tts
+
+docker run --gpus all \
+    -e TORTOISE_MODELS_DIR=/models \
+    -v /mnt/user/data/tortoise_tts/models:/models \
+    -v /mnt/user/data/tortoise_tts/results:/results \
+    -v /mnt/user/data/.cache/huggingface:/root/.cache/huggingface \
+    -v /root:/work \
+    -it tts
+```
+This gives you an interactive terminal in an environment that's ready to do some tts. Now you can explore the different interfaces that tortoise exposes for tts.
+
+For example:
+
+```sh
+cd app
+conda activate tortoise
+time python tortoise/do_tts.py \
+    --output_path /results \
+    --preset ultra_fast \
+    --voice geralt \
+    --text "Time flies like an arrow; fruit flies like a bananna."
+```
+
+## Apple Silicon
+
+On macOS 13+ with M1/M2 chips you need to install the nighly version of PyTorch, as stated in the official page you can do:
+
+```shell
+pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu
+```
+
+Be sure to do that after you activate the environment. If you don't use conda the commands would look like this:
+
+```shell
+python3.10 -m venv .venv
+source .venv/bin/activate
+pip install numba inflect psutil
+pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu
+pip install transformers
+git clone https://github.com/neonbjb/tortoise-tts.git
+cd tortoise-tts
+pip install .
+```
+
+Be aware that DeepSpeed is disabled on Apple Silicon since it does not work. The flag `--use_deepspeed` is ignored.
+You may need to prepend `PYTORCH_ENABLE_MPS_FALLBACK=1` to the commands below to make them work since MPS does not support all the operations in Pytorch.
+
+
+### do_tts.py
+
+This script allows you to speak a single phrase with one or more voices.
+```shell
+python tortoise/do_tts.py --text "I'm going to speak this" --voice random --preset fast
+```
+### do socket streaming
+```socket server
+python tortoise/socket_server.py 
+```
+will listen at port 5000
+
+
+### faster inference read.py
+
+This script provides tools for reading large amounts of text.
+
+```shell
+python tortoise/read_fast.py --textfile <your text to be read> --voice random
+```
+
+### read.py
+
+This script provides tools for reading large amounts of text.
+
+```shell
+python tortoise/read.py --textfile <your text to be read> --voice random
+```
+
+This will break up the textfile into sentences, and then convert them to speech one at a time. It will output a series
+of spoken clips as they are generated. Once all the clips are generated, it will combine them into a single file and
+output that as well.
+
+Sometimes Tortoise screws up an output. You can re-generate any bad clips by re-running `read.py` with the --regenerate
+argument.
+
+### API
+
+Tortoise can be used programmatically, like so:
+
+```python
+reference_clips = [utils.audio.load_audio(p, 22050) for p in clips_paths]
+tts = api.TextToSpeech()
+pcm_audio = tts.tts_with_preset("your text here", voice_samples=reference_clips, preset='fast')
+```
+
+To use deepspeed:
+
+```python
+reference_clips = [utils.audio.load_audio(p, 22050) for p in clips_paths]
+tts = api.TextToSpeech(use_deepspeed=True)
+pcm_audio = tts.tts_with_preset("your text here", voice_samples=reference_clips, preset='fast')
+```
+
+To use kv cache:
+
+```python
+reference_clips = [utils.audio.load_audio(p, 22050) for p in clips_paths]
+tts = api.TextToSpeech(kv_cache=True)
+pcm_audio = tts.tts_with_preset("your text here", voice_samples=reference_clips, preset='fast')
+```
+
+To run model in float16:
+
+```python
+reference_clips = [utils.audio.load_audio(p, 22050) for p in clips_paths]
+tts = api.TextToSpeech(half=True)
+pcm_audio = tts.tts_with_preset("your text here", voice_samples=reference_clips, preset='fast')
+```
+for Faster runs use all three:
+
+```python
+reference_clips = [utils.audio.load_audio(p, 22050) for p in clips_paths]
+tts = api.TextToSpeech(use_deepspeed=True, kv_cache=True, half=True)
+pcm_audio = tts.tts_with_preset("your text here", voice_samples=reference_clips, preset='fast')
+```
+
+## Acknowledgements
+
+This project has garnered more praise than I expected. I am standing on the shoulders of giants, though, and I want to
+credit a few of the amazing folks in the community that have helped make this happen:
+
+- Hugging Face, who wrote the GPT model and the generate API used by Tortoise, and who hosts the model weights.
+- [Ramesh et al](https://arxiv.org/pdf/2102.12092.pdf) who authored the DALLE paper, which is the inspiration behind Tortoise.
+- [Nichol and Dhariwal](https://arxiv.org/pdf/2102.09672.pdf) who authored the (revision of) the code that drives the diffusion model.
+- [Jang et al](https://arxiv.org/pdf/2106.07889.pdf) who developed and open-sourced univnet, the vocoder this repo uses.
+- [Kim and Jung](https://github.com/mindslab-ai/univnet) who implemented univnet pytorch model.
+- [lucidrains](https://github.com/lucidrains) who writes awesome open source pytorch models, many of which are used here.
+- [Patrick von Platen](https://huggingface.co/patrickvonplaten) whose guides on setting up wav2vec were invaluable to building my dataset.
+
+## Notice
+
+Tortoise was built entirely by the author (James Betker) using their own hardware. Their employer was not involved in any facet of Tortoise's development.
+
+## License
+
+Tortoise TTS is licensed under the Apache 2.0 license.
+
+If you use this repo or the ideas therein for your research, please cite it! A bibtex entree can be found in the right pane on GitHub.
diff --git a/tortoise/app.js b/tortoise/app.js
new file mode 100644
index 0000000..795a738
--- /dev/null
+++ b/tortoise/app.js
@@ -0,0 +1,42 @@
+const { spawn } = require('child_process');
+const path = require('path');
+
+// Python 스크립트의 경로
+const pythonScriptPath = path.join(__dirname, 'test.py');
+
+// 서버 IP와 포트 설정
+const serverIp = 'localhost';
+const serverPort = '5000';
+
+// 전송할 텍스트
+const textToConvert = "Hello, this is a test.";
+
+// Output 파일 이름은 Python 스크립트와 일치하도록 지정합니다.
+const characterName = "deniro";
+const outputFile = `output_${characterName}_${textToConvert.length}.wav`;
+
+// Python 프로세스를 실행합니다.
+const pythonProcess = spawn('python', [pythonScriptPath, textToConvert, serverIp, serverPort]);
+
+// Python 스크립트의 출력 및 오류를 처리합니다.
+pythonProcess.stdout.on('data', (data) => {
+    console.log(`stdout: ${data}`);
+});
+
+pythonProcess.stderr.on('data', (data) => {
+    console.error(`stderr: ${data}`);
+});
+
+pythonProcess.on('close', (code) => {
+    console.log(`Python script exited with code: ${code}`);
+    
+    // .wav 파일이 생성되었는지 확인합니다.
+    const fs = require('fs');
+    const outputPath = path.join(__dirname, outputFile);
+    if (fs.existsSync(outputPath)) {
+        console.log(`WAV file created successfully: ${outputPath}`);
+    } else {
+        console.error("WAV file was not created.");
+    }
+});
+
diff --git a/tortoise/audio_files/deniro.wav b/tortoise/audio_files/deniro.wav
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diff --git a/tortoise/audio_files/deniro_966ef369-f440-4d2c-a293-8e3347f5ad3c.wav b/tortoise/audio_files/deniro_966ef369-f440-4d2c-a293-8e3347f5ad3c.wav
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diff --git a/tortoise/audio_files/upload_file.py b/tortoise/audio_files/upload_file.py
new file mode 100644
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+++ b/tortoise/audio_files/upload_file.py
@@ -0,0 +1,36 @@
+import boto3
+from botocore.exceptions import NoCredentialsError
+
+def upload_to_s3(file_name, bucket, object_name=None):
+    """
+    파일을 지정한 S3 버킷에 업로드하는 함수
+
+    :param file_name: 로컬 파일 경로
+    :param bucket: S3 버킷 이름
+    :param object_name: S3에 저장할 파일 이름 (생략 시 로컬 파일 이름 사용)
+    :return: True if file was uploaded, else False
+    """
+    # object_name이 없을 경우, file_name을 object_name으로 설정
+    if object_name is None:
+        object_name = file_name
+
+    # S3 클라이언트 생성
+    s3_client = boto3.client('s3')
+
+    try:
+        # 파일 업로드
+        s3_client.upload_file(file_name, bucket, object_name)
+        print(f"파일 {file_name}을(를) {bucket}/{object_name}에 성공적으로 업로드했습니다.")
+        return True
+    except FileNotFoundError:
+        print(f"파일 {file_name}을(를) 찾을 수 없습니다.")
+        return False
+    except NoCredentialsError:
+        print("AWS 자격 증명을 찾을 수 없습니다.")
+        return False
+
+# 사용 예시
+file_name = 'deniro.wav'  # 업로드할 파일 경로
+bucket_name = 'dlgksruf'  # S3 버킷 이름
+upload_to_s3(file_name, bucket_name)
+
diff --git a/tortoise/client.py b/tortoise/client.py
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+++ b/tortoise/client.py
@@ -0,0 +1,56 @@
+import asyncio
+import websockets
+import soundfile as sf
+import numpy as np
+
+async def receive_and_save_audio(websocket, output_file):
+    buffer = b''
+    total_chunks_received = 0
+
+    try:
+        while True:
+            chunk = await websocket.recv()
+            total_chunks_received += 1
+
+            if isinstance(chunk, str) and chunk == "END_OF_AUDIO":
+                print("End of audio signal received.")
+                break
+
+            buffer += chunk
+
+        # 서버에서 받은 데이터를 numpy 배열로 변환하고 .wav 파일로 저장
+        with open(output_file, 'wb') as f:
+            f.write(buffer)
+
+        print(f"Audio saved to {output_file}")
+        print(f"Total chunks received: {total_chunks_received}")
+
+    except Exception as e:
+        print(f"Error receiving audio: {e}")
+
+async def send_text_to_server(character_name, text, output_file, server_ip='localhost', server_port=5000):
+    uri = f"ws://{server_ip}:{server_port}"
+    async with websockets.connect(uri) as websocket:
+        data = f"{character_name}|{text}"
+        print(f"Sending text to server: {data}")
+        await websocket.send(data)
+
+        print("Text sent successfully. Awaiting audio stream...")
+        await receive_and_save_audio(websocket, output_file)
+
+async def main(server_ip='localhost', server_port=5000):
+    character_name = "deniro"  # 기본 캐릭터 이름
+
+    while True:
+        text = input("Enter text to convert to speech (or 'quit' to exit): ")
+
+        if text.lower() == 'quit':
+            print("Exiting the client...")
+            break
+
+        output_file = "client_output.wav"  # 클라이언트 측에서 저장할 파일 이름
+        await send_text_to_server(character_name, text, output_file, server_ip, server_port)
+
+if __name__ == "__main__":
+    asyncio.run(main())
+
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diff --git a/tortoise/json_server.py b/tortoise/json_server.py
new file mode 100644
index 0000000..42b0455
--- /dev/null
+++ b/tortoise/json_server.py
@@ -0,0 +1,110 @@
+import asyncio
+import websockets
+import spacy
+import json
+import os
+import uuid
+import numpy as np
+from scipy.io import wavfile
+from tortoise.api_fast import TextToSpeech
+from tortoise.utils.audio import load_voices
+
+tts = TextToSpeech()
+nlp = spacy.load("en_core_web_sm")
+
+def generate_audio_stream(text, tts, voice_samples):
+    print(f"Generating audio stream...: {text}")
+    voice_samples, conditioning_latents = load_voices([voice_samples])
+    stream = tts.tts_stream(
+        text,
+        voice_samples=voice_samples,
+        conditioning_latents=conditioning_latents,
+        verbose=True,
+        stream_chunk_size=40
+    )
+    return stream
+
+def split_text(text, max_length=200):
+    doc = nlp(text)
+    chunks = []
+    chunk = []
+    length = 0
+    for sent in doc.sents:
+        sent_length = len(sent.text)
+        if length + sent_length > max_length:
+            chunks.append(' '.join(chunk))
+            chunk = []
+            length = 0
+        chunk.append(sent.text)
+        length += sent_length + 1
+    if chunk:
+        chunks.append(' '.join(chunk))
+    return chunks
+
+async def handle_client(websocket, path):
+    try:
+        async for data in websocket:
+            try:
+                data = json.loads(data)
+                sender_id = data.get('senderId')
+                message = data.get('message')
+                
+                if sender_id is not None:
+                    print(f"연결된 사용자: {sender_id}")
+                    print(f"메시지: {message}")
+
+                if '|' in message:
+                    character_name, text = message.split('|', 1)
+                else:
+                    character_name = "deniro"
+                    text = message
+
+                text_chunks = split_text(text, max_length=200)
+                print(f"사용자 {sender_id}의 텍스트 청크: {text_chunks}")
+                
+                all_audio_data = []
+                for chunk in text_chunks:
+                    audio_stream = generate_audio_stream(chunk, tts, character_name)
+                    chunk_audio_data = []
+                    
+                    for audio_chunk in audio_stream:
+                        # Convert PyTorch tensor to numpy array
+                        audio_data = audio_chunk.cpu().numpy().flatten()
+                        chunk_audio_data.append(audio_data)
+                        
+                        # Send chunk immediately for streaming
+                        await websocket.send(audio_data.tobytes())
+                    
+                    # Combine chunk audio data
+                    if chunk_audio_data:
+                        combined_chunk = np.concatenate(chunk_audio_data)
+                        all_audio_data.append(combined_chunk)
+
+                # Save complete audio file
+                if all_audio_data:
+                    complete_audio = np.concatenate(all_audio_data)
+                    os.makedirs("audio_files", exist_ok=True)
+                    file_name = f"{character_name}_{uuid.uuid4()}.wav"
+                    file_path = os.path.join("audio_files", file_name)
+                    wavfile.write(file_path, 24000, complete_audio)
+                    print(f"Audio saved to: {file_path}")
+
+                await websocket.send("END_OF_AUDIO")
+                
+            except json.JSONDecodeError:
+                print("잘못된 JSON 형식입니다.")
+            except KeyError as e:
+                print(f"필수 키가 누락되었습니다: {e}")
+            except Exception as e:
+                print(f"오류 발생: {str(e)}")
+                
+    finally:
+        print(f"사용자 {sender_id} 연결 해제.")
+
+async def start_server():
+    server = await websockets.serve(handle_client, "0.0.0.0", 5000)
+    print("서버가 5000번 포트에서 리스닝 중입니다.")
+    await server.wait_closed()
+
+if __name__ == "__main__":
+    asyncio.run(start_server())
diff --git a/tortoise/main.py b/tortoise/main.py
new file mode 100644
index 0000000..8a99c5f
--- /dev/null
+++ b/tortoise/main.py
@@ -0,0 +1,206 @@
+import asyncio
+import json
+import os
+import uuid
+import numpy as np
+from scipy.io import wavfile
+from fastapi import FastAPI, UploadFile, File, WebSocket, Form
+import uvicorn
+from fastapi.middleware.cors import CORSMiddleware
+from tortoise.api_fast import TextToSpeech
+from tortoise.utils.audio import load_voices
+import spacy
+
+app = FastAPI()
+
+# CORS 설정
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # 실제 운영환경에서는 구체적인 도메인을 지정하세요
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# 전역 변수 초기화
+tts = TextToSpeech()
+nlp = spacy.load("en_core_web_sm")
+
+def generate_audio_stream(text, tts, voice_samples):
+    print(f"Generating audio stream...: {text}")
+    voice_samples, conditioning_latents = load_voices([voice_samples])
+    stream = tts.tts_stream(
+        text,
+        voice_samples=voice_samples,
+        conditioning_latents=conditioning_latents,
+        verbose=True,
+        stream_chunk_size=40
+    )
+    return stream
+
+def split_text(text, max_length=200):
+    doc = nlp(text)
+    chunks = []
+    chunk = []
+    length = 0
+    for sent in doc.sents:
+        sent_length = len(sent.text)
+        if length + sent_length > max_length:
+            chunks.append(' '.join(chunk))
+            chunk = []
+            length = 0
+        chunk.append(sent.text)
+        length += sent_length + 1
+    if chunk:
+        chunks.append(' '.join(chunk))
+    return chunks
+
+@app.post("/receive_audio_file")
+async def receive_audio_file(
+        senderId: str = Form(...),
+        file: UploadFile = File(...)
+        ):
+    try:
+        sender_id = senderId
+        upload_dir = os.path.join("/home/ubuntu/ai-server/tortoise/tortoise/voices",sender_id)
+        os.makedirs(upload_dir, exist_ok=True)
+
+        file_extension = os.path.splitext(file.filename)[1]
+        unique_filename = f"{sender_id}_{uuid.uuid4()}{file_extension}"
+        file_path = os.path.join(upload_dir, unique_filename)
+
+        with open(file_path, "wb") as buffer:
+            content = await file.read()
+            buffer.write(content)
+
+        return {
+            "status": "success",
+            "message": "File uploaded successfully",
+            "senderId" : sender_id,
+            "filename": unique_filename,
+            "file_path": file_path
+        }
+    except Exception as e:
+        return {
+            "status": "error",
+            "senderId" : sender_id,
+            "message": f"An error occurred: {str(e)}"
+        }
+
+# WebSocket 연결 관리를 위한 ConnectionManager 클래스
+class ConnectionManager:
+    def __init__(self):
+        self.active_connections: dict = {}
+
+    async def connect(self, websocket: WebSocket):
+        await websocket.accept()
+        return websocket
+
+    def store_connection(self, sender_id: str, websocket: WebSocket):
+        self.active_connections[sender_id] = websocket
+        print(f"\n=== WebSocket Connected ===")
+        print(f"Sender ID: {sender_id}")
+        print("=========================\n")
+
+    def disconnect(self, sender_id: str):
+        if sender_id in self.active_connections:
+            del self.active_connections[sender_id]
+            print(f"\n=== WebSocket Disconnected ===")
+            print(f"Sender ID: {sender_id}")
+            print("============================\n")
+
+    async def send_audio(self, sender_id: str, audio_data: bytes):
+        if sender_id in self.active_connections:
+            await self.active_connections[sender_id].send_bytes(audio_data)
+
+    async def send_message(self, sender_id: str, message: str):
+        if sender_id in self.active_connections:
+            await self.active_connections[sender_id].send_text(message)
+
+manager = ConnectionManager()
+
+def check_and_set_character_name(sender_id,voice_type):
+    voices_dir = os.path.join("tortoise/voices", sender_id)
+
+    if os.path.isdir(voices_dir):
+        character_name = sender_id
+    elif voice_type == 'male':
+        character_name = "deniro"
+    elif voice_type == 'female':
+        character_name = "emma"
+    else:
+        character_name = voice_type
+    # tortoise/voices 경로에 ROSE, GD, BRUNO,GRANDE 목소리 샘플 3가지 추가 후 voice_type으로 변경
+    return character_name
+
+# WebSocket 엔드포인트
+@app.websocket("/ws")
+async def websocket_endpoint(websocket: WebSocket):
+    await manager.connect(websocket)
+    try:
+        while True:
+            data = await websocket.receive_text()
+            try:
+                data = json.loads(data)
+
+                # JSON 데이터에서 필요한 정보 추출
+                chat_room_id = data.get('chatRoomId')
+                sender_id = data.get('senderId')
+                voice_type = data.get('voiceType').lower()
+                message = data.get('message')
+                
+                # 필수 필드 확인
+                if not all([chat_room_id, sender_id, voice_type, message]):
+                    raise ValueError("Missing required fields (chatRoomId, senderId, voice_type or message)")
+
+                # 첫 메시지인 경우 연결 저장
+                if sender_id not in manager.active_connections:
+                    manager.store_connection(sender_id, websocket)
+
+                # 텍스트 수신 로그
+                print(f"\n=== Text Message Received ===")
+                print(f"Sender ID: {sender_id}")
+                print(f"chat_room_id: {chat_room_id}")
+                print(f"Message: {message}")
+                print(f"voice_type: {voice_type}")
+                print("===========================\n")
+                
+                if '|' in message:
+                    character_name, text = message.split('|', 1)
+                else:
+                    # voiceType : mine 인 경우 사용자 본인 목소리 가중치 사용
+                    # 사용자가 본인 목소리 서버에 전송하지 않은 이상 mine이 AI 서버에 전송될 일 없음
+                    # 추후 에러 처리 필요
+                    character_name = check_and_set_character_name(sender_id, voice_type)
+                    text = message
+                    
+                text_chunks = split_text(text, max_length=200)
+                print(f"사용자 {sender_id}의 텍스트 청크: {text_chunks}")
+                
+                all_audio_data = []
+                for chunk in text_chunks:
+                    audio_stream = generate_audio_stream(chunk, tts, character_name)
+                    chunk_audio_data = []
+                    for audio_chunk in audio_stream:
+                        audio_data = audio_chunk.cpu().numpy().flatten()
+                        chunk_audio_data.append(audio_data)
+                        await manager.send_audio(sender_id, audio_data.tobytes())
+                        
+                    if chunk_audio_data:
+                        combined_chunk = np.concatenate(chunk_audio_data)
+                        all_audio_data.append(combined_chunk)
+                    
+                await manager.send_message(sender_id, "END_OF_AUDIO")
+                
+            except json.JSONDecodeError:
+                print("잘못된 JSON 형식입니다.")
+            except Exception as e:
+                print(f"오류 발생: {str(e)}")
+    except Exception as e:
+        print(f"WebSocket 오류: {str(e)}")
+    finally:
+        if sender_id:
+            manager.disconnect(sender_id)
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=8000)
diff --git a/tortoise/node_modules/.package-lock.json b/tortoise/node_modules/.package-lock.json
new file mode 100644
index 0000000..ed8ee18
--- /dev/null
+++ b/tortoise/node_modules/.package-lock.json
@@ -0,0 +1,106 @@
+{
+  "name": "tortoise",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "node_modules/asynckit": {
+      "version": "0.4.0",
+      "resolved": "https://registry.npmjs.org/asynckit/-/asynckit-0.4.0.tgz",
+      "integrity": "sha512-Oei9OH4tRh0YqU3GxhX79dM/mwVgvbZJaSNaRk+bshkj0S5cfHcgYakreBjrHwatXKbz+IoIdYLxrKim2MjW0Q==",
+      "license": "MIT"
+    },
+    "node_modules/axios": {
+      "version": "1.7.7",
+      "resolved": "https://registry.npmjs.org/axios/-/axios-1.7.7.tgz",
+      "integrity": "sha512-S4kL7XrjgBmvdGut0sN3yJxqYzrDOnivkBiN0OFs6hLiUam3UPvswUo0kqGyhqUZGEOytHyumEdXsAkgCOUf3Q==",
+      "license": "MIT",
+      "dependencies": {
+        "follow-redirects": "^1.15.6",
+        "form-data": "^4.0.0",
+        "proxy-from-env": "^1.1.0"
+      }
+    },
+    "node_modules/combined-stream": {
+      "version": "1.0.8",
+      "resolved": "https://registry.npmjs.org/combined-stream/-/combined-stream-1.0.8.tgz",
+      "integrity": "sha512-FQN4MRfuJeHf7cBbBMJFXhKSDq+2kAArBlmRBvcvFE5BB1HZKXtSFASDhdlz9zOYwxh8lDdnvmMOe/+5cdoEdg==",
+      "license": "MIT",
+      "dependencies": {
+        "delayed-stream": "~1.0.0"
+      },
+      "engines": {
+        "node": ">= 0.8"
+      }
+    },
+    "node_modules/delayed-stream": {
+      "version": "1.0.0",
+      "resolved": "https://registry.npmjs.org/delayed-stream/-/delayed-stream-1.0.0.tgz",
+      "integrity": "sha512-ZySD7Nf91aLB0RxL4KGrKHBXl7Eds1DAmEdcoVawXnLD7SDhpNgtuII2aAkg7a7QS41jxPSZ17p4VdGnMHk3MQ==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=0.4.0"
+      }
+    },
+    "node_modules/follow-redirects": {
+      "version": "1.15.9",
+      "resolved": "https://registry.npmjs.org/follow-redirects/-/follow-redirects-1.15.9.tgz",
+      "integrity": "sha512-gew4GsXizNgdoRyqmyfMHyAmXsZDk6mHkSxZFCzW9gwlbtOW44CDtYavM+y+72qD/Vq2l550kMF52DT8fOLJqQ==",
+      "funding": [
+        {
+          "type": "individual",
+          "url": "https://github.com/sponsors/RubenVerborgh"
+        }
+      ],
+      "license": "MIT",
+      "engines": {
+        "node": ">=4.0"
+      },
+      "peerDependenciesMeta": {
+        "debug": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/form-data": {
+      "version": "4.0.1",
+      "resolved": "https://registry.npmjs.org/form-data/-/form-data-4.0.1.tgz",
+      "integrity": "sha512-tzN8e4TX8+kkxGPK8D5u0FNmjPUjw3lwC9lSLxxoB/+GtsJG91CO8bSWy73APlgAZzZbXEYZJuxjkHH2w+Ezhw==",
+      "license": "MIT",
+      "dependencies": {
+        "asynckit": "^0.4.0",
+        "combined-stream": "^1.0.8",
+        "mime-types": "^2.1.12"
+      },
+      "engines": {
+        "node": ">= 6"
+      }
+    },
+    "node_modules/mime-db": {
+      "version": "1.52.0",
+      "resolved": "https://registry.npmjs.org/mime-db/-/mime-db-1.52.0.tgz",
+      "integrity": "sha512-sPU4uV7dYlvtWJxwwxHD0PuihVNiE7TyAbQ5SWxDCB9mUYvOgroQOwYQQOKPJ8CIbE+1ETVlOoK1UC2nU3gYvg==",
+      "license": "MIT",
+      "engines": {
+        "node": ">= 0.6"
+      }
+    },
+    "node_modules/mime-types": {
+      "version": "2.1.35",
+      "resolved": "https://registry.npmjs.org/mime-types/-/mime-types-2.1.35.tgz",
+      "integrity": "sha512-ZDY+bPm5zTTF+YpCrAU9nK0UgICYPT0QtT1NZWFv4s++TNkcgVaT0g6+4R2uI4MjQjzysHB1zxuWL50hzaeXiw==",
+      "license": "MIT",
+      "dependencies": {
+        "mime-db": "1.52.0"
+      },
+      "engines": {
+        "node": ">= 0.6"
+      }
+    },
+    "node_modules/proxy-from-env": {
+      "version": "1.1.0",
+      "resolved": "https://registry.npmjs.org/proxy-from-env/-/proxy-from-env-1.1.0.tgz",
+      "integrity": "sha512-D+zkORCbA9f1tdWRK0RaCR3GPv50cMxcrz4X8k5LTSUD1Dkw47mKJEZQNunItRTkWwgtaUSo1RVFRIG9ZXiFYg==",
+      "license": "MIT"
+    }
+  }
+}
diff --git a/tortoise/node_modules/asynckit/LICENSE b/tortoise/node_modules/asynckit/LICENSE
new file mode 100644
index 0000000..c9eca5d
--- /dev/null
+++ b/tortoise/node_modules/asynckit/LICENSE
@@ -0,0 +1,21 @@
+The MIT License (MIT)
+
+Copyright (c) 2016 Alex Indigo
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/tortoise/node_modules/asynckit/README.md b/tortoise/node_modules/asynckit/README.md
new file mode 100644
index 0000000..ddcc7e6
--- /dev/null
+++ b/tortoise/node_modules/asynckit/README.md
@@ -0,0 +1,233 @@
+# asynckit [![NPM Module](https://img.shields.io/npm/v/asynckit.svg?style=flat)](https://www.npmjs.com/package/asynckit)
+
+Minimal async jobs utility library, with streams support.
+
+[![PhantomJS Build](https://img.shields.io/travis/alexindigo/asynckit/v0.4.0.svg?label=browser&style=flat)](https://travis-ci.org/alexindigo/asynckit)
+[![Linux Build](https://img.shields.io/travis/alexindigo/asynckit/v0.4.0.svg?label=linux:0.12-6.x&style=flat)](https://travis-ci.org/alexindigo/asynckit)
+[![Windows Build](https://img.shields.io/appveyor/ci/alexindigo/asynckit/v0.4.0.svg?label=windows:0.12-6.x&style=flat)](https://ci.appveyor.com/project/alexindigo/asynckit)
+
+[![Coverage Status](https://img.shields.io/coveralls/alexindigo/asynckit/v0.4.0.svg?label=code+coverage&style=flat)](https://coveralls.io/github/alexindigo/asynckit?branch=master)
+[![Dependency Status](https://img.shields.io/david/alexindigo/asynckit/v0.4.0.svg?style=flat)](https://david-dm.org/alexindigo/asynckit)
+[![bitHound Overall Score](https://www.bithound.io/github/alexindigo/asynckit/badges/score.svg)](https://www.bithound.io/github/alexindigo/asynckit)
+
+<!-- [![Readme](https://img.shields.io/badge/readme-tested-brightgreen.svg?style=flat)](https://www.npmjs.com/package/reamde) -->
+
+AsyncKit provides harness for `parallel` and `serial` iterators over list of items represented by arrays or objects.
+Optionally it accepts abort function (should be synchronously return by iterator for each item), and terminates left over jobs upon an error event. For specific iteration order built-in (`ascending` and `descending`) and custom sort helpers also supported, via `asynckit.serialOrdered` method.
+
+It ensures async operations to keep behavior more stable and prevent `Maximum call stack size exceeded` errors, from sync iterators.
+
+| compression        |     size |
+| :----------------- | -------: |
+| asynckit.js        | 12.34 kB |
+| asynckit.min.js    |  4.11 kB |
+| asynckit.min.js.gz |  1.47 kB |
+
+
+## Install
+
+```sh
+$ npm install --save asynckit
+```
+
+## Examples
+
+### Parallel Jobs
+
+Runs iterator over provided array in parallel. Stores output in the `result` array,
+on the matching positions. In unlikely event of an error from one of the jobs,
+will terminate rest of the active jobs (if abort function is provided)
+and return error along with salvaged data to the main callback function.
+
+#### Input Array
+
+```javascript
+var parallel = require('asynckit').parallel
+  , assert   = require('assert')
+  ;
+
+var source         = [ 1, 1, 4, 16, 64, 32, 8, 2 ]
+  , expectedResult = [ 2, 2, 8, 32, 128, 64, 16, 4 ]
+  , expectedTarget = [ 1, 1, 2, 4, 8, 16, 32, 64 ]
+  , target         = []
+  ;
+
+parallel(source, asyncJob, function(err, result)
+{
+  assert.deepEqual(result, expectedResult);
+  assert.deepEqual(target, expectedTarget);
+});
+
+// async job accepts one element from the array
+// and a callback function
+function asyncJob(item, cb)
+{
+  // different delays (in ms) per item
+  var delay = item * 25;
+
+  // pretend different jobs take different time to finish
+  // and not in consequential order
+  var timeoutId = setTimeout(function() {
+    target.push(item);
+    cb(null, item * 2);
+  }, delay);
+
+  // allow to cancel "leftover" jobs upon error
+  // return function, invoking of which will abort this job
+  return clearTimeout.bind(null, timeoutId);
+}
+```
+
+More examples could be found in [test/test-parallel-array.js](test/test-parallel-array.js).
+
+#### Input Object
+
+Also it supports named jobs, listed via object.
+
+```javascript
+var parallel = require('asynckit/parallel')
+  , assert   = require('assert')
+  ;
+
+var source         = { first: 1, one: 1, four: 4, sixteen: 16, sixtyFour: 64, thirtyTwo: 32, eight: 8, two: 2 }
+  , expectedResult = { first: 2, one: 2, four: 8, sixteen: 32, sixtyFour: 128, thirtyTwo: 64, eight: 16, two: 4 }
+  , expectedTarget = [ 1, 1, 2, 4, 8, 16, 32, 64 ]
+  , expectedKeys   = [ 'first', 'one', 'two', 'four', 'eight', 'sixteen', 'thirtyTwo', 'sixtyFour' ]
+  , target         = []
+  , keys           = []
+  ;
+
+parallel(source, asyncJob, function(err, result)
+{
+  assert.deepEqual(result, expectedResult);
+  assert.deepEqual(target, expectedTarget);
+  assert.deepEqual(keys, expectedKeys);
+});
+
+// supports full value, key, callback (shortcut) interface
+function asyncJob(item, key, cb)
+{
+  // different delays (in ms) per item
+  var delay = item * 25;
+
+  // pretend different jobs take different time to finish
+  // and not in consequential order
+  var timeoutId = setTimeout(function() {
+    keys.push(key);
+    target.push(item);
+    cb(null, item * 2);
+  }, delay);
+
+  // allow to cancel "leftover" jobs upon error
+  // return function, invoking of which will abort this job
+  return clearTimeout.bind(null, timeoutId);
+}
+```
+
+More examples could be found in [test/test-parallel-object.js](test/test-parallel-object.js).
+
+### Serial Jobs
+
+Runs iterator over provided array sequentially. Stores output in the `result` array,
+on the matching positions. In unlikely event of an error from one of the jobs,
+will not proceed to the rest of the items in the list
+and return error along with salvaged data to the main callback function.
+
+#### Input Array
+
+```javascript
+var serial = require('asynckit/serial')
+  , assert = require('assert')
+  ;
+
+var source         = [ 1, 1, 4, 16, 64, 32, 8, 2 ]
+  , expectedResult = [ 2, 2, 8, 32, 128, 64, 16, 4 ]
+  , expectedTarget = [ 0, 1, 2, 3, 4, 5, 6, 7 ]
+  , target         = []
+  ;
+
+serial(source, asyncJob, function(err, result)
+{
+  assert.deepEqual(result, expectedResult);
+  assert.deepEqual(target, expectedTarget);
+});
+
+// extended interface (item, key, callback)
+// also supported for arrays
+function asyncJob(item, key, cb)
+{
+  target.push(key);
+
+  // it will be automatically made async
+  // even it iterator "returns" in the same event loop
+  cb(null, item * 2);
+}
+```
+
+More examples could be found in [test/test-serial-array.js](test/test-serial-array.js).
+
+#### Input Object
+
+Also it supports named jobs, listed via object.
+
+```javascript
+var serial = require('asynckit').serial
+  , assert = require('assert')
+  ;
+
+var source         = [ 1, 1, 4, 16, 64, 32, 8, 2 ]
+  , expectedResult = [ 2, 2, 8, 32, 128, 64, 16, 4 ]
+  , expectedTarget = [ 0, 1, 2, 3, 4, 5, 6, 7 ]
+  , target         = []
+  ;
+
+var source         = { first: 1, one: 1, four: 4, sixteen: 16, sixtyFour: 64, thirtyTwo: 32, eight: 8, two: 2 }
+  , expectedResult = { first: 2, one: 2, four: 8, sixteen: 32, sixtyFour: 128, thirtyTwo: 64, eight: 16, two: 4 }
+  , expectedTarget = [ 1, 1, 4, 16, 64, 32, 8, 2 ]
+  , target         = []
+  ;
+
+
+serial(source, asyncJob, function(err, result)
+{
+  assert.deepEqual(result, expectedResult);
+  assert.deepEqual(target, expectedTarget);
+});
+
+// shortcut interface (item, callback)
+// works for object as well as for the arrays
+function asyncJob(item, cb)
+{
+  target.push(item);
+
+  // it will be automatically made async
+  // even it iterator "returns" in the same event loop
+  cb(null, item * 2);
+}
+```
+
+More examples could be found in [test/test-serial-object.js](test/test-serial-object.js).
+
+_Note: Since _object_ is an _unordered_ collection of properties,
+it may produce unexpected results with sequential iterations.
+Whenever order of the jobs' execution is important please use `serialOrdered` method._
+
+### Ordered Serial Iterations
+
+TBD
+
+For example [compare-property](compare-property) package.
+
+### Streaming interface
+
+TBD
+
+## Want to Know More?
+
+More examples can be found in [test folder](test/).
+
+Or open an [issue](https://github.com/alexindigo/asynckit/issues) with questions and/or suggestions.
+
+## License
+
+AsyncKit is licensed under the MIT license.
diff --git a/tortoise/node_modules/asynckit/bench.js b/tortoise/node_modules/asynckit/bench.js
new file mode 100644
index 0000000..c612f1a
--- /dev/null
+++ b/tortoise/node_modules/asynckit/bench.js
@@ -0,0 +1,76 @@
+/* eslint no-console: "off" */
+
+var asynckit = require('./')
+  , async    = require('async')
+  , assert   = require('assert')
+  , expected = 0
+  ;
+
+var Benchmark = require('benchmark');
+var suite = new Benchmark.Suite;
+
+var source = [];
+for (var z = 1; z < 100; z++)
+{
+  source.push(z);
+  expected += z;
+}
+
+suite
+// add tests
+
+.add('async.map', function(deferred)
+{
+  var total = 0;
+
+  async.map(source,
+  function(i, cb)
+  {
+    setImmediate(function()
+    {
+      total += i;
+      cb(null, total);
+    });
+  },
+  function(err, result)
+  {
+    assert.ifError(err);
+    assert.equal(result[result.length - 1], expected);
+    deferred.resolve();
+  });
+}, {'defer': true})
+
+
+.add('asynckit.parallel', function(deferred)
+{
+  var total = 0;
+
+  asynckit.parallel(source,
+  function(i, cb)
+  {
+    setImmediate(function()
+    {
+      total += i;
+      cb(null, total);
+    });
+  },
+  function(err, result)
+  {
+    assert.ifError(err);
+    assert.equal(result[result.length - 1], expected);
+    deferred.resolve();
+  });
+}, {'defer': true})
+
+
+// add listeners
+.on('cycle', function(ev)
+{
+  console.log(String(ev.target));
+})
+.on('complete', function()
+{
+  console.log('Fastest is ' + this.filter('fastest').map('name'));
+})
+// run async
+.run({ 'async': true });
diff --git a/tortoise/node_modules/asynckit/index.js b/tortoise/node_modules/asynckit/index.js
new file mode 100644
index 0000000..455f945
--- /dev/null
+++ b/tortoise/node_modules/asynckit/index.js
@@ -0,0 +1,6 @@
+module.exports =
+{
+  parallel      : require('./parallel.js'),
+  serial        : require('./serial.js'),
+  serialOrdered : require('./serialOrdered.js')
+};
diff --git a/tortoise/node_modules/asynckit/package.json b/tortoise/node_modules/asynckit/package.json
new file mode 100644
index 0000000..51147d6
--- /dev/null
+++ b/tortoise/node_modules/asynckit/package.json
@@ -0,0 +1,63 @@
+{
+  "name": "asynckit",
+  "version": "0.4.0",
+  "description": "Minimal async jobs utility library, with streams support",
+  "main": "index.js",
+  "scripts": {
+    "clean": "rimraf coverage",
+    "lint": "eslint *.js lib/*.js test/*.js",
+    "test": "istanbul cover --reporter=json tape -- 'test/test-*.js' | tap-spec",
+    "win-test": "tape test/test-*.js",
+    "browser": "browserify -t browserify-istanbul test/lib/browserify_adjustment.js test/test-*.js | obake --coverage | tap-spec",
+    "report": "istanbul report",
+    "size": "browserify index.js | size-table asynckit",
+    "debug": "tape test/test-*.js"
+  },
+  "pre-commit": [
+    "clean",
+    "lint",
+    "test",
+    "browser",
+    "report",
+    "size"
+  ],
+  "repository": {
+    "type": "git",
+    "url": "git+https://github.com/alexindigo/asynckit.git"
+  },
+  "keywords": [
+    "async",
+    "jobs",
+    "parallel",
+    "serial",
+    "iterator",
+    "array",
+    "object",
+    "stream",
+    "destroy",
+    "terminate",
+    "abort"
+  ],
+  "author": "Alex Indigo <iam@alexindigo.com>",
+  "license": "MIT",
+  "bugs": {
+    "url": "https://github.com/alexindigo/asynckit/issues"
+  },
+  "homepage": "https://github.com/alexindigo/asynckit#readme",
+  "devDependencies": {
+    "browserify": "^13.0.0",
+    "browserify-istanbul": "^2.0.0",
+    "coveralls": "^2.11.9",
+    "eslint": "^2.9.0",
+    "istanbul": "^0.4.3",
+    "obake": "^0.1.2",
+    "phantomjs-prebuilt": "^2.1.7",
+    "pre-commit": "^1.1.3",
+    "reamde": "^1.1.0",
+    "rimraf": "^2.5.2",
+    "size-table": "^0.2.0",
+    "tap-spec": "^4.1.1",
+    "tape": "^4.5.1"
+  },
+  "dependencies": {}
+}
diff --git a/tortoise/node_modules/asynckit/parallel.js b/tortoise/node_modules/asynckit/parallel.js
new file mode 100644
index 0000000..3c50344
--- /dev/null
+++ b/tortoise/node_modules/asynckit/parallel.js
@@ -0,0 +1,43 @@
+var iterate    = require('./lib/iterate.js')
+  , initState  = require('./lib/state.js')
+  , terminator = require('./lib/terminator.js')
+  ;
+
+// Public API
+module.exports = parallel;
+
+/**
+ * Runs iterator over provided array elements in parallel
+ *
+ * @param   {array|object} list - array or object (named list) to iterate over
+ * @param   {function} iterator - iterator to run
+ * @param   {function} callback - invoked when all elements processed
+ * @returns {function} - jobs terminator
+ */
+function parallel(list, iterator, callback)
+{
+  var state = initState(list);
+
+  while (state.index < (state['keyedList'] || list).length)
+  {
+    iterate(list, iterator, state, function(error, result)
+    {
+      if (error)
+      {
+        callback(error, result);
+        return;
+      }
+
+      // looks like it's the last one
+      if (Object.keys(state.jobs).length === 0)
+      {
+        callback(null, state.results);
+        return;
+      }
+    });
+
+    state.index++;
+  }
+
+  return terminator.bind(state, callback);
+}
diff --git a/tortoise/node_modules/asynckit/serial.js b/tortoise/node_modules/asynckit/serial.js
new file mode 100644
index 0000000..6cd949a
--- /dev/null
+++ b/tortoise/node_modules/asynckit/serial.js
@@ -0,0 +1,17 @@
+var serialOrdered = require('./serialOrdered.js');
+
+// Public API
+module.exports = serial;
+
+/**
+ * Runs iterator over provided array elements in series
+ *
+ * @param   {array|object} list - array or object (named list) to iterate over
+ * @param   {function} iterator - iterator to run
+ * @param   {function} callback - invoked when all elements processed
+ * @returns {function} - jobs terminator
+ */
+function serial(list, iterator, callback)
+{
+  return serialOrdered(list, iterator, null, callback);
+}
diff --git a/tortoise/node_modules/asynckit/serialOrdered.js b/tortoise/node_modules/asynckit/serialOrdered.js
new file mode 100644
index 0000000..607eafe
--- /dev/null
+++ b/tortoise/node_modules/asynckit/serialOrdered.js
@@ -0,0 +1,75 @@
+var iterate    = require('./lib/iterate.js')
+  , initState  = require('./lib/state.js')
+  , terminator = require('./lib/terminator.js')
+  ;
+
+// Public API
+module.exports = serialOrdered;
+// sorting helpers
+module.exports.ascending  = ascending;
+module.exports.descending = descending;
+
+/**
+ * Runs iterator over provided sorted array elements in series
+ *
+ * @param   {array|object} list - array or object (named list) to iterate over
+ * @param   {function} iterator - iterator to run
+ * @param   {function} sortMethod - custom sort function
+ * @param   {function} callback - invoked when all elements processed
+ * @returns {function} - jobs terminator
+ */
+function serialOrdered(list, iterator, sortMethod, callback)
+{
+  var state = initState(list, sortMethod);
+
+  iterate(list, iterator, state, function iteratorHandler(error, result)
+  {
+    if (error)
+    {
+      callback(error, result);
+      return;
+    }
+
+    state.index++;
+
+    // are we there yet?
+    if (state.index < (state['keyedList'] || list).length)
+    {
+      iterate(list, iterator, state, iteratorHandler);
+      return;
+    }
+
+    // done here
+    callback(null, state.results);
+  });
+
+  return terminator.bind(state, callback);
+}
+
+/*
+ * -- Sort methods
+ */
+
+/**
+ * sort helper to sort array elements in ascending order
+ *
+ * @param   {mixed} a - an item to compare
+ * @param   {mixed} b - an item to compare
+ * @returns {number} - comparison result
+ */
+function ascending(a, b)
+{
+  return a < b ? -1 : a > b ? 1 : 0;
+}
+
+/**
+ * sort helper to sort array elements in descending order
+ *
+ * @param   {mixed} a - an item to compare
+ * @param   {mixed} b - an item to compare
+ * @returns {number} - comparison result
+ */
+function descending(a, b)
+{
+  return -1 * ascending(a, b);
+}
diff --git a/tortoise/node_modules/asynckit/stream.js b/tortoise/node_modules/asynckit/stream.js
new file mode 100644
index 0000000..d43465f
--- /dev/null
+++ b/tortoise/node_modules/asynckit/stream.js
@@ -0,0 +1,21 @@
+var inherits              = require('util').inherits
+  , Readable              = require('stream').Readable
+  , ReadableAsyncKit      = require('./lib/readable_asynckit.js')
+  , ReadableParallel      = require('./lib/readable_parallel.js')
+  , ReadableSerial        = require('./lib/readable_serial.js')
+  , ReadableSerialOrdered = require('./lib/readable_serial_ordered.js')
+  ;
+
+// API
+module.exports =
+{
+  parallel      : ReadableParallel,
+  serial        : ReadableSerial,
+  serialOrdered : ReadableSerialOrdered, 
+};
+
+inherits(ReadableAsyncKit, Readable);
+
+inherits(ReadableParallel, ReadableAsyncKit);
+inherits(ReadableSerial, ReadableAsyncKit);
+inherits(ReadableSerialOrdered, ReadableAsyncKit);
diff --git a/tortoise/node_modules/axios/CHANGELOG.md b/tortoise/node_modules/axios/CHANGELOG.md
new file mode 100644
index 0000000..27abf97
--- /dev/null
+++ b/tortoise/node_modules/axios/CHANGELOG.md
@@ -0,0 +1,1023 @@
+# Changelog
+
+## [1.7.7](https://github.com/axios/axios/compare/v1.7.6...v1.7.7) (2024-08-31)
+
+
+### Bug Fixes
+
+* **fetch:** fix stream handling in Safari by fallback to using a stream reader instead of an async iterator; ([#6584](https://github.com/axios/axios/issues/6584)) ([d198085](https://github.com/axios/axios/commit/d1980854fee1765cd02fa0787adf5d6e34dd9dcf))
+* **http:** fixed support for IPv6 literal strings in url ([#5731](https://github.com/axios/axios/issues/5731)) ([364993f](https://github.com/axios/axios/commit/364993f0d8bc6e0e06f76b8a35d2d0a35cab054c))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/10539109?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Rishi556](https://github.com/Rishi556 "+39/-1 (#5731 )")
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+27/-7 (#6584 )")
+
+## [1.7.6](https://github.com/axios/axios/compare/v1.7.5...v1.7.6) (2024-08-30)
+
+
+### Bug Fixes
+
+* **fetch:** fix content length calculation for FormData payload; ([#6524](https://github.com/axios/axios/issues/6524)) ([085f568](https://github.com/axios/axios/commit/085f56861a83e9ac02c140ad9d68dac540dfeeaa))
+* **fetch:** optimize signals composing logic; ([#6582](https://github.com/axios/axios/issues/6582)) ([df9889b](https://github.com/axios/axios/commit/df9889b83c2cc37e9e6189675a73ab70c60f031f))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+98/-46 (#6582 )")
+- <img src="https://avatars.githubusercontent.com/u/3534453?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Jacques Germishuys](https://github.com/jacquesg "+5/-1 (#6524 )")
+- <img src="https://avatars.githubusercontent.com/u/53894505?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [kuroino721](https://github.com/kuroino721 "+3/-1 (#6575 )")
+
+## [1.7.5](https://github.com/axios/axios/compare/v1.7.4...v1.7.5) (2024-08-23)
+
+
+### Bug Fixes
+
+* **adapter:** fix undefined reference to hasBrowserEnv ([#6572](https://github.com/axios/axios/issues/6572)) ([7004707](https://github.com/axios/axios/commit/7004707c4180b416341863bd86913fe4fc2f1df1))
+* **core:** add the missed implementation of AxiosError#status property; ([#6573](https://github.com/axios/axios/issues/6573)) ([6700a8a](https://github.com/axios/axios/commit/6700a8adac06942205f6a7a21421ecb36c4e0852))
+* **core:** fix `ReferenceError: navigator is not defined` for custom environments; ([#6567](https://github.com/axios/axios/issues/6567)) ([fed1a4b](https://github.com/axios/axios/commit/fed1a4b2d78ed4a588c84e09d32749ed01dc2794))
+* **fetch:** fix credentials handling in Cloudflare workers ([#6533](https://github.com/axios/axios/issues/6533)) ([550d885](https://github.com/axios/axios/commit/550d885eb90fd156add7b93bbdc54d30d2f9a98d))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+187/-83 (#6573 #6567 #6566 #6564 #6563 #6557 #6556 #6555 #6554 #6552 )")
+- <img src="https://avatars.githubusercontent.com/u/2495809?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Antonin Bas](https://github.com/antoninbas "+6/-6 (#6572 )")
+- <img src="https://avatars.githubusercontent.com/u/5406212?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Hans Otto Wirtz](https://github.com/hansottowirtz "+4/-1 (#6533 )")
+
+## [1.7.4](https://github.com/axios/axios/compare/v1.7.3...v1.7.4) (2024-08-13)
+
+
+### Bug Fixes
+
+* **sec:** CVE-2024-39338 ([#6539](https://github.com/axios/axios/issues/6539)) ([#6543](https://github.com/axios/axios/issues/6543)) ([6b6b605](https://github.com/axios/axios/commit/6b6b605eaf73852fb2dae033f1e786155959de3a))
+* **sec:** disregard protocol-relative URL to remediate SSRF ([#6539](https://github.com/axios/axios/issues/6539)) ([07a661a](https://github.com/axios/axios/commit/07a661a2a6b9092c4aa640dcc7f724ec5e65bdda))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/31389480?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Lev Pachmanov](https://github.com/levpachmanov "+47/-11 (#6543 )")
+- <img src="https://avatars.githubusercontent.com/u/41283691?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Đỗ Trọng Hải](https://github.com/hainenber "+49/-4 (#6539 )")
+
+## [1.7.3](https://github.com/axios/axios/compare/v1.7.2...v1.7.3) (2024-08-01)
+
+
+### Bug Fixes
+
+* **adapter:** fix progress event emitting; ([#6518](https://github.com/axios/axios/issues/6518)) ([e3c76fc](https://github.com/axios/axios/commit/e3c76fc9bdd03aa4d98afaf211df943e2031453f))
+* **fetch:** fix withCredentials request config ([#6505](https://github.com/axios/axios/issues/6505)) ([85d4d0e](https://github.com/axios/axios/commit/85d4d0ea0aae91082f04e303dec46510d1b4e787))
+* **xhr:** return original config on errors from XHR adapter ([#6515](https://github.com/axios/axios/issues/6515)) ([8966ee7](https://github.com/axios/axios/commit/8966ee7ea62ecbd6cfb39a905939bcdab5cf6388))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+211/-159 (#6518 #6519 )")
+- <img src="https://avatars.githubusercontent.com/u/10867286?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Valerii Sidorenko](https://github.com/ValeraS "+3/-3 (#6515 )")
+- <img src="https://avatars.githubusercontent.com/u/8599535?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [prianYu](https://github.com/prianyu "+2/-2 (#6505 )")
+
+## [1.7.2](https://github.com/axios/axios/compare/v1.7.1...v1.7.2) (2024-05-21)
+
+
+### Bug Fixes
+
+* **fetch:** enhance fetch API detection; ([#6413](https://github.com/axios/axios/issues/6413)) ([4f79aef](https://github.com/axios/axios/commit/4f79aef81b7c4644328365bfc33acf0a9ef595bc))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+3/-3 (#6413 )")
+
+## [1.7.1](https://github.com/axios/axios/compare/v1.7.0...v1.7.1) (2024-05-20)
+
+
+### Bug Fixes
+
+* **fetch:** fixed ReferenceError issue when TextEncoder is not available in the environment; ([#6410](https://github.com/axios/axios/issues/6410)) ([733f15f](https://github.com/axios/axios/commit/733f15fe5bd2d67e1fadaee82e7913b70d45dc5e))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+14/-9 (#6410 )")
+
+# [1.7.0](https://github.com/axios/axios/compare/v1.7.0-beta.2...v1.7.0) (2024-05-19)
+
+
+### Features
+
+* **adapter:** add fetch adapter; ([#6371](https://github.com/axios/axios/issues/6371)) ([a3ff99b](https://github.com/axios/axios/commit/a3ff99b59d8ec2ab5dd049e68c043617a4072e42))
+
+### Bug Fixes
+
+* **core/axios:** handle un-writable error stack ([#6362](https://github.com/axios/axios/issues/6362)) ([81e0455](https://github.com/axios/axios/commit/81e0455b7b57fbaf2be16a73ebe0e6591cc6d8f9))  
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+1015/-127 (#6371 )")
+- <img src="https://avatars.githubusercontent.com/u/4814473?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Jay](https://github.com/jasonsaayman "+30/-14 ()")
+- <img src="https://avatars.githubusercontent.com/u/16711696?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Alexandre ABRIOUX](https://github.com/alexandre-abrioux "+56/-6 (#6362 )")
+
+# [1.7.0-beta.2](https://github.com/axios/axios/compare/v1.7.0-beta.1...v1.7.0-beta.2) (2024-05-19)
+
+
+### Bug Fixes
+
+* **fetch:** capitalize HTTP method names; ([#6395](https://github.com/axios/axios/issues/6395)) ([ad3174a](https://github.com/axios/axios/commit/ad3174a3515c3c2573f4bcb94818d582826f3914))
+* **fetch:** fix & optimize progress capturing for cases when the request data has a nullish value or zero data length ([#6400](https://github.com/axios/axios/issues/6400)) ([95a3e8e](https://github.com/axios/axios/commit/95a3e8e346cfd6a5548e171f2341df3235d0e26b))
+* **fetch:** fix headers getting from a stream response; ([#6401](https://github.com/axios/axios/issues/6401)) ([870e0a7](https://github.com/axios/axios/commit/870e0a76f60d0094774a6a63fa606eec52a381af))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+99/-46 (#6405 #6404 #6401 #6400 #6395 )")
+
+# [1.7.0-beta.1](https://github.com/axios/axios/compare/v1.7.0-beta.0...v1.7.0-beta.1) (2024-05-07)
+
+
+### Bug Fixes
+
+* **core/axios:** handle un-writable error stack ([#6362](https://github.com/axios/axios/issues/6362)) ([81e0455](https://github.com/axios/axios/commit/81e0455b7b57fbaf2be16a73ebe0e6591cc6d8f9))
+* **fetch:** fix cases when ReadableStream or Response.body are not available; ([#6377](https://github.com/axios/axios/issues/6377)) ([d1d359d](https://github.com/axios/axios/commit/d1d359da347704e8b28d768e61515a3e96c5b072))
+* **fetch:** treat fetch-related TypeError as an AxiosError.ERR_NETWORK error; ([#6380](https://github.com/axios/axios/issues/6380)) ([bb5f9a5](https://github.com/axios/axios/commit/bb5f9a5ab768452de9e166dc28d0ffc234245ef1))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/16711696?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Alexandre ABRIOUX](https://github.com/alexandre-abrioux "+56/-6 (#6362 )")
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+42/-17 (#6380 #6377 )")
+
+# [1.7.0-beta.0](https://github.com/axios/axios/compare/v1.6.8...v1.7.0-beta.0) (2024-04-28)
+
+
+### Features
+
+* **adapter:** add fetch adapter; ([#6371](https://github.com/axios/axios/issues/6371)) ([a3ff99b](https://github.com/axios/axios/commit/a3ff99b59d8ec2ab5dd049e68c043617a4072e42))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+1015/-127 (#6371 )")
+- <img src="https://avatars.githubusercontent.com/u/4814473?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Jay](https://github.com/jasonsaayman "+30/-14 ()")
+
+## [1.6.8](https://github.com/axios/axios/compare/v1.6.7...v1.6.8) (2024-03-15)
+
+
+### Bug Fixes
+
+* **AxiosHeaders:** fix AxiosHeaders conversion to an object during config merging ([#6243](https://github.com/axios/axios/issues/6243)) ([2656612](https://github.com/axios/axios/commit/2656612bc10fe2757e9832b708ed773ab340b5cb))
+* **import:** use named export for EventEmitter; ([7320430](https://github.com/axios/axios/commit/7320430aef2e1ba2b89488a0eaf42681165498b1))
+* **vulnerability:** update follow-redirects to 1.15.6 ([#6300](https://github.com/axios/axios/issues/6300)) ([8786e0f](https://github.com/axios/axios/commit/8786e0ff55a8c68d4ca989801ad26df924042e27))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/4814473?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Jay](https://github.com/jasonsaayman "+4572/-3446 (#6238 )")
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+30/-0 (#6231 )")
+- <img src="https://avatars.githubusercontent.com/u/68230846?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Mitchell](https://github.com/Creaous "+9/-9 (#6300 )")
+- <img src="https://avatars.githubusercontent.com/u/53797821?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Emmanuel](https://github.com/mannoeu "+2/-2 (#6196 )")
+- <img src="https://avatars.githubusercontent.com/u/44109284?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Lucas Keller](https://github.com/ljkeller "+3/-0 (#6194 )")
+- <img src="https://avatars.githubusercontent.com/u/72791488?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Aditya Mogili](https://github.com/ADITYA-176 "+1/-1 ()")
+- <img src="https://avatars.githubusercontent.com/u/46135319?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Miroslav Petrov](https://github.com/petrovmiroslav "+1/-1 (#6243 )")
+
+## [1.6.7](https://github.com/axios/axios/compare/v1.6.6...v1.6.7) (2024-01-25)
+
+
+### Bug Fixes
+
+* capture async stack only for rejections with native error objects; ([#6203](https://github.com/axios/axios/issues/6203)) ([1a08f90](https://github.com/axios/axios/commit/1a08f90f402336e4d00e9ee82f211c6adb1640b0))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+30/-26 (#6203 )")
+- <img src="https://avatars.githubusercontent.com/u/73059627?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [zhoulixiang](https://github.com/zh-lx "+0/-3 (#6186 )")
+
+## [1.6.6](https://github.com/axios/axios/compare/v1.6.5...v1.6.6) (2024-01-24)
+
+
+### Bug Fixes
+
+* fixed missed dispatchBeforeRedirect argument ([#5778](https://github.com/axios/axios/issues/5778)) ([a1938ff](https://github.com/axios/axios/commit/a1938ff073fcb0f89011f001dfbc1fa1dc995e39))
+* wrap errors to improve async stack trace ([#5987](https://github.com/axios/axios/issues/5987)) ([123f354](https://github.com/axios/axios/commit/123f354b920f154a209ea99f76b7b2ef3d9ebbab))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/1186084?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Ilya Priven](https://github.com/ikonst "+91/-8 (#5987 )")
+- <img src="https://avatars.githubusercontent.com/u/1884246?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Zao Soula](https://github.com/zaosoula "+6/-6 (#5778 )")
+
+## [1.6.5](https://github.com/axios/axios/compare/v1.6.4...v1.6.5) (2024-01-05)
+
+
+### Bug Fixes
+
+* **ci:** refactor notify action as a job of publish action; ([#6176](https://github.com/axios/axios/issues/6176)) ([0736f95](https://github.com/axios/axios/commit/0736f95ce8776366dc9ca569f49ba505feb6373c))
+* **dns:** fixed lookup error handling; ([#6175](https://github.com/axios/axios/issues/6175)) ([f4f2b03](https://github.com/axios/axios/commit/f4f2b039dd38eb4829e8583caede4ed6d2dd59be))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+41/-6 (#6176 #6175 )")
+- <img src="https://avatars.githubusercontent.com/u/4814473?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Jay](https://github.com/jasonsaayman "+6/-1 ()")
+
+## [1.6.4](https://github.com/axios/axios/compare/v1.6.3...v1.6.4) (2024-01-03)
+
+
+### Bug Fixes
+
+* **security:** fixed formToJSON prototype pollution vulnerability; ([#6167](https://github.com/axios/axios/issues/6167)) ([3c0c11c](https://github.com/axios/axios/commit/3c0c11cade045c4412c242b5727308cff9897a0e))
+* **security:** fixed security vulnerability in follow-redirects ([#6163](https://github.com/axios/axios/issues/6163)) ([75af1cd](https://github.com/axios/axios/commit/75af1cdff5b3a6ca3766d3d3afbc3115bb0811b8))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/4814473?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Jay](https://github.com/jasonsaayman "+34/-6 ()")
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+34/-3 (#6172 #6167 )")
+- <img src="https://avatars.githubusercontent.com/u/1402060?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Guy Nesher](https://github.com/gnesher "+10/-10 (#6163 )")
+
+## [1.6.3](https://github.com/axios/axios/compare/v1.6.2...v1.6.3) (2023-12-26)
+
+
+### Bug Fixes
+
+* Regular Expression Denial of Service (ReDoS) ([#6132](https://github.com/axios/axios/issues/6132)) ([5e7ad38](https://github.com/axios/axios/commit/5e7ad38fb0f819fceb19fb2ee5d5d38f56aa837d))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/4814473?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Jay](https://github.com/jasonsaayman "+15/-6 (#6145 )")
+- <img src="https://avatars.githubusercontent.com/u/22686401?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Willian Agostini](https://github.com/WillianAgostini "+17/-2 (#6132 )")
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+3/-0 (#6084 )")
+
+## [1.6.2](https://github.com/axios/axios/compare/v1.6.1...v1.6.2) (2023-11-14)
+
+
+### Features
+
+* **withXSRFToken:** added withXSRFToken option as a workaround to achieve the old `withCredentials` behavior; ([#6046](https://github.com/axios/axios/issues/6046)) ([cff9967](https://github.com/axios/axios/commit/cff996779b272a5e94c2b52f5503ccf668bc42dc))
+
+### PRs
+- feat(withXSRFToken): added withXSRFToken option as a workaround to achieve the old &#x60;withCredentials&#x60; behavior; ( [#6046](https://api.github.com/repos/axios/axios/pulls/6046) )
+```
+
+📢 This PR added &#x27;withXSRFToken&#x27; option as a replacement for old withCredentials behaviour. 
+You should now use withXSRFToken along with withCredential to get the old behavior.
+This functionality is considered as a fix.
+```
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+271/-146 (#6081 #6080 #6079 #6078 #6046 #6064 #6063 )")
+- <img src="https://avatars.githubusercontent.com/u/79681367?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Ng Choon Khon (CK)](https://github.com/ckng0221 "+4/-4 (#6073 )")
+- <img src="https://avatars.githubusercontent.com/u/9162827?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Muhammad Noman](https://github.com/mnomanmemon "+2/-2 (#6048 )")
+
+## [1.6.1](https://github.com/axios/axios/compare/v1.6.0...v1.6.1) (2023-11-08)
+
+
+### Bug Fixes
+
+* **formdata:** fixed content-type header normalization for non-standard browser environments; ([#6056](https://github.com/axios/axios/issues/6056)) ([dd465ab](https://github.com/axios/axios/commit/dd465ab22bbfa262c6567be6574bf46a057d5288))
+* **platform:** fixed emulated browser detection in node.js environment; ([#6055](https://github.com/axios/axios/issues/6055)) ([3dc8369](https://github.com/axios/axios/commit/3dc8369e505e32a4e12c22f154c55fd63ac67fbb))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+432/-65 (#6059 #6056 #6055 )")
+- <img src="https://avatars.githubusercontent.com/u/3982806?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Fabian Meyer](https://github.com/meyfa "+5/-2 (#5835 )")
+
+### PRs
+- feat(withXSRFToken): added withXSRFToken option as a workaround to achieve the old &#x60;withCredentials&#x60; behavior; ( [#6046](https://api.github.com/repos/axios/axios/pulls/6046) )
+```
+
+📢 This PR added &#x27;withXSRFToken&#x27; option as a replacement for old withCredentials behaviour. 
+You should now use withXSRFToken along with withCredential to get the old behavior.
+This functionality is considered as a fix.
+```
+
+# [1.6.0](https://github.com/axios/axios/compare/v1.5.1...v1.6.0) (2023-10-26)
+
+
+### Bug Fixes
+
+* **CSRF:** fixed CSRF vulnerability CVE-2023-45857 ([#6028](https://github.com/axios/axios/issues/6028)) ([96ee232](https://github.com/axios/axios/commit/96ee232bd3ee4de2e657333d4d2191cd389e14d0))
+* **dns:** fixed lookup function decorator to work properly in node v20; ([#6011](https://github.com/axios/axios/issues/6011)) ([5aaff53](https://github.com/axios/axios/commit/5aaff532a6b820bb9ab6a8cd0f77131b47e2adb8))
+* **types:** fix AxiosHeaders types; ([#5931](https://github.com/axios/axios/issues/5931)) ([a1c8ad0](https://github.com/axios/axios/commit/a1c8ad008b3c13d53e135bbd0862587fb9d3fc09))
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+449/-114 (#6032 #6021 #6011 #5932 #5931 )")
+- <img src="https://avatars.githubusercontent.com/u/63700910?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Valentin Panov](https://github.com/valentin-panov "+4/-4 (#6028 )")
+- <img src="https://avatars.githubusercontent.com/u/76877078?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Rinku Chaudhari](https://github.com/therealrinku "+1/-1 (#5889 )")
+
+## [1.5.1](https://github.com/axios/axios/compare/v1.5.0...v1.5.1) (2023-09-26)
+
+
+### Bug Fixes
+
+* **adapters:** improved adapters loading logic to have clear error messages; ([#5919](https://github.com/axios/axios/issues/5919)) ([e410779](https://github.com/axios/axios/commit/e4107797a7a1376f6209fbecfbbce73d3faa7859))
+* **formdata:** fixed automatic addition of the `Content-Type` header for FormData in non-browser environments; ([#5917](https://github.com/axios/axios/issues/5917)) ([bc9af51](https://github.com/axios/axios/commit/bc9af51b1886d1b3529617702f2a21a6c0ed5d92))
+* **headers:** allow `content-encoding` header to handle case-insensitive values ([#5890](https://github.com/axios/axios/issues/5890)) ([#5892](https://github.com/axios/axios/issues/5892)) ([4c89f25](https://github.com/axios/axios/commit/4c89f25196525e90a6e75eda9cb31ae0a2e18acd))
+* **types:** removed duplicated code ([9e62056](https://github.com/axios/axios/commit/9e6205630e1c9cf863adf141c0edb9e6d8d4b149))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+89/-18 (#5919 #5917 )")
+- <img src="https://avatars.githubusercontent.com/u/110460234?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [David Dallas](https://github.com/DavidJDallas "+11/-5 ()")
+- <img src="https://avatars.githubusercontent.com/u/71556073?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Sean Sattler](https://github.com/fb-sean "+2/-8 ()")
+- <img src="https://avatars.githubusercontent.com/u/4294069?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Mustafa Ateş Uzun](https://github.com/0o001 "+4/-4 ()")
+- <img src="https://avatars.githubusercontent.com/u/132928043?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Przemyslaw Motacki](https://github.com/sfc-gh-pmotacki "+2/-1 (#5892 )")
+- <img src="https://avatars.githubusercontent.com/u/5492927?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Michael Di Prisco](https://github.com/Cadienvan "+1/-1 ()")
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+# [1.5.0](https://github.com/axios/axios/compare/v1.4.0...v1.5.0) (2023-08-26)
+
+
+### Bug Fixes
+
+* **adapter:** make adapter loading error more clear by using platform-specific adapters explicitly ([#5837](https://github.com/axios/axios/issues/5837)) ([9a414bb](https://github.com/axios/axios/commit/9a414bb6c81796a95c6c7fe668637825458e8b6d))
+* **dns:** fixed `cacheable-lookup` integration; ([#5836](https://github.com/axios/axios/issues/5836)) ([b3e327d](https://github.com/axios/axios/commit/b3e327dcc9277bdce34c7ef57beedf644b00d628))
+* **headers:** added support for setting header names that overlap with class methods; ([#5831](https://github.com/axios/axios/issues/5831)) ([d8b4ca0](https://github.com/axios/axios/commit/d8b4ca0ea5f2f05efa4edfe1e7684593f9f68273))
+* **headers:** fixed common Content-Type header merging; ([#5832](https://github.com/axios/axios/issues/5832)) ([8fda276](https://github.com/axios/axios/commit/8fda2766b1e6bcb72c3fabc146223083ef13ce17))
+
+
+### Features
+
+* export getAdapter function ([#5324](https://github.com/axios/axios/issues/5324)) ([ca73eb8](https://github.com/axios/axios/commit/ca73eb878df0ae2dace81fe3a7f1fb5986231bf1))
+* **export:** export adapters without `unsafe` prefix ([#5839](https://github.com/axios/axios/issues/5839)) ([1601f4a](https://github.com/axios/axios/commit/1601f4a27a81ab47fea228f1e244b2c4e3ce28bf))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+66/-29 (#5839 #5837 #5836 #5832 #5831 )")
+- <img src="https://avatars.githubusercontent.com/u/102841186?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [夜葬](https://github.com/geekact "+42/-0 (#5324 )")
+- <img src="https://avatars.githubusercontent.com/u/65978976?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Jonathan Budiman](https://github.com/JBudiman00 "+30/-0 (#5788 )")
+- <img src="https://avatars.githubusercontent.com/u/5492927?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Michael Di Prisco](https://github.com/Cadienvan "+3/-5 (#5791 )")
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+# [1.4.0](https://github.com/axios/axios/compare/v1.3.6...v1.4.0) (2023-04-27)
+
+
+### Bug Fixes
+
+* **formdata:** add `multipart/form-data` content type for FormData payload on custom client environments; ([#5678](https://github.com/axios/axios/issues/5678)) ([bbb61e7](https://github.com/axios/axios/commit/bbb61e70cb1185adfb1cbbb86eaf6652c48d89d1))
+* **package:** export package internals with unsafe path prefix; ([#5677](https://github.com/axios/axios/issues/5677)) ([df38c94](https://github.com/axios/axios/commit/df38c949f26414d88ba29ec1e353c4d4f97eaf09))
+
+
+### Features
+
+* **dns:** added support for a custom lookup function; ([#5339](https://github.com/axios/axios/issues/5339)) ([2701911](https://github.com/axios/axios/commit/2701911260a1faa5cc5e1afe437121b330a3b7bb))
+* **types:** export `AxiosHeaderValue` type. ([#5525](https://github.com/axios/axios/issues/5525)) ([726f1c8](https://github.com/axios/axios/commit/726f1c8e00cffa0461a8813a9bdcb8f8b9d762cf))
+
+
+### Performance Improvements
+
+* **merge-config:** optimize mergeConfig performance by avoiding duplicate key visits; ([#5679](https://github.com/axios/axios/issues/5679)) ([e6f7053](https://github.com/axios/axios/commit/e6f7053bf1a3e87cf1f9da8677e12e3fe829d68e))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+151/-16 (#5684 #5339 #5679 #5678 #5677 )")
+- <img src="https://avatars.githubusercontent.com/u/47537704?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Arthur Fiorette](https://github.com/arthurfiorette "+19/-19 (#5525 )")
+- <img src="https://avatars.githubusercontent.com/u/43876655?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [PIYUSH NEGI](https://github.com/npiyush97 "+2/-18 (#5670 )")
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+## [1.3.6](https://github.com/axios/axios/compare/v1.3.5...v1.3.6) (2023-04-19)
+
+
+### Bug Fixes
+
+* **types:** added transport to RawAxiosRequestConfig ([#5445](https://github.com/axios/axios/issues/5445)) ([6f360a2](https://github.com/axios/axios/commit/6f360a2531d8d70363fd9becef6a45a323f170e2))
+* **utils:** make isFormData detection logic stricter to avoid unnecessary calling of the `toString` method on the target; ([#5661](https://github.com/axios/axios/issues/5661)) ([aa372f7](https://github.com/axios/axios/commit/aa372f7306295dfd1100c1c2c77ce95c95808e76))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+48/-10 (#5665 #5661 #5663 )")
+- <img src="https://avatars.githubusercontent.com/u/5492927?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Michael Di Prisco](https://github.com/Cadienvan "+2/-0 (#5445 )")
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+## [1.3.5](https://github.com/axios/axios/compare/v1.3.4...v1.3.5) (2023-04-05)
+
+
+### Bug Fixes
+
+* **headers:** fixed isValidHeaderName to support full list of allowed characters; ([#5584](https://github.com/axios/axios/issues/5584)) ([e7decef](https://github.com/axios/axios/commit/e7decef6a99f4627e27ed9ea5b00ce8e201c3841))
+* **params:** re-added the ability to set the function as `paramsSerializer` config; ([#5633](https://github.com/axios/axios/issues/5633)) ([a56c866](https://github.com/axios/axios/commit/a56c8661209d5ce5a645a05f294a0e08a6c1f6b3))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+28/-10 (#5633 #5584 )")
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+## [1.3.4](https://github.com/axios/axios/compare/v1.3.3...v1.3.4) (2023-02-22)
+
+
+### Bug Fixes
+
+* **blob:** added a check to make sure the Blob class is available in the browser's global scope; ([#5548](https://github.com/axios/axios/issues/5548)) ([3772c8f](https://github.com/axios/axios/commit/3772c8fe74112a56e3e9551f894d899bc3a9443a))
+* **http:** fixed regression bug when handling synchronous errors inside the adapter; ([#5564](https://github.com/axios/axios/issues/5564)) ([a3b246c](https://github.com/axios/axios/commit/a3b246c9de5c3bc4b5a742e15add55b375479451))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+38/-26 (#5564 )")
+- <img src="https://avatars.githubusercontent.com/u/19550000?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [lcysgsg](https://github.com/lcysgsg "+4/-0 (#5548 )")
+- <img src="https://avatars.githubusercontent.com/u/5492927?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Michael Di Prisco](https://github.com/Cadienvan "+3/-0 (#5444 )")
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+## [1.3.3](https://github.com/axios/axios/compare/v1.3.2...v1.3.3) (2023-02-13)
+
+
+### Bug Fixes
+
+* **formdata:** added a check to make sure the FormData class is available in the browser's global scope; ([#5545](https://github.com/axios/axios/issues/5545)) ([a6dfa72](https://github.com/axios/axios/commit/a6dfa72010db5ad52db8bd13c0f98e537e8fd05d))
+* **formdata:** fixed setting NaN as Content-Length for form payload in some cases; ([#5535](https://github.com/axios/axios/issues/5535)) ([c19f7bf](https://github.com/axios/axios/commit/c19f7bf770f90ae8307f4ea3104f227056912da1))
+* **headers:** fixed the filtering logic of the clear method; ([#5542](https://github.com/axios/axios/issues/5542)) ([ea87ebf](https://github.com/axios/axios/commit/ea87ebfe6d1699af072b9e7cd40faf8f14b0ab93))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+11/-7 (#5545 #5535 #5542 )")
+- <img src="https://avatars.githubusercontent.com/u/19842213?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [陈若枫](https://github.com/ruofee "+2/-2 (#5467 )")
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+## [1.3.2](https://github.com/axios/axios/compare/v1.3.1...v1.3.2) (2023-02-03)
+
+
+### Bug Fixes
+
+* **http:** treat http://localhost as base URL for relative paths to avoid `ERR_INVALID_URL` error; ([#5528](https://github.com/axios/axios/issues/5528)) ([128d56f](https://github.com/axios/axios/commit/128d56f4a0fb8f5f2ed6e0dd80bc9225fee9538c))
+* **http:** use explicit import instead of TextEncoder global; ([#5530](https://github.com/axios/axios/issues/5530)) ([6b3c305](https://github.com/axios/axios/commit/6b3c305fc40c56428e0afabedc6f4d29c2830f6f))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+2/-1 (#5530 #5528 )")
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+## [1.3.1](https://github.com/axios/axios/compare/v1.3.0...v1.3.1) (2023-02-01)
+
+
+### Bug Fixes
+
+* **formdata:** add hotfix to use the asynchronous API to compute the content-length header value; ([#5521](https://github.com/axios/axios/issues/5521)) ([96d336f](https://github.com/axios/axios/commit/96d336f527619f21da012fe1f117eeb53e5a2120))
+* **serializer:** fixed serialization of array-like objects; ([#5518](https://github.com/axios/axios/issues/5518)) ([08104c0](https://github.com/axios/axios/commit/08104c028c0f9353897b1b6691d74c440fd0c32d))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+27/-8 (#5521 #5518 )")
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+# [1.3.0](https://github.com/axios/axios/compare/v1.2.6...v1.3.0) (2023-01-31)
+
+
+### Bug Fixes
+
+* **headers:** fixed & optimized clear method; ([#5507](https://github.com/axios/axios/issues/5507)) ([9915635](https://github.com/axios/axios/commit/9915635c69d0ab70daca5738488421f67ca60959))
+* **http:** add zlib headers if missing ([#5497](https://github.com/axios/axios/issues/5497)) ([65e8d1e](https://github.com/axios/axios/commit/65e8d1e28ce829f47a837e45129730e541950d3c))
+
+
+### Features
+
+* **fomdata:** added support for spec-compliant FormData & Blob types; ([#5316](https://github.com/axios/axios/issues/5316)) ([6ac574e](https://github.com/axios/axios/commit/6ac574e00a06731288347acea1e8246091196953))
+
+### Contributors to this release
+
+- <img src="https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+352/-67 (#5514 #5512 #5510 #5509 #5508 #5316 #5507 )")
+- <img src="https://avatars.githubusercontent.com/u/35015993?v&#x3D;4&amp;s&#x3D;18" alt="avatar" width="18"/> [ItsNotGoodName](https://github.com/ItsNotGoodName "+43/-2 (#5497 )")
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+## [1.2.6](https://github.com/axios/axios/compare/v1.2.5...v1.2.6) (2023-01-28)
+
+
+### Bug Fixes
+
+* **headers:** added missed Authorization accessor; ([#5502](https://github.com/axios/axios/issues/5502)) ([342c0ba](https://github.com/axios/axios/commit/342c0ba9a16ea50f5ed7d2366c5c1a2c877e3f26))
+* **types:** fixed `CommonRequestHeadersList` & `CommonResponseHeadersList` types to be private in commonJS; ([#5503](https://github.com/axios/axios/issues/5503)) ([5a3d0a3](https://github.com/axios/axios/commit/5a3d0a3234d77361a1bc7cedee2da1e11df08e2c))
+
+### Contributors to this release
+
+- ![avatar](https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;16) [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+24/-9 (#5503 #5502 )")
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+## [1.2.5](https://github.com/axios/axios/compare/v1.2.4...v1.2.5) (2023-01-26)
+
+
+### Bug Fixes
+
+* **types:** fixed AxiosHeaders to handle spread syntax by making all methods non-enumerable; ([#5499](https://github.com/axios/axios/issues/5499)) ([580f1e8](https://github.com/axios/axios/commit/580f1e8033a61baa38149d59fd16019de3932c22))
+
+### Contributors to this release
+
+- ![avatar](https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;16) [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+82/-54 (#5499 )")
+- ![avatar](https://avatars.githubusercontent.com/u/20516159?v&#x3D;4&amp;s&#x3D;16) [Elliot Ford](https://github.com/EFord36 "+1/-1 (#5462 )")
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+## [1.2.4](https://github.com/axios/axios/compare/v1.2.3...v1.2.4) (2023-01-22)
+
+
+### Bug Fixes
+
+* **types:** renamed `RawAxiosRequestConfig` back to `AxiosRequestConfig`; ([#5486](https://github.com/axios/axios/issues/5486)) ([2a71f49](https://github.com/axios/axios/commit/2a71f49bc6c68495fa419003a3107ed8bd703ad0))
+* **types:** fix `AxiosRequestConfig` generic; ([#5478](https://github.com/axios/axios/issues/5478)) ([9bce81b](https://github.com/axios/axios/commit/186ea062da8b7d578ae78b1a5c220986b9bce81b))
+
+### Contributors to this release
+
+- ![avatar](https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;16) [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+242/-108 (#5486 #5482 )")
+- ![avatar](https://avatars.githubusercontent.com/u/9430821?v&#x3D;4&amp;s&#x3D;16) [Daniel Hillmann](https://github.com/hilleer "+1/-1 (#5478 )")
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+## [1.2.3](https://github.com/axios/axios/compare/1.2.2...1.2.3) (2023-01-10)
+
+
+### Bug Fixes
+
+* **types:** fixed AxiosRequestConfig header interface by refactoring it to RawAxiosRequestConfig; ([#5420](https://github.com/axios/axios/issues/5420)) ([0811963](https://github.com/axios/axios/commit/08119634a22f1d5b19f5c9ea0adccb6d3eebc3bc))
+
+### Contributors to this release
+
+- ![avatar](https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;16) [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS "+938/-442 (#5456 #5455 #5453 #5451 #5449 #5447 #5446 #5443 #5442 #5439 #5420 )")
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+## [1.2.2] - 2022-12-29
+
+### Fixed
+- fix(ci): fix release script inputs [#5392](https://github.com/axios/axios/pull/5392)
+- fix(ci): prerelease scipts [#5377](https://github.com/axios/axios/pull/5377)
+- fix(ci): release scripts [#5376](https://github.com/axios/axios/pull/5376)
+- fix(ci): typescript tests [#5375](https://github.com/axios/axios/pull/5375)
+- fix: Brotli decompression [#5353](https://github.com/axios/axios/pull/5353)
+- fix: add missing HttpStatusCode [#5345](https://github.com/axios/axios/pull/5345)
+
+### Chores
+- chore(ci): set conventional-changelog header config [#5406](https://github.com/axios/axios/pull/5406)
+- chore(ci): fix automatic contributors resolving [#5403](https://github.com/axios/axios/pull/5403)
+- chore(ci): improved logging for the contributors list generator [#5398](https://github.com/axios/axios/pull/5398)
+- chore(ci): fix release action [#5397](https://github.com/axios/axios/pull/5397)
+- chore(ci): fix version bump script by adding bump argument for target version [#5393](https://github.com/axios/axios/pull/5393)
+- chore(deps): bump decode-uri-component from 0.2.0 to 0.2.2 [#5342](https://github.com/axios/axios/pull/5342)
+- chore(ci): GitHub Actions Release script [#5384](https://github.com/axios/axios/pull/5384)
+- chore(ci): release scripts [#5364](https://github.com/axios/axios/pull/5364)
+
+### Contributors to this release
+- ![avatar](https://avatars.githubusercontent.com/u/12586868?v&#x3D;4&amp;s&#x3D;16) [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS)
+- ![avatar](https://avatars.githubusercontent.com/u/1652293?v&#x3D;4&amp;s&#x3D;16) [Winnie](https://github.com/winniehell)
+
+## [1.2.1] - 2022-12-05
+
+### Changed
+- feat(exports): export mergeConfig [#5151](https://github.com/axios/axios/pull/5151)
+
+### Fixed
+- fix(CancelledError): include config [#4922](https://github.com/axios/axios/pull/4922)
+- fix(general): removing multiple/trailing/leading whitespace [#5022](https://github.com/axios/axios/pull/5022)
+- fix(headers): decompression for responses without Content-Length header [#5306](https://github.com/axios/axios/pull/5306)
+- fix(webWorker): exception to sending form data in web worker [#5139](https://github.com/axios/axios/pull/5139)
+
+### Refactors
+- refactor(types): AxiosProgressEvent.event type to any [#5308](https://github.com/axios/axios/pull/5308)
+- refactor(types): add missing types for static AxiosError.from method [#4956](https://github.com/axios/axios/pull/4956)
+
+### Chores
+- chore(docs): remove README link to non-existent upgrade guide [#5307](https://github.com/axios/axios/pull/5307)
+- chore(docs): typo in issue template name [#5159](https://github.com/axios/axios/pull/5159)
+
+### Contributors to this release
+
+- [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS)
+- [Zachary Lysobey](https://github.com/zachlysobey)
+- [Kevin Ennis](https://github.com/kevincennis)
+- [Philipp Loose](https://github.com/phloose)
+- [secondl1ght](https://github.com/secondl1ght)
+- [wenzheng](https://github.com/0x30)
+- [Ivan Barsukov](https://github.com/ovarn)
+- [Arthur Fiorette](https://github.com/arthurfiorette)
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+## [1.2.0] - 2022-11-10
+
+### Changed
+
+- changed: refactored module exports [#5162](https://github.com/axios/axios/pull/5162)
+- change: re-added support for loading Axios with require('axios').default [#5225](https://github.com/axios/axios/pull/5225)
+
+### Fixed
+
+- fix: improve AxiosHeaders class [#5224](https://github.com/axios/axios/pull/5224)
+- fix: TypeScript type definitions for commonjs [#5196](https://github.com/axios/axios/pull/5196)
+- fix: type definition of use method on AxiosInterceptorManager to match the the README [#5071](https://github.com/axios/axios/pull/5071)
+- fix: __dirname is not defined in the sandbox [#5269](https://github.com/axios/axios/pull/5269)
+- fix: AxiosError.toJSON method to avoid circular references [#5247](https://github.com/axios/axios/pull/5247)
+- fix: Z_BUF_ERROR when content-encoding is set but the response body is empty [#5250](https://github.com/axios/axios/pull/5250)
+
+### Refactors
+- refactor: allowing adapters to be loaded by name [#5277](https://github.com/axios/axios/pull/5277)
+
+### Chores
+
+- chore: force CI restart [#5243](https://github.com/axios/axios/pull/5243)
+- chore: update ECOSYSTEM.md [#5077](https://github.com/axios/axios/pull/5077)
+- chore: update get/index.html [#5116](https://github.com/axios/axios/pull/5116)
+- chore: update Sandbox UI/UX [#5205](https://github.com/axios/axios/pull/5205)
+- chore:(actions): remove git credentials after checkout [#5235](https://github.com/axios/axios/pull/5235)
+- chore(actions): bump actions/dependency-review-action from 2 to 3 [#5266](https://github.com/axios/axios/pull/5266)
+- chore(packages): bump loader-utils from 1.4.1 to 1.4.2 [#5295](https://github.com/axios/axios/pull/5295)
+- chore(packages): bump engine.io from 6.2.0 to 6.2.1 [#5294](https://github.com/axios/axios/pull/5294)
+- chore(packages): bump socket.io-parser from 4.0.4 to 4.0.5 [#5241](https://github.com/axios/axios/pull/5241)
+- chore(packages): bump loader-utils from 1.4.0 to 1.4.1 [#5245](https://github.com/axios/axios/pull/5245)
+- chore(docs): update Resources links in README [#5119](https://github.com/axios/axios/pull/5119)
+- chore(docs): update the link for JSON url [#5265](https://github.com/axios/axios/pull/5265)
+- chore(docs): fix broken links [#5218](https://github.com/axios/axios/pull/5218)
+- chore(docs): update and rename UPGRADE_GUIDE.md to MIGRATION_GUIDE.md [#5170](https://github.com/axios/axios/pull/5170)
+- chore(docs): typo fix line #856 and #920 [#5194](https://github.com/axios/axios/pull/5194)
+- chore(docs): typo fix #800 [#5193](https://github.com/axios/axios/pull/5193)
+- chore(docs): fix typos [#5184](https://github.com/axios/axios/pull/5184)
+- chore(docs): fix punctuation in README.md [#5197](https://github.com/axios/axios/pull/5197)
+- chore(docs): update readme in the Handling Errors section - issue reference #5260 [#5261](https://github.com/axios/axios/pull/5261)
+- chore: remove \b from filename [#5207](https://github.com/axios/axios/pull/5207)
+- chore(docs): update CHANGELOG.md [#5137](https://github.com/axios/axios/pull/5137)
+- chore: add sideEffects false to package.json [#5025](https://github.com/axios/axios/pull/5025)
+
+### Contributors to this release
+
+- [Maddy Miller](https://github.com/me4502)
+- [Amit Saini](https://github.com/amitsainii)
+- [ecyrbe](https://github.com/ecyrbe)
+- [Ikko Ashimine](https://github.com/eltociear)
+- [Geeth Gunnampalli](https://github.com/thetechie7)
+- [Shreem Asati](https://github.com/shreem-123)
+- [Frieder Bluemle](https://github.com/friederbluemle)
+- [윤세영](https://github.com/yunseyeong)
+- [Claudio Busatto](https://github.com/cjcbusatto)
+- [Remco Haszing](https://github.com/remcohaszing)
+- [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS)
+- [Csaba Maulis](https://github.com/om4csaba)
+- [MoPaMo](https://github.com/MoPaMo)
+- [Daniel Fjeldstad](https://github.com/w3bdesign)
+- [Adrien Brunet](https://github.com/adrien-may)
+- [Frazer Smith](https://github.com/Fdawgs)
+- [HaiTao](https://github.com/836334258)
+- [AZM](https://github.com/aziyatali)
+- [relbns](https://github.com/relbns)
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+## [1.1.3] - 2022-10-15
+
+### Added
+
+- Added custom params serializer support [#5113](https://github.com/axios/axios/pull/5113)
+
+### Fixed
+
+- Fixed top-level export to keep them in-line with static properties [#5109](https://github.com/axios/axios/pull/5109)
+- Stopped including null values to query string. [#5108](https://github.com/axios/axios/pull/5108)
+- Restored proxy config backwards compatibility with 0.x [#5097](https://github.com/axios/axios/pull/5097)
+- Added back AxiosHeaders in AxiosHeaderValue [#5103](https://github.com/axios/axios/pull/5103)
+- Pin CDN install instructions to a specific version [#5060](https://github.com/axios/axios/pull/5060)
+- Handling of array values fixed for AxiosHeaders [#5085](https://github.com/axios/axios/pull/5085)
+
+### Chores
+
+- docs: match badge style, add link to them [#5046](https://github.com/axios/axios/pull/5046)
+- chore: fixing comments typo [#5054](https://github.com/axios/axios/pull/5054)
+- chore: update issue template [#5061](https://github.com/axios/axios/pull/5061)
+- chore: added progress capturing section to the docs; [#5084](https://github.com/axios/axios/pull/5084)
+
+### Contributors to this release
+
+- [Jason Saayman](https://github.com/jasonsaayman)
+- [scarf](https://github.com/scarf005)
+- [Lenz Weber-Tronic](https://github.com/phryneas)
+- [Arvindh](https://github.com/itsarvindh)
+- [Félix Legrelle](https://github.com/FelixLgr)
+- [Patrick Petrovic](https://github.com/ppati000)
+- [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS)
+- [littledian](https://github.com/littledian)
+- [ChronosMasterOfAllTime](https://github.com/ChronosMasterOfAllTime)
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+## [1.1.2] - 2022-10-07
+
+### Fixed
+
+- Fixed broken exports for UMD builds.
+
+### Contributors to this release
+
+- [Jason Saayman](https://github.com/jasonsaayman)
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+## [1.1.1] - 2022-10-07
+
+### Fixed
+
+- Fixed broken exports for common js. This fix breaks a prior fix, I will fix both issues ASAP but the commonJS use is more impactful.
+
+### Contributors to this release
+
+- [Jason Saayman](https://github.com/jasonsaayman)
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+## [1.1.0] - 2022-10-06
+
+### Fixed
+
+- Fixed missing exports in type definition index.d.ts [#5003](https://github.com/axios/axios/pull/5003)
+- Fixed query params composing [#5018](https://github.com/axios/axios/pull/5018)
+- Fixed GenericAbortSignal interface by making it more generic [#5021](https://github.com/axios/axios/pull/5021)
+- Fixed adding "clear" to AxiosInterceptorManager [#5010](https://github.com/axios/axios/pull/5010)
+- Fixed commonjs & umd exports [#5030](https://github.com/axios/axios/pull/5030)
+- Fixed inability to access response headers when using axios 1.x with Jest [#5036](https://github.com/axios/axios/pull/5036)
+
+### Contributors to this release
+
+- [Trim21](https://github.com/trim21)
+- [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS)
+- [shingo.sasaki](https://github.com/s-sasaki-0529)
+- [Ivan Pepelko](https://github.com/ivanpepelko)
+- [Richard Kořínek](https://github.com/risa)
+
+### PRs
+- CVE 2023 45857 ( [#6028](https://api.github.com/repos/axios/axios/pulls/6028) )
+```
+
+⚠️ Critical vulnerability fix. See https://security.snyk.io/vuln/SNYK-JS-AXIOS-6032459
+```
+
+## [1.0.0] - 2022-10-04
+
+### Added
+
+- Added stack trace to AxiosError [#4624](https://github.com/axios/axios/pull/4624)
+- Add AxiosError to AxiosStatic [#4654](https://github.com/axios/axios/pull/4654)
+- Replaced Rollup as our build runner [#4596](https://github.com/axios/axios/pull/4596)
+- Added generic TS types for the exposed toFormData helper [#4668](https://github.com/axios/axios/pull/4668)
+- Added listen callback function [#4096](https://github.com/axios/axios/pull/4096)
+- Added instructions for installing using PNPM [#4207](https://github.com/axios/axios/pull/4207)
+- Added generic AxiosAbortSignal TS interface to avoid importing AbortController polyfill [#4229](https://github.com/axios/axios/pull/4229)
+- Added axios-url-template in ECOSYSTEM.md [#4238](https://github.com/axios/axios/pull/4238)
+- Added a clear() function to the request and response interceptors object so a user can ensure that all interceptors have been removed from an axios instance [#4248](https://github.com/axios/axios/pull/4248)
+- Added react hook plugin [#4319](https://github.com/axios/axios/pull/4319)
+- Adding HTTP status code for transformResponse [#4580](https://github.com/axios/axios/pull/4580)
+- Added blob to the list of protocols supported by the browser [#4678](https://github.com/axios/axios/pull/4678)
+- Resolving proxy from env on redirect [#4436](https://github.com/axios/axios/pull/4436)
+- Added enhanced toFormData implementation with additional options [4704](https://github.com/axios/axios/pull/4704)
+- Adding Canceler parameters config and request [#4711](https://github.com/axios/axios/pull/4711)
+- Added automatic payload serialization to application/x-www-form-urlencoded [#4714](https://github.com/axios/axios/pull/4714)
+- Added the ability for webpack users to overwrite built-ins [#4715](https://github.com/axios/axios/pull/4715)
+- Added string[] to AxiosRequestHeaders type [#4322](https://github.com/axios/axios/pull/4322)
+- Added the ability for the url-encoded-form serializer to respect the formSerializer config [#4721](https://github.com/axios/axios/pull/4721)
+- Added isCancel type assert [#4293](https://github.com/axios/axios/pull/4293)
+- Added data URL support for node.js [#4725](https://github.com/axios/axios/pull/4725)
+- Adding types for progress event callbacks [#4675](https://github.com/axios/axios/pull/4675)
+- URL params serializer [#4734](https://github.com/axios/axios/pull/4734)
+- Added axios.formToJSON method [#4735](https://github.com/axios/axios/pull/4735)
+- Bower platform add data protocol [#4804](https://github.com/axios/axios/pull/4804)
+- Use WHATWG URL API instead of url.parse() [#4852](https://github.com/axios/axios/pull/4852)
+- Add ENUM containing Http Status Codes to typings [#4903](https://github.com/axios/axios/pull/4903)
+- Improve typing of timeout in index.d.ts [#4934](https://github.com/axios/axios/pull/4934)
+
+### Changed
+
+- Updated AxiosError.config to be optional in the type definition [#4665](https://github.com/axios/axios/pull/4665)
+- Updated README emphasizing the URLSearchParam built-in interface over other solutions [#4590](https://github.com/axios/axios/pull/4590)
+- Include request and config when creating a CanceledError instance [#4659](https://github.com/axios/axios/pull/4659)
+- Changed func-names eslint rule to as-needed [#4492](https://github.com/axios/axios/pull/4492)
+- Replacing deprecated substr() with slice() as substr() is deprecated [#4468](https://github.com/axios/axios/pull/4468)
+- Updating HTTP links in README.md to use HTTPS [#4387](https://github.com/axios/axios/pull/4387)
+- Updated to a better trim() polyfill [#4072](https://github.com/axios/axios/pull/4072)
+- Updated types to allow specifying partial default headers on instance create [#4185](https://github.com/axios/axios/pull/4185)
+- Expanded isAxiosError types [#4344](https://github.com/axios/axios/pull/4344)
+- Updated type definition for axios instance methods [#4224](https://github.com/axios/axios/pull/4224)
+- Updated eslint config [#4722](https://github.com/axios/axios/pull/4722)
+- Updated Docs [#4742](https://github.com/axios/axios/pull/4742)
+- Refactored Axios to use ES2017 [#4787](https://github.com/axios/axios/pull/4787)
+
+
+### Deprecated
+- There are multiple deprecations, refactors and fixes provided in this release. Please read through the full release notes to see how this may impact your project and use case.
+
+### Removed
+
+- Removed incorrect argument for NetworkError constructor [#4656](https://github.com/axios/axios/pull/4656)
+- Removed Webpack [#4596](https://github.com/axios/axios/pull/4596)
+- Removed function that transform arguments to array [#4544](https://github.com/axios/axios/pull/4544)
+
+### Fixed
+
+- Fixed grammar in README [#4649](https://github.com/axios/axios/pull/4649)
+- Fixed code error in README [#4599](https://github.com/axios/axios/pull/4599)
+- Optimized the code that checks cancellation [#4587](https://github.com/axios/axios/pull/4587)
+- Fix url pointing to defaults.js in README [#4532](https://github.com/axios/axios/pull/4532)
+- Use type alias instead of interface for AxiosPromise [#4505](https://github.com/axios/axios/pull/4505)
+- Fix some word spelling and lint style in code comments [#4500](https://github.com/axios/axios/pull/4500)
+- Edited readme with 3 updated browser icons of Chrome, FireFox and Safari [#4414](https://github.com/axios/axios/pull/4414)
+- Bump follow-redirects from 1.14.9 to 1.15.0 [#4673](https://github.com/axios/axios/pull/4673)
+- Fixing http tests to avoid hanging when assertions fail [#4435](https://github.com/axios/axios/pull/4435)
+- Fix TS definition for AxiosRequestTransformer [#4201](https://github.com/axios/axios/pull/4201)
+- Fix grammatical issues in README [#4232](https://github.com/axios/axios/pull/4232)
+- Fixing instance.defaults.headers type [#4557](https://github.com/axios/axios/pull/4557)
+- Fixed race condition on immediate requests cancellation [#4261](https://github.com/axios/axios/pull/4261)
+- Fixing Z_BUF_ERROR when no content [#4701](https://github.com/axios/axios/pull/4701)
+- Fixing proxy beforeRedirect regression [#4708](https://github.com/axios/axios/pull/4708)
+- Fixed AxiosError status code type [#4717](https://github.com/axios/axios/pull/4717)
+- Fixed AxiosError stack capturing [#4718](https://github.com/axios/axios/pull/4718)
+- Fixing AxiosRequestHeaders typings [#4334](https://github.com/axios/axios/pull/4334)
+- Fixed max body length defaults [#4731](https://github.com/axios/axios/pull/4731)
+- Fixed toFormData Blob issue on node>v17 [#4728](https://github.com/axios/axios/pull/4728)
+- Bump grunt from 1.5.2 to 1.5.3 [#4743](https://github.com/axios/axios/pull/4743)
+- Fixing content-type header repeated [#4745](https://github.com/axios/axios/pull/4745)
+- Fixed timeout error message for http [4738](https://github.com/axios/axios/pull/4738)
+- Request ignores false, 0 and empty string as body values [#4785](https://github.com/axios/axios/pull/4785)
+- Added back missing minified builds [#4805](https://github.com/axios/axios/pull/4805)
+- Fixed a type error [#4815](https://github.com/axios/axios/pull/4815)
+- Fixed a regression bug with unsubscribing from cancel token; [#4819](https://github.com/axios/axios/pull/4819)
+- Remove repeated compression algorithm [#4820](https://github.com/axios/axios/pull/4820)
+- The error of calling extend to pass parameters [#4857](https://github.com/axios/axios/pull/4857)
+- SerializerOptions.indexes allows boolean | null | undefined [#4862](https://github.com/axios/axios/pull/4862)
+- Require interceptors to return values [#4874](https://github.com/axios/axios/pull/4874)
+- Removed unused imports [#4949](https://github.com/axios/axios/pull/4949)
+- Allow null indexes on formSerializer and paramsSerializer [#4960](https://github.com/axios/axios/pull/4960)
+
+### Chores
+- Set permissions for GitHub actions [#4765](https://github.com/axios/axios/pull/4765)
+- Included githubactions in the dependabot config [#4770](https://github.com/axios/axios/pull/4770)
+- Included dependency review [#4771](https://github.com/axios/axios/pull/4771)
+- Update security.md [#4784](https://github.com/axios/axios/pull/4784)
+- Remove unnecessary spaces [#4854](https://github.com/axios/axios/pull/4854)
+- Simplify the import path of AxiosError [#4875](https://github.com/axios/axios/pull/4875)
+- Fix Gitpod dead link [#4941](https://github.com/axios/axios/pull/4941)
+- Enable syntax highlighting for a code block [#4970](https://github.com/axios/axios/pull/4970)
+- Using Logo Axios in Readme.md [#4993](https://github.com/axios/axios/pull/4993)
+- Fix markup for note in README [#4825](https://github.com/axios/axios/pull/4825)
+- Fix typo and formatting, add colons [#4853](https://github.com/axios/axios/pull/4853)
+- Fix typo in readme [#4942](https://github.com/axios/axios/pull/4942)
+
+### Security
+
+- Update SECURITY.md [#4687](https://github.com/axios/axios/pull/4687)
+
+### Contributors to this release
+
+- [Bertrand Marron](https://github.com/tusbar)
+- [Dmitriy Mozgovoy](https://github.com/DigitalBrainJS)
+- [Dan Mooney](https://github.com/danmooney)
+- [Michael Li](https://github.com/xiaoyu-tamu)
+- [aong](https://github.com/yxwzaxns)
+- [Des Preston](https://github.com/despreston)
+- [Ted Robertson](https://github.com/tredondo)
+- [zhoulixiang](https://github.com/zh-lx)
+- [Arthur Fiorette](https://github.com/arthurfiorette)
+- [Kumar Shanu](https://github.com/Kr-Shanu)
+- [JALAL](https://github.com/JLL32)
+- [Jingyi Lin](https://github.com/MageeLin)
+- [Philipp Loose](https://github.com/phloose)
+- [Alexander Shchukin](https://github.com/sashsvamir)
+- [Dave Cardwell](https://github.com/davecardwell)
+- [Cat Scarlet](https://github.com/catscarlet)
+- [Luca Pizzini](https://github.com/lpizzinidev)
+- [Kai](https://github.com/Schweinepriester)
+- [Maxime Bargiel](https://github.com/mbargiel)
+- [Brian Helba](https://github.com/brianhelba)
+- [reslear](https://github.com/reslear)
+- [Jamie Slome](https://github.com/JamieSlome)
+- [Landro3](https://github.com/Landro3)
+- [rafw87](https://github.com/rafw87)
+- [Afzal Sayed](https://github.com/afzalsayed96)
+- [Koki Oyatsu](https://github.com/kaishuu0123)
+- [Dave](https://github.com/wangcch)
+- [暴走老七](https://github.com/baozouai)
+- [Spencer](https://github.com/spalger)
+- [Adrian Wieprzkowicz](https://github.com/Argeento)
+- [Jamie Telin](https://github.com/lejahmie)
+- [毛呆](https://github.com/aweikalee)
+- [Kirill Shakirov](https://github.com/turisap)
+- [Rraji Abdelbari](https://github.com/estarossa0)
+- [Jelle Schutter](https://github.com/jelleschutter)
+- [Tom Ceuppens](https://github.com/KyorCode)
+- [Johann Cooper](https://github.com/JohannCooper)
+- [Dimitris Halatsis](https://github.com/mitsos1os)
+- [chenjigeng](https://github.com/chenjigeng)
+- [João Gabriel Quaresma](https://github.com/joaoGabriel55)
+- [Victor Augusto](https://github.com/VictorAugDB)
+- [neilnaveen](https://github.com/neilnaveen)
+- [Pavlos](https://github.com/psmoros)
+- [Kiryl Valkovich](https://github.com/visortelle)
+- [Naveen](https://github.com/naveensrinivasan)
+- [wenzheng](https://github.com/0x30)
+- [hcwhan](https://github.com/hcwhan)
+- [Bassel Rachid](https://github.com/basselworkforce)
+- [Grégoire Pineau](https://github.com/lyrixx)
+- [felipedamin](https://github.com/felipedamin)
+- [Karl Horky](https://github.com/karlhorky)
+- [Yue JIN](https://github.com/kingyue737)
+- [Usman Ali Siddiqui](https://github.com/usman250994)
+- [WD](https://github.com/techbirds)
+- [Günther Foidl](https://github.com/gfoidl)
+- [Stephen Jennings](https://github.com/jennings)
+- [C.T.Lin](https://github.com/chentsulin)
+- [mia-z](https://github.com/mia-z)
+- [Parth Banathia](https://github.com/Parth0105)
+- [parth0105pluang](https://github.com/parth0105pluang)
+- [Marco Weber](https://github.com/mrcwbr)
+- [Luca Pizzini](https://github.com/lpizzinidev)
+- [Willian Agostini](https://github.com/WillianAgostini)
+- [Huyen Nguyen](https://github.com/huyenltnguyen)
\ No newline at end of file
diff --git a/tortoise/node_modules/axios/LICENSE b/tortoise/node_modules/axios/LICENSE
new file mode 100644
index 0000000..05006a5
--- /dev/null
+++ b/tortoise/node_modules/axios/LICENSE
@@ -0,0 +1,7 @@
+# Copyright (c) 2014-present Matt Zabriskie & Collaborators
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/tortoise/node_modules/axios/MIGRATION_GUIDE.md b/tortoise/node_modules/axios/MIGRATION_GUIDE.md
new file mode 100644
index 0000000..ec3ae0d
--- /dev/null
+++ b/tortoise/node_modules/axios/MIGRATION_GUIDE.md
@@ -0,0 +1,3 @@
+# Migration Guide
+
+## 0.x.x -> 1.1.0
diff --git a/tortoise/node_modules/axios/README.md b/tortoise/node_modules/axios/README.md
new file mode 100644
index 0000000..33c4749
--- /dev/null
+++ b/tortoise/node_modules/axios/README.md
@@ -0,0 +1,1645 @@
+
+<h3 align="center"> 🥇 Gold sponsors <br> </h3> <table align="center" width="100%"><tr width="33.333333333333336%"><td align="center" width="33.333333333333336%"> <a href="https://opencollective.com/buy-instagram-followers-buzzoid?utm_source&#x3D;axios&amp;utm_medium&#x3D;sponsorlist&amp;utm_campaign&#x3D;sponsorship" style="padding: 10px; display: inline-block"> <img width="62px" height="70px" src="https://axios-http.com/assets/sponsors/opencollective/buy-instagram-followers-buzzoid.png" alt="Buzzoid - Buy Instagram Followers"/> </a> <p align="center" title="At Buzzoid, you can buy Instagram followers quickly, safely, and easily with just a few clicks. Rated world&#x27;s #1 IG service since 2012.">At Buzzoid, you can buy Instagram followers quickly, safely, and easily with just a few clicks. Rate...</p> <p align="center"> </p>
+</td><td align="center" width="33.333333333333336%"> <a href="https://stytch.com/?utm_source&#x3D;oss-sponsorship&amp;utm_medium&#x3D;paid_sponsorship&amp;utm_content&#x3D;website-link&amp;utm_campaign&#x3D;axios-http" style="padding: 10px; display: inline-block"> <picture> <source width="200px" height="38px" media="(prefers-color-scheme: dark)" srcset="/assets/sponsors/stytch_white.png"> <img width="200px" height="38px" src="https://axios-http.com/assets/sponsors/stytch.png" alt="Stytch"/> </picture> </a> <p align="center" title="API-first authentication, authorization, and fraud prevention">API-first authentication, authorization, and fraud prevention</p> <p align="center"> <a href="https://stytch.com/?utm_source&#x3D;oss-sponsorship&amp;utm_medium&#x3D;paid_sponsorship&amp;utm_content&#x3D;website-link&amp;utm_campaign&#x3D;axios-http"><b>Website</b></a> | <a href="https://stytch.com/docs?utm_source&#x3D;oss-sponsorship&amp;utm_medium&#x3D;paid_sponsorship&amp;utm_content&#x3D;docs-link&amp;utm_campaign&#x3D;axios-http"><b>Documentation</b></a> | <a href="https://github.com/stytchauth/stytch-node?utm_source&#x3D;oss-sponsorship&amp;utm_medium&#x3D;paid_sponsorship&amp;utm_content&#x3D;node-sdk&amp;utm_campaign&#x3D;axios-http"><b>Node.js</b></a> </p>
+</td><td align="center" width="33.333333333333336%"> <a href="https://www.principal.com/about-us?utm_source&#x3D;axios&amp;utm_medium&#x3D;sponsorlist&amp;utm_campaign&#x3D;sponsorship" style="padding: 10px; display: inline-block"> <img width="133px" height="43px" src="https://axios-http.com/assets/sponsors/principal.svg" alt="Principal Financial Group"/> </a> <p align="center" title="We’re bound by one common purpose: to give you the financial tools, resources and information you need to live your best life.">We’re bound by one common purpose: to give you the financial tools, resources and information you ne...</p> <p align="center"> </p>
+</td></tr><tr width="33.333333333333336%"><td align="center" width="33.333333333333336%"> <a href="https://www.descope.com/?utm_source&#x3D;axios&amp;utm_medium&#x3D;referral&amp;utm_campaign&#x3D;axios-oss-sponsorship" style="padding: 10px; display: inline-block"> <picture> <source width="200px" height="52px" media="(prefers-color-scheme: dark)" srcset="/assets/sponsors/descope_white.png"> <img width="200px" height="52px" src="https://axios-http.com/assets/sponsors/descope.png" alt="Descope"/> </picture> </a> <p align="center" title="Hi, we&#x27;re Descope! We are building something in the authentication space for app developers and can’t wait to place it in your hands.">Hi, we&#x27;re Descope! We are building something in the authentication space for app developers and...</p> <p align="center"> <a href="https://www.descope.com/?utm_source&#x3D;axios&amp;utm_medium&#x3D;referral&amp;utm_campaign&#x3D;axios-oss-sponsorship"><b>Website</b></a> | <a href="https://docs.descope.com/?utm_source&#x3D;axios&amp;utm_medium&#x3D;referral&amp;utm_campaign&#x3D;axios-oss-sponsorship"><b>Documentation</b></a> | <a href="https://www.descope.com/community?utm_source&#x3D;axios&amp;utm_medium&#x3D;referral&amp;utm_campaign&#x3D;axios-oss-sponsorship"><b>Community</b></a> </p>
+</td><td align="center" width="33.333333333333336%"> <a href="https://route4me.com/?utm_source&#x3D;axios&amp;utm_medium&#x3D;sponsorlist&amp;utm_campaign&#x3D;sponsorship" style="padding: 10px; display: inline-block"> <picture> <source width="200px" height="51px" media="(prefers-color-scheme: dark)" srcset="/assets/sponsors/route4me_white.png"> <img width="200px" height="51px" src="https://axios-http.com/assets/sponsors/route4me.png" alt="Route4Me"/> </picture> </a> <p align="center" title="Best Route Planning And Route Optimization Software">Best Route Planning And Route Optimization Software</p> <p align="center"> <a href="https://route4me.com/platform/route-optimization-software"><b>Explore</b></a> | <a href="https://route4me.com/platform/marketplace/pricing"><b>Free Trial</b></a> | <a href="https://route4me.com/contact"><b>Contact</b></a> </p>
+</td></tr></table>
+
+<!--<div>marker</div>-->
+<br><br>
+<div align="center">
+   <a href="https://axios-http.com"><img src="https://axios-http.com/assets/logo.svg" /></a><br>
+</div>
+
+<p align="center">Promise based HTTP client for the browser and node.js</p>
+
+<p align="center">
+    <a href="https://axios-http.com/"><b>Website</b></a> •
+    <a href="https://axios-http.com/docs/intro"><b>Documentation</b></a>
+</p>
+
+<div align="center">
+
+[![npm version](https://img.shields.io/npm/v/axios.svg?style=flat-square)](https://www.npmjs.org/package/axios)
+[![CDNJS](https://img.shields.io/cdnjs/v/axios.svg?style=flat-square)](https://cdnjs.com/libraries/axios)
+[![Build status](https://img.shields.io/github/actions/workflow/status/axios/axios/ci.yml?branch=v1.x&label=CI&logo=github&style=flat-square)](https://github.com/axios/axios/actions/workflows/ci.yml)
+[![Gitpod Ready-to-Code](https://img.shields.io/badge/Gitpod-Ready--to--Code-blue?logo=gitpod&style=flat-square)](https://gitpod.io/#https://github.com/axios/axios)
+[![code coverage](https://img.shields.io/coveralls/mzabriskie/axios.svg?style=flat-square)](https://coveralls.io/r/mzabriskie/axios)
+[![install size](https://img.shields.io/badge/dynamic/json?url=https://packagephobia.com/v2/api.json?p=axios&query=$.install.pretty&label=install%20size&style=flat-square)](https://packagephobia.now.sh/result?p=axios)
+[![npm bundle size](https://img.shields.io/bundlephobia/minzip/axios?style=flat-square)](https://bundlephobia.com/package/axios@latest)
+[![npm downloads](https://img.shields.io/npm/dm/axios.svg?style=flat-square)](https://npm-stat.com/charts.html?package=axios)
+[![gitter chat](https://img.shields.io/gitter/room/mzabriskie/axios.svg?style=flat-square)](https://gitter.im/mzabriskie/axios)
+[![code helpers](https://www.codetriage.com/axios/axios/badges/users.svg)](https://www.codetriage.com/axios/axios)
+[![Known Vulnerabilities](https://snyk.io/test/npm/axios/badge.svg)](https://snyk.io/test/npm/axios)
+
+
+
+
+</div>
+
+## Table of Contents
+
+  - [Features](#features)
+  - [Browser Support](#browser-support)
+  - [Installing](#installing)
+    - [Package manager](#package-manager)
+    - [CDN](#cdn)
+  - [Example](#example)
+  - [Axios API](#axios-api)
+  - [Request method aliases](#request-method-aliases)
+  - [Concurrency 👎](#concurrency-deprecated)
+  - [Creating an instance](#creating-an-instance)
+  - [Instance methods](#instance-methods)
+  - [Request Config](#request-config)
+  - [Response Schema](#response-schema)
+  - [Config Defaults](#config-defaults)
+    - [Global axios defaults](#global-axios-defaults)
+    - [Custom instance defaults](#custom-instance-defaults)
+    - [Config order of precedence](#config-order-of-precedence)
+  - [Interceptors](#interceptors)
+    - [Multiple Interceptors](#multiple-interceptors)
+  - [Handling Errors](#handling-errors)
+  - [Cancellation](#cancellation)
+    - [AbortController](#abortcontroller)
+    - [CancelToken 👎](#canceltoken-deprecated)
+  - [Using application/x-www-form-urlencoded format](#using-applicationx-www-form-urlencoded-format)
+    - [URLSearchParams](#urlsearchparams)
+    - [Query string](#query-string-older-browsers)
+    - [🆕 Automatic serialization](#-automatic-serialization-to-urlsearchparams)
+  - [Using multipart/form-data format](#using-multipartform-data-format)
+    - [FormData](#formdata)
+    - [🆕 Automatic serialization](#-automatic-serialization-to-formdata)
+  - [Files Posting](#files-posting)
+  - [HTML Form Posting](#-html-form-posting-browser)
+  - [🆕 Progress capturing](#-progress-capturing)
+  - [🆕 Rate limiting](#-progress-capturing)
+  - [🆕 AxiosHeaders](#-axiosheaders)
+  - [🔥 Fetch adapter](#-fetch-adapter)
+  - [Semver](#semver)
+  - [Promises](#promises)
+  - [TypeScript](#typescript)
+  - [Resources](#resources)
+  - [Credits](#credits)
+  - [License](#license)
+
+## Features
+
+- Make [XMLHttpRequests](https://developer.mozilla.org/en-US/docs/Web/API/XMLHttpRequest) from the browser
+- Make [http](https://nodejs.org/api/http.html) requests from node.js
+- Supports the [Promise](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Promise) API
+- Intercept request and response
+- Transform request and response data
+- Cancel requests
+- Automatic transforms for [JSON](https://www.json.org/json-en.html) data
+- 🆕 Automatic data object serialization to `multipart/form-data` and `x-www-form-urlencoded` body encodings
+- Client side support for protecting against [XSRF](https://en.wikipedia.org/wiki/Cross-site_request_forgery)
+
+## Browser Support
+
+![Chrome](https://raw.githubusercontent.com/alrra/browser-logos/main/src/chrome/chrome_48x48.png) | ![Firefox](https://raw.githubusercontent.com/alrra/browser-logos/main/src/firefox/firefox_48x48.png) | ![Safari](https://raw.githubusercontent.com/alrra/browser-logos/main/src/safari/safari_48x48.png) | ![Opera](https://raw.githubusercontent.com/alrra/browser-logos/main/src/opera/opera_48x48.png) | ![Edge](https://raw.githubusercontent.com/alrra/browser-logos/main/src/edge/edge_48x48.png) | ![IE](https://raw.githubusercontent.com/alrra/browser-logos/master/src/archive/internet-explorer_9-11/internet-explorer_9-11_48x48.png) |
+--- | --- | --- | --- | --- | --- |
+Latest ✔ | Latest ✔ | Latest ✔ | Latest ✔ | Latest ✔ | 11 ✔ |
+
+[![Browser Matrix](https://saucelabs.com/open_sauce/build_matrix/axios.svg)](https://saucelabs.com/u/axios)
+
+## Installing
+
+### Package manager
+
+Using npm:
+
+```bash
+$ npm install axios
+```
+
+Using bower:
+
+```bash
+$ bower install axios
+```
+
+Using yarn:
+
+```bash
+$ yarn add axios
+```
+
+Using pnpm:
+
+```bash
+$ pnpm add axios
+```
+
+Once the package is installed, you can import the library using `import` or `require` approach:
+
+```js
+import axios, {isCancel, AxiosError} from 'axios';
+```
+
+You can also use the default export, since the named export is just a re-export from the Axios factory:
+
+```js
+import axios from 'axios';
+
+console.log(axios.isCancel('something'));
+````
+
+If you use `require` for importing, **only default export is available**:
+
+```js
+const axios = require('axios');
+
+console.log(axios.isCancel('something'));
+```
+
+For cases where something went wrong when trying to import a module into a custom or legacy environment,
+you can try importing the module package directly:
+
+```js
+const axios = require('axios/dist/browser/axios.cjs'); // browser commonJS bundle (ES2017)
+// const axios = require('axios/dist/node/axios.cjs'); // node commonJS bundle (ES2017)
+```
+
+### CDN
+
+Using jsDelivr CDN (ES5 UMD browser module):
+
+```html
+<script src="https://cdn.jsdelivr.net/npm/axios@1.6.7/dist/axios.min.js"></script>
+```
+
+Using unpkg CDN:
+
+```html
+<script src="https://unpkg.com/axios@1.6.7/dist/axios.min.js"></script>
+```
+
+## Example
+
+> **Note**: CommonJS usage  
+> In order to gain the TypeScript typings (for intellisense / autocomplete) while using CommonJS imports with `require()`, use the following approach:
+
+```js
+import axios from 'axios';
+//const axios = require('axios'); // legacy way
+
+// Make a request for a user with a given ID
+axios.get('/user?ID=12345')
+  .then(function (response) {
+    // handle success
+    console.log(response);
+  })
+  .catch(function (error) {
+    // handle error
+    console.log(error);
+  })
+  .finally(function () {
+    // always executed
+  });
+
+// Optionally the request above could also be done as
+axios.get('/user', {
+    params: {
+      ID: 12345
+    }
+  })
+  .then(function (response) {
+    console.log(response);
+  })
+  .catch(function (error) {
+    console.log(error);
+  })
+  .finally(function () {
+    // always executed
+  });
+
+// Want to use async/await? Add the `async` keyword to your outer function/method.
+async function getUser() {
+  try {
+    const response = await axios.get('/user?ID=12345');
+    console.log(response);
+  } catch (error) {
+    console.error(error);
+  }
+}
+```
+
+> **Note**: `async/await` is part of ECMAScript 2017 and is not supported in Internet
+> Explorer and older browsers, so use with caution.
+
+Performing a `POST` request
+
+```js
+axios.post('/user', {
+    firstName: 'Fred',
+    lastName: 'Flintstone'
+  })
+  .then(function (response) {
+    console.log(response);
+  })
+  .catch(function (error) {
+    console.log(error);
+  });
+```
+
+Performing multiple concurrent requests
+
+```js
+function getUserAccount() {
+  return axios.get('/user/12345');
+}
+
+function getUserPermissions() {
+  return axios.get('/user/12345/permissions');
+}
+
+Promise.all([getUserAccount(), getUserPermissions()])
+  .then(function (results) {
+    const acct = results[0];
+    const perm = results[1];
+  });
+```
+
+## axios API
+
+Requests can be made by passing the relevant config to `axios`.
+
+##### axios(config)
+
+```js
+// Send a POST request
+axios({
+  method: 'post',
+  url: '/user/12345',
+  data: {
+    firstName: 'Fred',
+    lastName: 'Flintstone'
+  }
+});
+```
+
+```js
+// GET request for remote image in node.js
+axios({
+  method: 'get',
+  url: 'https://bit.ly/2mTM3nY',
+  responseType: 'stream'
+})
+  .then(function (response) {
+    response.data.pipe(fs.createWriteStream('ada_lovelace.jpg'))
+  });
+```
+
+##### axios(url[, config])
+
+```js
+// Send a GET request (default method)
+axios('/user/12345');
+```
+
+### Request method aliases
+
+For convenience, aliases have been provided for all common request methods.
+
+##### axios.request(config)
+##### axios.get(url[, config])
+##### axios.delete(url[, config])
+##### axios.head(url[, config])
+##### axios.options(url[, config])
+##### axios.post(url[, data[, config]])
+##### axios.put(url[, data[, config]])
+##### axios.patch(url[, data[, config]])
+
+###### NOTE
+When using the alias methods `url`, `method`, and `data` properties don't need to be specified in config.
+
+### Concurrency (Deprecated)
+Please use `Promise.all` to replace the below functions.
+
+Helper functions for dealing with concurrent requests.
+
+axios.all(iterable)
+axios.spread(callback)
+
+### Creating an instance
+
+You can create a new instance of axios with a custom config.
+
+##### axios.create([config])
+
+```js
+const instance = axios.create({
+  baseURL: 'https://some-domain.com/api/',
+  timeout: 1000,
+  headers: {'X-Custom-Header': 'foobar'}
+});
+```
+
+### Instance methods
+
+The available instance methods are listed below. The specified config will be merged with the instance config.
+
+##### axios#request(config)
+##### axios#get(url[, config])
+##### axios#delete(url[, config])
+##### axios#head(url[, config])
+##### axios#options(url[, config])
+##### axios#post(url[, data[, config]])
+##### axios#put(url[, data[, config]])
+##### axios#patch(url[, data[, config]])
+##### axios#getUri([config])
+
+## Request Config
+
+These are the available config options for making requests. Only the `url` is required. Requests will default to `GET` if `method` is not specified.
+
+```js
+{
+  // `url` is the server URL that will be used for the request
+  url: '/user',
+
+  // `method` is the request method to be used when making the request
+  method: 'get', // default
+
+  // `baseURL` will be prepended to `url` unless `url` is absolute.
+  // It can be convenient to set `baseURL` for an instance of axios to pass relative URLs
+  // to methods of that instance.
+  baseURL: 'https://some-domain.com/api/',
+
+  // `transformRequest` allows changes to the request data before it is sent to the server
+  // This is only applicable for request methods 'PUT', 'POST', 'PATCH' and 'DELETE'
+  // The last function in the array must return a string or an instance of Buffer, ArrayBuffer,
+  // FormData or Stream
+  // You may modify the headers object.
+  transformRequest: [function (data, headers) {
+    // Do whatever you want to transform the data
+
+    return data;
+  }],
+
+  // `transformResponse` allows changes to the response data to be made before
+  // it is passed to then/catch
+  transformResponse: [function (data) {
+    // Do whatever you want to transform the data
+
+    return data;
+  }],
+
+  // `headers` are custom headers to be sent
+  headers: {'X-Requested-With': 'XMLHttpRequest'},
+
+  // `params` are the URL parameters to be sent with the request
+  // Must be a plain object or a URLSearchParams object
+  params: {
+    ID: 12345
+  },
+  
+  // `paramsSerializer` is an optional config that allows you to customize serializing `params`. 
+  paramsSerializer: {
+
+    //Custom encoder function which sends key/value pairs in an iterative fashion.
+    encode?: (param: string): string => { /* Do custom operations here and return transformed string */ }, 
+    
+    // Custom serializer function for the entire parameter. Allows user to mimic pre 1.x behaviour.
+    serialize?: (params: Record<string, any>, options?: ParamsSerializerOptions ), 
+    
+    //Configuration for formatting array indexes in the params. 
+    indexes: false // Three available options: (1) indexes: null (leads to no brackets), (2) (default) indexes: false (leads to empty brackets), (3) indexes: true (leads to brackets with indexes).    
+  },
+
+  // `data` is the data to be sent as the request body
+  // Only applicable for request methods 'PUT', 'POST', 'DELETE , and 'PATCH'
+  // When no `transformRequest` is set, must be of one of the following types:
+  // - string, plain object, ArrayBuffer, ArrayBufferView, URLSearchParams
+  // - Browser only: FormData, File, Blob
+  // - Node only: Stream, Buffer, FormData (form-data package)
+  data: {
+    firstName: 'Fred'
+  },
+
+  // syntax alternative to send data into the body
+  // method post
+  // only the value is sent, not the key
+  data: 'Country=Brasil&City=Belo Horizonte',
+
+  // `timeout` specifies the number of milliseconds before the request times out.
+  // If the request takes longer than `timeout`, the request will be aborted.
+  timeout: 1000, // default is `0` (no timeout)
+
+  // `withCredentials` indicates whether or not cross-site Access-Control requests
+  // should be made using credentials
+  withCredentials: false, // default
+
+  // `adapter` allows custom handling of requests which makes testing easier.
+  // Return a promise and supply a valid response (see lib/adapters/README.md)
+  adapter: function (config) {
+    /* ... */
+  },
+  // Also, you can set the name of the built-in adapter, or provide an array with their names
+  // to choose the first available in the environment
+  adapter: 'xhr' // 'fetch' | 'http' | ['xhr', 'http', 'fetch']
+
+  // `auth` indicates that HTTP Basic auth should be used, and supplies credentials.
+  // This will set an `Authorization` header, overwriting any existing
+  // `Authorization` custom headers you have set using `headers`.
+  // Please note that only HTTP Basic auth is configurable through this parameter.
+  // For Bearer tokens and such, use `Authorization` custom headers instead.
+  auth: {
+    username: 'janedoe',
+    password: 's00pers3cret'
+  },
+
+  // `responseType` indicates the type of data that the server will respond with
+  // options are: 'arraybuffer', 'document', 'json', 'text', 'stream'
+  //   browser only: 'blob'
+  responseType: 'json', // default
+
+  // `responseEncoding` indicates encoding to use for decoding responses (Node.js only)
+  // Note: Ignored for `responseType` of 'stream' or client-side requests
+  // options are: 'ascii', 'ASCII', 'ansi', 'ANSI', 'binary', 'BINARY', 'base64', 'BASE64', 'base64url',
+  // 'BASE64URL', 'hex', 'HEX', 'latin1', 'LATIN1', 'ucs-2', 'UCS-2', 'ucs2', 'UCS2', 'utf-8', 'UTF-8',
+  // 'utf8', 'UTF8', 'utf16le', 'UTF16LE'
+  responseEncoding: 'utf8', // default
+
+  // `xsrfCookieName` is the name of the cookie to use as a value for xsrf token
+  xsrfCookieName: 'XSRF-TOKEN', // default
+
+  // `xsrfHeaderName` is the name of the http header that carries the xsrf token value
+  xsrfHeaderName: 'X-XSRF-TOKEN', // default
+    
+  // `undefined` (default) - set XSRF header only for the same origin requests
+  withXSRFToken: boolean | undefined | ((config: InternalAxiosRequestConfig) => boolean | undefined),
+
+  // `onUploadProgress` allows handling of progress events for uploads
+  // browser & node.js
+  onUploadProgress: function ({loaded, total, progress, bytes, estimated, rate, upload = true}) {
+    // Do whatever you want with the Axios progress event
+  },
+
+  // `onDownloadProgress` allows handling of progress events for downloads
+  // browser & node.js
+  onDownloadProgress: function ({loaded, total, progress, bytes, estimated, rate, download = true}) {
+    // Do whatever you want with the Axios progress event
+  },
+
+  // `maxContentLength` defines the max size of the http response content in bytes allowed in node.js
+  maxContentLength: 2000,
+
+  // `maxBodyLength` (Node only option) defines the max size of the http request content in bytes allowed
+  maxBodyLength: 2000,
+
+  // `validateStatus` defines whether to resolve or reject the promise for a given
+  // HTTP response status code. If `validateStatus` returns `true` (or is set to `null`
+  // or `undefined`), the promise will be resolved; otherwise, the promise will be
+  // rejected.
+  validateStatus: function (status) {
+    return status >= 200 && status < 300; // default
+  },
+
+  // `maxRedirects` defines the maximum number of redirects to follow in node.js.
+  // If set to 0, no redirects will be followed.
+  maxRedirects: 21, // default
+
+  // `beforeRedirect` defines a function that will be called before redirect.
+  // Use this to adjust the request options upon redirecting,
+  // to inspect the latest response headers,
+  // or to cancel the request by throwing an error
+  // If maxRedirects is set to 0, `beforeRedirect` is not used.
+  beforeRedirect: (options, { headers }) => {
+    if (options.hostname === "example.com") {
+      options.auth = "user:password";
+    }
+  },
+
+  // `socketPath` defines a UNIX Socket to be used in node.js.
+  // e.g. '/var/run/docker.sock' to send requests to the docker daemon.
+  // Only either `socketPath` or `proxy` can be specified.
+  // If both are specified, `socketPath` is used.
+  socketPath: null, // default
+  
+  // `transport` determines the transport method that will be used to make the request. If defined, it will be used. Otherwise, if `maxRedirects` is 0, the default `http` or `https` library will be used, depending on the protocol specified in `protocol`. Otherwise, the `httpFollow` or `httpsFollow` library will be used, again depending on the protocol, which can handle redirects.
+  transport: undefined, // default
+
+  // `httpAgent` and `httpsAgent` define a custom agent to be used when performing http
+  // and https requests, respectively, in node.js. This allows options to be added like
+  // `keepAlive` that are not enabled by default.
+  httpAgent: new http.Agent({ keepAlive: true }),
+  httpsAgent: new https.Agent({ keepAlive: true }),
+
+  // `proxy` defines the hostname, port, and protocol of the proxy server.
+  // You can also define your proxy using the conventional `http_proxy` and
+  // `https_proxy` environment variables. If you are using environment variables
+  // for your proxy configuration, you can also define a `no_proxy` environment
+  // variable as a comma-separated list of domains that should not be proxied.
+  // Use `false` to disable proxies, ignoring environment variables.
+  // `auth` indicates that HTTP Basic auth should be used to connect to the proxy, and
+  // supplies credentials.
+  // This will set an `Proxy-Authorization` header, overwriting any existing
+  // `Proxy-Authorization` custom headers you have set using `headers`.
+  // If the proxy server uses HTTPS, then you must set the protocol to `https`.
+  proxy: {
+    protocol: 'https',
+    host: '127.0.0.1',
+    // hostname: '127.0.0.1' // Takes precedence over 'host' if both are defined
+    port: 9000,
+    auth: {
+      username: 'mikeymike',
+      password: 'rapunz3l'
+    }
+  },
+
+  // `cancelToken` specifies a cancel token that can be used to cancel the request
+  // (see Cancellation section below for details)
+  cancelToken: new CancelToken(function (cancel) {
+  }),
+
+  // an alternative way to cancel Axios requests using AbortController
+  signal: new AbortController().signal,
+
+  // `decompress` indicates whether or not the response body should be decompressed
+  // automatically. If set to `true` will also remove the 'content-encoding' header
+  // from the responses objects of all decompressed responses
+  // - Node only (XHR cannot turn off decompression)
+  decompress: true, // default
+
+  // `insecureHTTPParser` boolean.
+  // Indicates where to use an insecure HTTP parser that accepts invalid HTTP headers.
+  // This may allow interoperability with non-conformant HTTP implementations.
+  // Using the insecure parser should be avoided.
+  // see options https://nodejs.org/dist/latest-v12.x/docs/api/http.html#http_http_request_url_options_callback
+  // see also https://nodejs.org/en/blog/vulnerability/february-2020-security-releases/#strict-http-header-parsing-none
+  insecureHTTPParser: undefined, // default
+
+  // transitional options for backward compatibility that may be removed in the newer versions
+  transitional: {
+    // silent JSON parsing mode
+    // `true`  - ignore JSON parsing errors and set response.data to null if parsing failed (old behaviour)
+    // `false` - throw SyntaxError if JSON parsing failed (Note: responseType must be set to 'json')
+    silentJSONParsing: true, // default value for the current Axios version
+
+    // try to parse the response string as JSON even if `responseType` is not 'json'
+    forcedJSONParsing: true,
+
+    // throw ETIMEDOUT error instead of generic ECONNABORTED on request timeouts
+    clarifyTimeoutError: false,
+  },
+
+  env: {
+    // The FormData class to be used to automatically serialize the payload into a FormData object
+    FormData: window?.FormData || global?.FormData
+  },
+
+  formSerializer: {
+      visitor: (value, key, path, helpers) => {}; // custom visitor function to serialize form values
+      dots: boolean; // use dots instead of brackets format
+      metaTokens: boolean; // keep special endings like {} in parameter key
+      indexes: boolean; // array indexes format null - no brackets, false - empty brackets, true - brackets with indexes
+  },
+
+  // http adapter only (node.js)
+  maxRate: [
+    100 * 1024, // 100KB/s upload limit,
+    100 * 1024  // 100KB/s download limit
+  ]
+}
+```
+
+## Response Schema
+
+The response for a request contains the following information.
+
+```js
+{
+  // `data` is the response that was provided by the server
+  data: {},
+
+  // `status` is the HTTP status code from the server response
+  status: 200,
+
+  // `statusText` is the HTTP status message from the server response
+  statusText: 'OK',
+
+  // `headers` the HTTP headers that the server responded with
+  // All header names are lowercase and can be accessed using the bracket notation.
+  // Example: `response.headers['content-type']`
+  headers: {},
+
+  // `config` is the config that was provided to `axios` for the request
+  config: {},
+
+  // `request` is the request that generated this response
+  // It is the last ClientRequest instance in node.js (in redirects)
+  // and an XMLHttpRequest instance in the browser
+  request: {}
+}
+```
+
+When using `then`, you will receive the response as follows:
+
+```js
+axios.get('/user/12345')
+  .then(function (response) {
+    console.log(response.data);
+    console.log(response.status);
+    console.log(response.statusText);
+    console.log(response.headers);
+    console.log(response.config);
+  });
+```
+
+When using `catch`, or passing a [rejection callback](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Promise/then) as second parameter of `then`, the response will be available through the `error` object as explained in the [Handling Errors](#handling-errors) section.
+
+## Config Defaults
+
+You can specify config defaults that will be applied to every request.
+
+### Global axios defaults
+
+```js
+axios.defaults.baseURL = 'https://api.example.com';
+
+// Important: If axios is used with multiple domains, the AUTH_TOKEN will be sent to all of them.
+// See below for an example using Custom instance defaults instead.
+axios.defaults.headers.common['Authorization'] = AUTH_TOKEN;
+
+axios.defaults.headers.post['Content-Type'] = 'application/x-www-form-urlencoded';
+```
+
+### Custom instance defaults
+
+```js
+// Set config defaults when creating the instance
+const instance = axios.create({
+  baseURL: 'https://api.example.com'
+});
+
+// Alter defaults after instance has been created
+instance.defaults.headers.common['Authorization'] = AUTH_TOKEN;
+```
+
+### Config order of precedence
+
+Config will be merged with an order of precedence. The order is library defaults found in [lib/defaults.js](https://github.com/axios/axios/blob/master/lib/defaults/index.js#L28), then `defaults` property of the instance, and finally `config` argument for the request. The latter will take precedence over the former. Here's an example.
+
+```js
+// Create an instance using the config defaults provided by the library
+// At this point the timeout config value is `0` as is the default for the library
+const instance = axios.create();
+
+// Override timeout default for the library
+// Now all requests using this instance will wait 2.5 seconds before timing out
+instance.defaults.timeout = 2500;
+
+// Override timeout for this request as it's known to take a long time
+instance.get('/longRequest', {
+  timeout: 5000
+});
+```
+
+## Interceptors
+
+You can intercept requests or responses before they are handled by `then` or `catch`.
+
+```js
+// Add a request interceptor
+axios.interceptors.request.use(function (config) {
+    // Do something before request is sent
+    return config;
+  }, function (error) {
+    // Do something with request error
+    return Promise.reject(error);
+  });
+
+// Add a response interceptor
+axios.interceptors.response.use(function (response) {
+    // Any status code that lie within the range of 2xx cause this function to trigger
+    // Do something with response data
+    return response;
+  }, function (error) {
+    // Any status codes that falls outside the range of 2xx cause this function to trigger
+    // Do something with response error
+    return Promise.reject(error);
+  });
+```
+
+If you need to remove an interceptor later you can.
+
+```js
+const myInterceptor = axios.interceptors.request.use(function () {/*...*/});
+axios.interceptors.request.eject(myInterceptor);
+```
+
+You can also clear all interceptors for requests or responses.
+```js
+const instance = axios.create();
+instance.interceptors.request.use(function () {/*...*/});
+instance.interceptors.request.clear(); // Removes interceptors from requests
+instance.interceptors.response.use(function () {/*...*/});
+instance.interceptors.response.clear(); // Removes interceptors from responses
+```
+
+You can add interceptors to a custom instance of axios.
+
+```js
+const instance = axios.create();
+instance.interceptors.request.use(function () {/*...*/});
+```
+
+When you add request interceptors, they are presumed to be asynchronous by default. This can cause a delay
+in the execution of your axios request when the main thread is blocked (a promise is created under the hood for
+the interceptor and your request gets put on the bottom of the call stack). If your request interceptors are synchronous you can add a flag
+to the options object that will tell axios to run the code synchronously and avoid any delays in request execution.
+
+```js
+axios.interceptors.request.use(function (config) {
+  config.headers.test = 'I am only a header!';
+  return config;
+}, null, { synchronous: true });
+```
+
+If you want to execute a particular interceptor based on a runtime check,
+you can add a `runWhen` function to the options object. The interceptor will not be executed **if and only if** the return
+of `runWhen` is `false`. The function will be called with the config
+object (don't forget that you can bind your own arguments to it as well.) This can be handy when you have an
+asynchronous request interceptor that only needs to run at certain times.
+
+```js
+function onGetCall(config) {
+  return config.method === 'get';
+}
+axios.interceptors.request.use(function (config) {
+  config.headers.test = 'special get headers';
+  return config;
+}, null, { runWhen: onGetCall });
+```
+
+### Multiple Interceptors
+
+Given you add multiple response interceptors
+and when the response was fulfilled
+- then each interceptor is executed
+- then they are executed in the order they were added
+- then only the last interceptor's result is returned
+- then every interceptor receives the result of its predecessor
+- and when the fulfillment-interceptor throws
+    - then the following fulfillment-interceptor is not called
+    - then the following rejection-interceptor is called
+    - once caught, another following fulfill-interceptor is called again (just like in a promise chain).
+
+Read [the interceptor tests](./test/specs/interceptors.spec.js) for seeing all this in code.
+
+## Error Types
+
+There are many different axios error messages that can appear that can provide basic information about the specifics of the error and where opportunities may lie in debugging.
+
+The general structure of axios errors is as follows:
+| Property  | Definition  |
+| -------- | ----------  |
+| message  | A quick summary of the error message and the status it failed with. |
+| name     | This defines where the error originated from. For axios, it will always be an 'AxiosError'. |
+| stack    | Provides the stack trace of the error. | 
+| config   | An axios config object with specific instance configurations defined by the user from when the request was made |
+| code     | Represents an axios identified error. The table below lists out specific definitions for internal axios error.  |
+| status   | HTTP response status code. See [here](https://en.wikipedia.org/wiki/List_of_HTTP_status_codes) for common HTTP response status code meanings. 
+
+Below is a list of potential axios identified error
+| Code  |  Definition   |
+| -------- | ----------  |
+| ERR_BAD_OPTION_VALUE | Invalid or unsupported value provided in axios configuration. |
+| ERR_BAD_OPTION | Invalid option provided in axios configuration. |
+| ECONNABORTED | Request timed out due to exceeding timeout specified in axios configuration. |
+| ETIMEDOUT | Request timed out due to exceeding default axios timelimit. |
+| ERR_NETWORK | Network-related issue.
+| ERR_FR_TOO_MANY_REDIRECTS | Request is redirected too many times; exceeds max redirects specified in axios configuration.
+| ERR_DEPRECATED | Deprecated feature or method used in axios. 
+| ERR_BAD_RESPONSE | Response cannot be parsed properly or is in an unexpected format. 
+| ERR_BAD_REQUEST | Requested has unexpected format or missing required parameters. |
+| ERR_CANCELED | Feature or method is canceled explicitly by the user.   
+| ERR_NOT_SUPPORT | Feature or method not supported in the current axios environment. 
+| ERR_INVALID_URL | Invalid URL provided for axios request.
+
+## Handling Errors
+
+the default behavior is to reject every response that returns with a status code that falls out of the range of 2xx and treat it as an error.
+
+```js
+axios.get('/user/12345')
+  .catch(function (error) {
+    if (error.response) {
+      // The request was made and the server responded with a status code
+      // that falls out of the range of 2xx
+      console.log(error.response.data);
+      console.log(error.response.status);
+      console.log(error.response.headers);
+    } else if (error.request) {
+      // The request was made but no response was received
+      // `error.request` is an instance of XMLHttpRequest in the browser and an instance of
+      // http.ClientRequest in node.js
+      console.log(error.request);
+    } else {
+      // Something happened in setting up the request that triggered an Error
+      console.log('Error', error.message);
+    }
+    console.log(error.config);
+  });
+```
+
+Using the `validateStatus` config option, you can override the default condition (status >= 200 && status < 300) and define HTTP code(s) that should throw an error.
+
+```js
+axios.get('/user/12345', {
+  validateStatus: function (status) {
+    return status < 500; // Resolve only if the status code is less than 500
+  }
+})
+```
+
+Using `toJSON` you get an object with more information about the HTTP error.
+
+```js
+axios.get('/user/12345')
+  .catch(function (error) {
+    console.log(error.toJSON());
+  });
+```
+
+## Cancellation
+
+### AbortController
+
+Starting from `v0.22.0` Axios supports AbortController to cancel requests in fetch API way:
+
+```js
+const controller = new AbortController();
+
+axios.get('/foo/bar', {
+   signal: controller.signal
+}).then(function(response) {
+   //...
+});
+// cancel the request
+controller.abort()
+```
+
+### CancelToken `👎deprecated`
+
+You can also cancel a request using a *CancelToken*.
+
+> The axios cancel token API is based on the withdrawn [cancellable promises proposal](https://github.com/tc39/proposal-cancelable-promises).
+
+> This API is deprecated since v0.22.0 and shouldn't be used in new projects
+
+You can create a cancel token using the `CancelToken.source` factory as shown below:
+
+```js
+const CancelToken = axios.CancelToken;
+const source = CancelToken.source();
+
+axios.get('/user/12345', {
+  cancelToken: source.token
+}).catch(function (thrown) {
+  if (axios.isCancel(thrown)) {
+    console.log('Request canceled', thrown.message);
+  } else {
+    // handle error
+  }
+});
+
+axios.post('/user/12345', {
+  name: 'new name'
+}, {
+  cancelToken: source.token
+})
+
+// cancel the request (the message parameter is optional)
+source.cancel('Operation canceled by the user.');
+```
+
+You can also create a cancel token by passing an executor function to the `CancelToken` constructor:
+
+```js
+const CancelToken = axios.CancelToken;
+let cancel;
+
+axios.get('/user/12345', {
+  cancelToken: new CancelToken(function executor(c) {
+    // An executor function receives a cancel function as a parameter
+    cancel = c;
+  })
+});
+
+// cancel the request
+cancel();
+```
+
+> **Note:** you can cancel several requests with the same cancel token/abort controller.
+> If a cancellation token is already cancelled at the moment of starting an Axios request, then the request is cancelled immediately, without any attempts to make a real request.
+
+> During the transition period, you can use both cancellation APIs, even for the same request:
+
+## Using `application/x-www-form-urlencoded` format
+
+### URLSearchParams
+
+By default, axios serializes JavaScript objects to `JSON`. To send data in the [`application/x-www-form-urlencoded` format](https://developer.mozilla.org/en-US/docs/Web/HTTP/Methods/POST) instead, you can use the [`URLSearchParams`](https://developer.mozilla.org/en-US/docs/Web/API/URLSearchParams) API, which is [supported](http://www.caniuse.com/#feat=urlsearchparams) in the vast majority of browsers,and [ Node](https://nodejs.org/api/url.html#url_class_urlsearchparams) starting with v10 (released in 2018).
+
+```js
+const params = new URLSearchParams({ foo: 'bar' });
+params.append('extraparam', 'value');
+axios.post('/foo', params);
+```
+
+### Query string (Older browsers)
+
+For compatibility with very old browsers, there is a [polyfill](https://github.com/WebReflection/url-search-params) available (make sure to polyfill the global environment).
+
+Alternatively, you can encode data using the [`qs`](https://github.com/ljharb/qs) library:
+
+```js
+const qs = require('qs');
+axios.post('/foo', qs.stringify({ 'bar': 123 }));
+```
+
+Or in another way (ES6),
+
+```js
+import qs from 'qs';
+const data = { 'bar': 123 };
+const options = {
+  method: 'POST',
+  headers: { 'content-type': 'application/x-www-form-urlencoded' },
+  data: qs.stringify(data),
+  url,
+};
+axios(options);
+```
+
+### Older Node.js versions
+
+For older Node.js engines, you can use the [`querystring`](https://nodejs.org/api/querystring.html) module as follows:
+
+```js
+const querystring = require('querystring');
+axios.post('https://something.com/', querystring.stringify({ foo: 'bar' }));
+```
+
+You can also use the [`qs`](https://github.com/ljharb/qs) library.
+
+> **Note**: The `qs` library is preferable if you need to stringify nested objects, as the `querystring` method has [known issues](https://github.com/nodejs/node-v0.x-archive/issues/1665) with that use case.
+
+### 🆕 Automatic serialization to URLSearchParams
+
+Axios will automatically serialize the data object to urlencoded format if the content-type header is set to "application/x-www-form-urlencoded".
+
+```js
+const data = {
+  x: 1,
+  arr: [1, 2, 3],
+  arr2: [1, [2], 3],
+  users: [{name: 'Peter', surname: 'Griffin'}, {name: 'Thomas', surname: 'Anderson'}],
+};
+
+await axios.postForm('https://postman-echo.com/post', data,
+  {headers: {'content-type': 'application/x-www-form-urlencoded'}}
+);
+```
+
+The server will handle it as:
+
+```js
+  {
+    x: '1',
+    'arr[]': [ '1', '2', '3' ],
+    'arr2[0]': '1',
+    'arr2[1][0]': '2',
+    'arr2[2]': '3',
+    'arr3[]': [ '1', '2', '3' ],
+    'users[0][name]': 'Peter',
+    'users[0][surname]': 'griffin',
+    'users[1][name]': 'Thomas',
+    'users[1][surname]': 'Anderson'
+  }
+````
+
+If your backend body-parser (like `body-parser` of `express.js`) supports nested objects decoding, you will get the same object on the server-side automatically
+
+```js
+  var app = express();
+
+  app.use(bodyParser.urlencoded({ extended: true })); // support encoded bodies
+
+  app.post('/', function (req, res, next) {
+     // echo body as JSON
+     res.send(JSON.stringify(req.body));
+  });
+
+  server = app.listen(3000);
+```
+
+## Using `multipart/form-data` format
+
+### FormData
+
+To send the data as a `multipart/formdata` you need to pass a formData instance as a payload.
+Setting the `Content-Type` header is not required as Axios guesses it based on the payload type.
+
+```js
+const formData = new FormData();
+formData.append('foo', 'bar');
+
+axios.post('https://httpbin.org/post', formData);
+```
+
+In node.js, you can use the [`form-data`](https://github.com/form-data/form-data) library as follows:
+
+```js
+const FormData = require('form-data');
+
+const form = new FormData();
+form.append('my_field', 'my value');
+form.append('my_buffer', new Buffer(10));
+form.append('my_file', fs.createReadStream('/foo/bar.jpg'));
+
+axios.post('https://example.com', form)
+```
+
+### 🆕 Automatic serialization to FormData
+
+Starting from `v0.27.0`, Axios supports automatic object serialization to a FormData object if the request `Content-Type`
+header is set to `multipart/form-data`.
+
+The following request will submit the data in a FormData format (Browser & Node.js):
+
+```js
+import axios from 'axios';
+
+axios.post('https://httpbin.org/post', {x: 1}, {
+  headers: {
+    'Content-Type': 'multipart/form-data'
+  }
+}).then(({data}) => console.log(data));
+```
+
+In the `node.js` build, the ([`form-data`](https://github.com/form-data/form-data)) polyfill is used by default.
+
+You can overload the FormData class by setting the `env.FormData` config variable,
+but you probably won't need it in most cases:
+
+```js
+const axios = require('axios');
+var FormData = require('form-data');
+
+axios.post('https://httpbin.org/post', {x: 1, buf: new Buffer(10)}, {
+  headers: {
+    'Content-Type': 'multipart/form-data'
+  }
+}).then(({data}) => console.log(data));
+```
+
+Axios FormData serializer supports some special endings to perform the following operations:
+
+- `{}` - serialize the value with JSON.stringify
+- `[]` - unwrap the array-like object as separate fields with the same key
+
+> **Note**: unwrap/expand operation will be used by default on arrays and FileList objects
+
+FormData serializer supports additional options via `config.formSerializer: object` property to handle rare cases:
+
+- `visitor: Function` - user-defined visitor function that will be called recursively to serialize the data object
+to a `FormData` object by following custom rules.
+
+- `dots: boolean = false` - use dot notation instead of brackets to serialize arrays and objects;
+
+- `metaTokens: boolean = true` - add the special ending (e.g `user{}: '{"name": "John"}'`) in the FormData key.
+The back-end body-parser could potentially use this meta-information to automatically parse the value as JSON.
+
+- `indexes: null|false|true = false` - controls how indexes will be added to unwrapped keys of `flat` array-like objects
+
+    - `null` - don't add brackets (`arr: 1`, `arr: 2`, `arr: 3`)
+    - `false`(default) - add empty brackets (`arr[]: 1`, `arr[]: 2`, `arr[]: 3`)
+    - `true` - add brackets with indexes  (`arr[0]: 1`, `arr[1]: 2`, `arr[2]: 3`)
+
+Let's say we have an object like this one:
+
+```js
+const obj = {
+  x: 1,
+  arr: [1, 2, 3],
+  arr2: [1, [2], 3],
+  users: [{name: 'Peter', surname: 'Griffin'}, {name: 'Thomas', surname: 'Anderson'}],
+  'obj2{}': [{x:1}]
+};
+```
+
+The following steps will be executed by the Axios serializer internally:
+
+```js
+const formData = new FormData();
+formData.append('x', '1');
+formData.append('arr[]', '1');
+formData.append('arr[]', '2');
+formData.append('arr[]', '3');
+formData.append('arr2[0]', '1');
+formData.append('arr2[1][0]', '2');
+formData.append('arr2[2]', '3');
+formData.append('users[0][name]', 'Peter');
+formData.append('users[0][surname]', 'Griffin');
+formData.append('users[1][name]', 'Thomas');
+formData.append('users[1][surname]', 'Anderson');
+formData.append('obj2{}', '[{"x":1}]');
+```
+
+Axios supports the following shortcut methods: `postForm`, `putForm`, `patchForm`
+which are just the corresponding http methods with the `Content-Type` header preset to `multipart/form-data`.
+
+## Files Posting
+
+You can easily submit a single file:
+
+```js
+await axios.postForm('https://httpbin.org/post', {
+  'myVar' : 'foo',
+  'file': document.querySelector('#fileInput').files[0]
+});
+```
+
+or multiple files as `multipart/form-data`:
+
+```js
+await axios.postForm('https://httpbin.org/post', {
+  'files[]': document.querySelector('#fileInput').files
+});
+```
+
+`FileList` object can be passed directly:
+
+```js
+await axios.postForm('https://httpbin.org/post', document.querySelector('#fileInput').files)
+```
+
+All files will be sent with the same field names: `files[]`.
+
+## 🆕 HTML Form Posting (browser)
+
+Pass HTML Form element as a payload to submit it as `multipart/form-data` content.
+
+```js
+await axios.postForm('https://httpbin.org/post', document.querySelector('#htmlForm'));
+```
+
+`FormData` and `HTMLForm` objects can also be posted as `JSON` by explicitly setting the `Content-Type` header to `application/json`:
+
+```js
+await axios.post('https://httpbin.org/post', document.querySelector('#htmlForm'), {
+  headers: {
+    'Content-Type': 'application/json'
+  }
+})
+```
+
+For example, the Form
+
+```html
+<form id="form">
+  <input type="text" name="foo" value="1">
+  <input type="text" name="deep.prop" value="2">
+  <input type="text" name="deep prop spaced" value="3">
+  <input type="text" name="baz" value="4">
+  <input type="text" name="baz" value="5">
+
+  <select name="user.age">
+    <option value="value1">Value 1</option>
+    <option value="value2" selected>Value 2</option>
+    <option value="value3">Value 3</option>
+  </select>
+
+  <input type="submit" value="Save">
+</form>
+```
+
+will be submitted as the following JSON object:
+
+```js
+{
+  "foo": "1",
+  "deep": {
+    "prop": {
+      "spaced": "3"
+    }
+  },
+  "baz": [
+    "4",
+    "5"
+  ],
+  "user": {
+    "age": "value2"
+  }
+}
+````
+
+Sending `Blobs`/`Files` as JSON (`base64`) is not currently supported.
+
+## 🆕 Progress capturing
+
+Axios supports both browser and node environments to capture request upload/download progress.
+The frequency of progress events is forced to be limited to `3` times per second.
+
+```js    
+await axios.post(url, data, {
+  onUploadProgress: function (axiosProgressEvent) {
+    /*{
+      loaded: number;
+      total?: number;
+      progress?: number; // in range [0..1]
+      bytes: number; // how many bytes have been transferred since the last trigger (delta)
+      estimated?: number; // estimated time in seconds
+      rate?: number; // upload speed in bytes
+      upload: true; // upload sign
+    }*/
+  },
+
+  onDownloadProgress: function (axiosProgressEvent) {
+    /*{
+      loaded: number;
+      total?: number;
+      progress?: number;
+      bytes: number; 
+      estimated?: number;
+      rate?: number; // download speed in bytes
+      download: true; // download sign
+    }*/
+  }
+});  
+```
+
+You can also track stream upload/download progress in node.js:
+
+```js
+const {data} = await axios.post(SERVER_URL, readableStream, {
+   onUploadProgress: ({progress}) => {
+     console.log((progress * 100).toFixed(2));
+   },
+  
+   headers: {
+    'Content-Length': contentLength
+   },
+
+   maxRedirects: 0 // avoid buffering the entire stream
+});
+````
+
+> **Note:**
+> Capturing FormData upload progress is not currently supported in node.js environments.
+
+> **⚠️ Warning**
+> It is recommended to disable redirects by setting maxRedirects: 0 to upload the stream in the **node.js** environment,
+> as follow-redirects package will buffer the entire stream in RAM without following the "backpressure" algorithm.
+
+
+## 🆕 Rate limiting
+
+Download and upload rate limits can only be set for the http adapter (node.js):
+
+```js
+const {data} = await axios.post(LOCAL_SERVER_URL, myBuffer, {
+  onUploadProgress: ({progress, rate}) => {
+    console.log(`Upload [${(progress*100).toFixed(2)}%]: ${(rate / 1024).toFixed(2)}KB/s`)
+  },
+   
+  maxRate: [100 * 1024], // 100KB/s limit
+});
+```
+
+## 🆕 AxiosHeaders
+
+Axios has its own `AxiosHeaders` class to manipulate headers using a Map-like API that guarantees caseless work.
+Although HTTP is case-insensitive in headers, Axios will retain the case of the original header for stylistic reasons
+and for a workaround when servers mistakenly consider the header's case.
+The old approach of directly manipulating headers object is still available, but deprecated and not recommended for future usage.
+
+### Working with headers
+
+An AxiosHeaders object instance can contain different types of internal values. that control setting and merging logic.
+The final headers object with string values is obtained by Axios by calling the `toJSON` method.
+
+> Note: By JSON here we mean an object consisting only of string values intended to be sent over the network.
+
+The header value can be one of the following types:
+- `string` - normal string value that will be sent to the server
+- `null` - skip header when rendering to JSON
+- `false` - skip header when rendering to JSON, additionally indicates that `set` method must be called with `rewrite` option set to `true`
+  to overwrite this value (Axios uses this internally to allow users to opt out of installing certain headers like `User-Agent` or `Content-Type`)
+- `undefined` - value is not set
+
+> Note: The header value is considered set if it is not equal to undefined.
+
+The headers object is always initialized inside interceptors and transformers:
+
+```ts
+  axios.interceptors.request.use((request: InternalAxiosRequestConfig) => {
+      request.headers.set('My-header', 'value');
+
+      request.headers.set({
+        "My-set-header1": "my-set-value1",
+        "My-set-header2": "my-set-value2"
+      });
+      
+      request.headers.set('User-Agent', false); // disable subsequent setting the header by Axios
+
+      request.headers.setContentType('text/plain');
+    
+      request.headers['My-set-header2'] = 'newValue' // direct access is deprecated
+    
+      return request;
+    }
+  );
+````
+
+You can iterate over an `AxiosHeaders` instance using a `for...of` statement:
+
+````js
+const headers = new AxiosHeaders({
+  foo: '1',
+  bar: '2',
+  baz: '3'
+});
+
+for(const [header, value] of headers) {
+  console.log(header, value);
+}
+
+// foo 1
+// bar 2
+// baz 3
+````
+
+### new AxiosHeaders(headers?)
+
+Constructs a new `AxiosHeaders` instance. 
+
+```
+constructor(headers?: RawAxiosHeaders | AxiosHeaders | string);
+```
+
+If the headers object is a string, it will be parsed as RAW HTTP headers.
+
+````js
+const headers = new AxiosHeaders(`
+Host: www.bing.com
+User-Agent: curl/7.54.0
+Accept: */*`);
+
+console.log(headers);
+
+// Object [AxiosHeaders] {
+//   host: 'www.bing.com',
+//   'user-agent': 'curl/7.54.0',
+//   accept: '*/*'
+// }
+````
+
+### AxiosHeaders#set
+
+```ts
+set(headerName, value: Axios, rewrite?: boolean);
+set(headerName, value, rewrite?: (this: AxiosHeaders, value: string, name: string, headers: RawAxiosHeaders) => boolean);
+set(headers?: RawAxiosHeaders | AxiosHeaders | string, rewrite?: boolean);
+```
+
+The `rewrite` argument controls the overwriting behavior:
+- `false` - do not overwrite if header's value is set (is not `undefined`)
+- `undefined` (default) - overwrite the header unless its value is set to `false`
+- `true` - rewrite anyway
+
+The option can also accept a user-defined function that determines whether the value should be overwritten or not.
+
+Returns `this`.
+
+### AxiosHeaders#get(header)
+
+```
+  get(headerName: string, matcher?: true | AxiosHeaderMatcher): AxiosHeaderValue;
+  get(headerName: string, parser: RegExp): RegExpExecArray | null;
+````
+
+Returns the internal value of the header. It can take an extra argument to parse the header's value with `RegExp.exec`,
+matcher function or internal key-value parser.
+
+```ts
+const headers = new AxiosHeaders({
+  'Content-Type': 'multipart/form-data; boundary=Asrf456BGe4h'
+});
+
+console.log(headers.get('Content-Type')); 
+// multipart/form-data; boundary=Asrf456BGe4h
+
+console.log(headers.get('Content-Type', true)); // parse key-value pairs from a string separated with \s,;= delimiters:
+// [Object: null prototype] {
+//   'multipart/form-data': undefined,
+//    boundary: 'Asrf456BGe4h'
+// }
+
+
+console.log(headers.get('Content-Type', (value, name, headers) => {
+  return String(value).replace(/a/g, 'ZZZ');
+}));
+// multipZZZrt/form-dZZZtZZZ; boundZZZry=Asrf456BGe4h
+
+console.log(headers.get('Content-Type', /boundary=(\w+)/)?.[0]);
+// boundary=Asrf456BGe4h
+
+```
+
+Returns the value of the header.
+
+### AxiosHeaders#has(header, matcher?)
+
+```
+has(header: string, matcher?: AxiosHeaderMatcher): boolean;
+```
+
+Returns `true` if the header is set (has no `undefined` value).
+
+### AxiosHeaders#delete(header, matcher?)
+
+```
+delete(header: string | string[], matcher?: AxiosHeaderMatcher): boolean;
+```
+
+Returns `true` if at least one header has been removed.
+
+### AxiosHeaders#clear(matcher?)
+
+```
+clear(matcher?: AxiosHeaderMatcher): boolean;
+```
+
+Removes all headers. 
+Unlike the `delete` method matcher, this optional matcher will be used to match against the header name rather than the value.
+
+```ts
+const headers = new AxiosHeaders({
+  'foo': '1',
+  'x-foo': '2',
+  'x-bar': '3',
+});
+
+console.log(headers.clear(/^x-/)); // true
+
+console.log(headers.toJSON()); // [Object: null prototype] { foo: '1' }
+```
+
+Returns `true` if at least one header has been cleared.
+
+### AxiosHeaders#normalize(format);
+
+If the headers object was changed directly, it can have duplicates with the same name but in different cases.
+This method normalizes the headers object by combining duplicate keys into one.
+Axios uses this method internally after calling each interceptor.
+Set `format` to true for converting headers name to lowercase and capitalize the initial letters (`cOntEnt-type` => `Content-Type`)
+
+```js
+const headers = new AxiosHeaders({
+  'foo': '1',
+});
+
+headers.Foo = '2';
+headers.FOO = '3';
+
+console.log(headers.toJSON()); // [Object: null prototype] { foo: '1', Foo: '2', FOO: '3' }
+console.log(headers.normalize().toJSON()); // [Object: null prototype] { foo: '3' }
+console.log(headers.normalize(true).toJSON()); // [Object: null prototype] { Foo: '3' }
+```
+
+Returns `this`.
+
+### AxiosHeaders#concat(...targets)
+
+```
+concat(...targets: Array<AxiosHeaders | RawAxiosHeaders | string | undefined | null>): AxiosHeaders;
+```
+
+Merges the instance with targets into a new `AxiosHeaders` instance. If the target is a string, it will be parsed as RAW HTTP headers.
+
+Returns a new `AxiosHeaders` instance.
+
+### AxiosHeaders#toJSON(asStrings?)
+
+````
+toJSON(asStrings?: boolean): RawAxiosHeaders;
+````
+
+Resolve all internal headers values into a new null prototype object. 
+Set `asStrings` to true to resolve arrays as a string containing all elements, separated by commas.
+
+### AxiosHeaders.from(thing?)
+
+````
+from(thing?: AxiosHeaders | RawAxiosHeaders | string): AxiosHeaders;
+````
+
+Returns a new `AxiosHeaders` instance created from the raw headers passed in,
+or simply returns the given headers object if it's an `AxiosHeaders` instance.
+
+### AxiosHeaders.concat(...targets)
+
+````
+concat(...targets: Array<AxiosHeaders | RawAxiosHeaders | string | undefined | null>): AxiosHeaders;
+````
+
+Returns a new `AxiosHeaders` instance created by merging the target objects.
+
+### Shortcuts
+
+The following shortcuts are available:
+
+- `setContentType`, `getContentType`, `hasContentType`
+
+- `setContentLength`, `getContentLength`, `hasContentLength`
+
+- `setAccept`, `getAccept`, `hasAccept`
+
+- `setUserAgent`, `getUserAgent`, `hasUserAgent`
+
+- `setContentEncoding`, `getContentEncoding`, `hasContentEncoding`
+
+## 🔥 Fetch adapter
+
+Fetch adapter was introduced in `v1.7.0`. By default, it will be used if `xhr` and `http` adapters are not available in the build,
+or not supported by the environment.
+To use it by default, it must be selected explicitly:
+
+```js
+const {data} = axios.get(url, {
+  adapter: 'fetch' // by default ['xhr', 'http', 'fetch']
+})
+```
+
+You can create a separate instance for this:
+
+```js
+const fetchAxios = axios.create({
+  adapter: 'fetch'
+});
+
+const {data} = fetchAxios.get(url);
+```
+
+The adapter supports the same functionality as `xhr` adapter, **including upload and download progress capturing**. 
+Also, it supports additional response types such as `stream` and `formdata` (if supported by the environment).
+
+## Semver
+
+Until axios reaches a `1.0` release, breaking changes will be released with a new minor version. For example `0.5.1`, and `0.5.4` will have the same API, but `0.6.0` will have breaking changes.
+
+## Promises
+
+axios depends on a native ES6 Promise implementation to be [supported](https://caniuse.com/promises).
+If your environment doesn't support ES6 Promises, you can [polyfill](https://github.com/jakearchibald/es6-promise).
+
+## TypeScript
+
+axios includes [TypeScript](https://typescriptlang.org) definitions and a type guard for axios errors.
+
+```typescript
+let user: User = null;
+try {
+  const { data } = await axios.get('/user?ID=12345');
+  user = data.userDetails;
+} catch (error) {
+  if (axios.isAxiosError(error)) {
+    handleAxiosError(error);
+  } else {
+    handleUnexpectedError(error);
+  }
+}
+```
+
+Because axios dual publishes with an ESM default export and a CJS `module.exports`, there are some caveats.
+The recommended setting is to use `"moduleResolution": "node16"` (this is implied by `"module": "node16"`). Note that this requires TypeScript 4.7 or greater.
+If use ESM, your settings should be fine.
+If you compile TypeScript to CJS and you can’t use `"moduleResolution": "node 16"`, you have to enable `esModuleInterop`.
+If you use TypeScript to type check CJS JavaScript code, your only option is to use `"moduleResolution": "node16"`.
+
+## Online one-click setup
+
+You can use Gitpod, an online IDE(which is free for Open Source) for contributing or running the examples online.
+
+[![Open in Gitpod](https://gitpod.io/button/open-in-gitpod.svg)](https://gitpod.io/#https://github.com/axios/axios/blob/main/examples/server.js)
+
+
+## Resources
+
+* [Changelog](https://github.com/axios/axios/blob/v1.x/CHANGELOG.md)
+* [Ecosystem](https://github.com/axios/axios/blob/v1.x/ECOSYSTEM.md)
+* [Contributing Guide](https://github.com/axios/axios/blob/v1.x/CONTRIBUTING.md)
+* [Code of Conduct](https://github.com/axios/axios/blob/v1.x/CODE_OF_CONDUCT.md)
+
+## Credits
+
+axios is heavily inspired by the [$http service](https://docs.angularjs.org/api/ng/service/$http) provided in [AngularJS](https://angularjs.org/). Ultimately axios is an effort to provide a standalone `$http`-like service for use outside of AngularJS.
+
+## License
+
+[MIT](LICENSE)
diff --git a/tortoise/node_modules/axios/SECURITY.md b/tortoise/node_modules/axios/SECURITY.md
new file mode 100644
index 0000000..a5a2b7d
--- /dev/null
+++ b/tortoise/node_modules/axios/SECURITY.md
@@ -0,0 +1,6 @@
+# Reporting a Vulnerability
+
+If you discover a security vulnerability in axios please disclose it via [our huntr page](https://huntr.dev/repos/axios/axios/). Bounty eligibility, CVE assignment, response times and past reports are all there.
+
+
+Thank you for improving the security of axios.
diff --git a/tortoise/node_modules/axios/index.d.cts b/tortoise/node_modules/axios/index.d.cts
new file mode 100644
index 0000000..7d12dd3
--- /dev/null
+++ b/tortoise/node_modules/axios/index.d.cts
@@ -0,0 +1,545 @@
+interface RawAxiosHeaders {
+  [key: string]: axios.AxiosHeaderValue;
+}
+
+type MethodsHeaders = Partial<{
+  [Key in axios.Method as Lowercase<Key>]: AxiosHeaders;
+} & {common: AxiosHeaders}>;
+
+type AxiosHeaderMatcher = (this: AxiosHeaders, value: string, name: string, headers: RawAxiosHeaders) => boolean;
+
+type AxiosHeaderParser = (this: AxiosHeaders, value: axios.AxiosHeaderValue, header: string) => any;
+
+type CommonRequestHeadersList = 'Accept' | 'Content-Length' | 'User-Agent'| 'Content-Encoding' | 'Authorization';
+
+type ContentType = axios.AxiosHeaderValue | 'text/html' | 'text/plain' | 'multipart/form-data' | 'application/json' | 'application/x-www-form-urlencoded' | 'application/octet-stream';
+
+type CommonResponseHeadersList = 'Server' | 'Content-Type' | 'Content-Length' | 'Cache-Control'| 'Content-Encoding';
+
+declare class AxiosHeaders {
+  constructor(
+      headers?: RawAxiosHeaders | AxiosHeaders | string
+  );
+
+  [key: string]: any;
+
+  set(headerName?: string, value?: axios.AxiosHeaderValue, rewrite?: boolean | AxiosHeaderMatcher): AxiosHeaders;
+  set(headers?: RawAxiosHeaders | AxiosHeaders | string, rewrite?: boolean): AxiosHeaders;
+
+  get(headerName: string, parser: RegExp): RegExpExecArray | null;
+  get(headerName: string, matcher?: true | AxiosHeaderParser): axios.AxiosHeaderValue;
+
+  has(header: string, matcher?: AxiosHeaderMatcher): boolean;
+
+  delete(header: string | string[], matcher?: AxiosHeaderMatcher): boolean;
+
+  clear(matcher?: AxiosHeaderMatcher): boolean;
+
+  normalize(format: boolean): AxiosHeaders;
+
+  concat(...targets: Array<AxiosHeaders | RawAxiosHeaders | string | undefined | null>): AxiosHeaders;
+
+  toJSON(asStrings?: boolean): RawAxiosHeaders;
+
+  static from(thing?: AxiosHeaders | RawAxiosHeaders | string): AxiosHeaders;
+
+  static accessor(header: string | string[]): AxiosHeaders;
+
+  static concat(...targets: Array<AxiosHeaders | RawAxiosHeaders | string | undefined | null>): AxiosHeaders;
+
+  setContentType(value: ContentType, rewrite?: boolean | AxiosHeaderMatcher): AxiosHeaders;
+  getContentType(parser?: RegExp): RegExpExecArray | null;
+  getContentType(matcher?: AxiosHeaderMatcher): axios.AxiosHeaderValue;
+  hasContentType(matcher?: AxiosHeaderMatcher): boolean;
+
+  setContentLength(value: axios.AxiosHeaderValue, rewrite?: boolean | AxiosHeaderMatcher): AxiosHeaders;
+  getContentLength(parser?: RegExp): RegExpExecArray | null;
+  getContentLength(matcher?: AxiosHeaderMatcher): axios.AxiosHeaderValue;
+  hasContentLength(matcher?: AxiosHeaderMatcher): boolean;
+
+  setAccept(value: axios.AxiosHeaderValue, rewrite?: boolean | AxiosHeaderMatcher): AxiosHeaders;
+  getAccept(parser?: RegExp): RegExpExecArray | null;
+  getAccept(matcher?: AxiosHeaderMatcher): axios.AxiosHeaderValue;
+  hasAccept(matcher?: AxiosHeaderMatcher): boolean;
+
+  setUserAgent(value: axios.AxiosHeaderValue, rewrite?: boolean | AxiosHeaderMatcher): AxiosHeaders;
+  getUserAgent(parser?: RegExp): RegExpExecArray | null;
+  getUserAgent(matcher?: AxiosHeaderMatcher): axios.AxiosHeaderValue;
+  hasUserAgent(matcher?: AxiosHeaderMatcher): boolean;
+
+  setContentEncoding(value: axios.AxiosHeaderValue, rewrite?: boolean | AxiosHeaderMatcher): AxiosHeaders;
+  getContentEncoding(parser?: RegExp): RegExpExecArray | null;
+  getContentEncoding(matcher?: AxiosHeaderMatcher): axios.AxiosHeaderValue;
+  hasContentEncoding(matcher?: AxiosHeaderMatcher): boolean;
+
+  setAuthorization(value: axios.AxiosHeaderValue, rewrite?: boolean | AxiosHeaderMatcher): AxiosHeaders;
+  getAuthorization(parser?: RegExp): RegExpExecArray | null;
+  getAuthorization(matcher?: AxiosHeaderMatcher): axios.AxiosHeaderValue;
+  hasAuthorization(matcher?: AxiosHeaderMatcher): boolean;
+
+  [Symbol.iterator](): IterableIterator<[string, axios.AxiosHeaderValue]>;
+}
+
+declare class AxiosError<T = unknown, D = any> extends Error {
+  constructor(
+      message?: string,
+      code?: string,
+      config?: axios.InternalAxiosRequestConfig<D>,
+      request?: any,
+      response?: axios.AxiosResponse<T, D>
+  );
+
+  config?: axios.InternalAxiosRequestConfig<D>;
+  code?: string;
+  request?: any;
+  response?: axios.AxiosResponse<T, D>;
+  isAxiosError: boolean;
+  status?: number;
+  toJSON: () => object;
+  cause?: Error;
+  static readonly ERR_FR_TOO_MANY_REDIRECTS = "ERR_FR_TOO_MANY_REDIRECTS";
+  static readonly ERR_BAD_OPTION_VALUE = "ERR_BAD_OPTION_VALUE";
+  static readonly ERR_BAD_OPTION = "ERR_BAD_OPTION";
+  static readonly ERR_NETWORK = "ERR_NETWORK";
+  static readonly ERR_DEPRECATED = "ERR_DEPRECATED";
+  static readonly ERR_BAD_RESPONSE = "ERR_BAD_RESPONSE";
+  static readonly ERR_BAD_REQUEST = "ERR_BAD_REQUEST";
+  static readonly ERR_NOT_SUPPORT = "ERR_NOT_SUPPORT";
+  static readonly ERR_INVALID_URL = "ERR_INVALID_URL";
+  static readonly ERR_CANCELED = "ERR_CANCELED";
+  static readonly ECONNABORTED = "ECONNABORTED";
+  static readonly ETIMEDOUT = "ETIMEDOUT";
+}
+
+declare class CanceledError<T> extends AxiosError<T> {
+}
+
+declare class Axios {
+  constructor(config?: axios.AxiosRequestConfig);
+  defaults: axios.AxiosDefaults;
+  interceptors: {
+    request: axios.AxiosInterceptorManager<axios.InternalAxiosRequestConfig>;
+    response: axios.AxiosInterceptorManager<axios.AxiosResponse>;
+  };
+  getUri(config?: axios.AxiosRequestConfig): string;
+  request<T = any, R = axios.AxiosResponse<T>, D = any>(config: axios.AxiosRequestConfig<D>): Promise<R>;
+  get<T = any, R = axios.AxiosResponse<T>, D = any>(url: string, config?: axios.AxiosRequestConfig<D>): Promise<R>;
+  delete<T = any, R = axios.AxiosResponse<T>, D = any>(url: string, config?: axios.AxiosRequestConfig<D>): Promise<R>;
+  head<T = any, R = axios.AxiosResponse<T>, D = any>(url: string, config?: axios.AxiosRequestConfig<D>): Promise<R>;
+  options<T = any, R = axios.AxiosResponse<T>, D = any>(url: string, config?: axios.AxiosRequestConfig<D>): Promise<R>;
+  post<T = any, R = axios.AxiosResponse<T>, D = any>(url: string, data?: D, config?: axios.AxiosRequestConfig<D>): Promise<R>;
+  put<T = any, R = axios.AxiosResponse<T>, D = any>(url: string, data?: D, config?: axios.AxiosRequestConfig<D>): Promise<R>;
+  patch<T = any, R = axios.AxiosResponse<T>, D = any>(url: string, data?: D, config?: axios.AxiosRequestConfig<D>): Promise<R>;
+  postForm<T = any, R = axios.AxiosResponse<T>, D = any>(url: string, data?: D, config?: axios.AxiosRequestConfig<D>): Promise<R>;
+  putForm<T = any, R = axios.AxiosResponse<T>, D = any>(url: string, data?: D, config?: axios.AxiosRequestConfig<D>): Promise<R>;
+  patchForm<T = any, R = axios.AxiosResponse<T>, D = any>(url: string, data?: D, config?: axios.AxiosRequestConfig<D>): Promise<R>;
+}
+
+declare enum HttpStatusCode {
+  Continue = 100,
+  SwitchingProtocols = 101,
+  Processing = 102,
+  EarlyHints = 103,
+  Ok = 200,
+  Created = 201,
+  Accepted = 202,
+  NonAuthoritativeInformation = 203,
+  NoContent = 204,
+  ResetContent = 205,
+  PartialContent = 206,
+  MultiStatus = 207,
+  AlreadyReported = 208,
+  ImUsed = 226,
+  MultipleChoices = 300,
+  MovedPermanently = 301,
+  Found = 302,
+  SeeOther = 303,
+  NotModified = 304,
+  UseProxy = 305,
+  Unused = 306,
+  TemporaryRedirect = 307,
+  PermanentRedirect = 308,
+  BadRequest = 400,
+  Unauthorized = 401,
+  PaymentRequired = 402,
+  Forbidden = 403,
+  NotFound = 404,
+  MethodNotAllowed = 405,
+  NotAcceptable = 406,
+  ProxyAuthenticationRequired = 407,
+  RequestTimeout = 408,
+  Conflict = 409,
+  Gone = 410,
+  LengthRequired = 411,
+  PreconditionFailed = 412,
+  PayloadTooLarge = 413,
+  UriTooLong = 414,
+  UnsupportedMediaType = 415,
+  RangeNotSatisfiable = 416,
+  ExpectationFailed = 417,
+  ImATeapot = 418,
+  MisdirectedRequest = 421,
+  UnprocessableEntity = 422,
+  Locked = 423,
+  FailedDependency = 424,
+  TooEarly = 425,
+  UpgradeRequired = 426,
+  PreconditionRequired = 428,
+  TooManyRequests = 429,
+  RequestHeaderFieldsTooLarge = 431,
+  UnavailableForLegalReasons = 451,
+  InternalServerError = 500,
+  NotImplemented = 501,
+  BadGateway = 502,
+  ServiceUnavailable = 503,
+  GatewayTimeout = 504,
+  HttpVersionNotSupported = 505,
+  VariantAlsoNegotiates = 506,
+  InsufficientStorage = 507,
+  LoopDetected = 508,
+  NotExtended = 510,
+  NetworkAuthenticationRequired = 511,
+}
+
+type InternalAxiosError<T = unknown, D = any> = AxiosError<T, D>;
+
+declare namespace axios {
+  type AxiosError<T = unknown, D = any> = InternalAxiosError<T, D>;
+
+  type RawAxiosRequestHeaders = Partial<RawAxiosHeaders & {
+    [Key in CommonRequestHeadersList]: AxiosHeaderValue;
+  } & {
+    'Content-Type': ContentType
+  }>;
+
+  type AxiosRequestHeaders = RawAxiosRequestHeaders & AxiosHeaders;
+
+  type AxiosHeaderValue = AxiosHeaders | string | string[] | number | boolean | null;
+
+  type RawCommonResponseHeaders = {
+    [Key in CommonResponseHeadersList]: AxiosHeaderValue;
+  } & {
+    "set-cookie": string[];
+  };
+
+  type RawAxiosResponseHeaders = Partial<RawAxiosHeaders & RawCommonResponseHeaders>;
+
+  type AxiosResponseHeaders = RawAxiosResponseHeaders & AxiosHeaders;
+
+  interface AxiosRequestTransformer {
+    (this: InternalAxiosRequestConfig, data: any, headers: AxiosRequestHeaders): any;
+  }
+
+  interface AxiosResponseTransformer {
+    (this: InternalAxiosRequestConfig, data: any, headers: AxiosResponseHeaders, status?: number): any;
+  }
+
+  interface AxiosAdapter {
+    (config: InternalAxiosRequestConfig): AxiosPromise;
+  }
+
+  interface AxiosBasicCredentials {
+    username: string;
+    password: string;
+  }
+
+  interface AxiosProxyConfig {
+    host: string;
+    port: number;
+    auth?: AxiosBasicCredentials;
+    protocol?: string;
+  }
+
+  type Method =
+    | 'get' | 'GET'
+    | 'delete' | 'DELETE'
+    | 'head' | 'HEAD'
+    | 'options' | 'OPTIONS'
+    | 'post' | 'POST'
+    | 'put' | 'PUT'
+    | 'patch' | 'PATCH'
+    | 'purge' | 'PURGE'
+    | 'link' | 'LINK'
+    | 'unlink' | 'UNLINK';
+
+  type ResponseType =
+    | 'arraybuffer'
+    | 'blob'
+    | 'document'
+    | 'json'
+    | 'text'
+    | 'stream'
+    | 'formdata';
+
+  type responseEncoding =
+    | 'ascii' | 'ASCII'
+    | 'ansi' | 'ANSI'
+    | 'binary' | 'BINARY'
+    | 'base64' | 'BASE64'
+    | 'base64url' | 'BASE64URL'
+    | 'hex' | 'HEX'
+    | 'latin1' | 'LATIN1'
+    | 'ucs-2' | 'UCS-2'
+    | 'ucs2' | 'UCS2'
+    | 'utf-8' | 'UTF-8'
+    | 'utf8' | 'UTF8'
+    | 'utf16le' | 'UTF16LE';
+
+  interface TransitionalOptions {
+    silentJSONParsing?: boolean;
+    forcedJSONParsing?: boolean;
+    clarifyTimeoutError?: boolean;
+  }
+
+  interface GenericAbortSignal {
+    readonly aborted: boolean;
+    onabort?: ((...args: any) => any) | null;
+    addEventListener?: (...args: any) => any;
+    removeEventListener?: (...args: any) => any;
+  }
+
+  interface FormDataVisitorHelpers {
+    defaultVisitor: SerializerVisitor;
+    convertValue: (value: any) => any;
+    isVisitable: (value: any) => boolean;
+  }
+
+  interface SerializerVisitor {
+    (
+        this: GenericFormData,
+        value: any,
+        key: string | number,
+        path: null | Array<string | number>,
+        helpers: FormDataVisitorHelpers
+    ): boolean;
+  }
+
+  interface SerializerOptions {
+    visitor?: SerializerVisitor;
+    dots?: boolean;
+    metaTokens?: boolean;
+    indexes?: boolean | null;
+  }
+
+  // tslint:disable-next-line
+  interface FormSerializerOptions extends SerializerOptions {
+  }
+
+  interface ParamEncoder {
+    (value: any, defaultEncoder: (value: any) => any): any;
+  }
+
+  interface CustomParamsSerializer {
+    (params: Record<string, any>, options?: ParamsSerializerOptions): string;
+  }
+
+  interface ParamsSerializerOptions extends SerializerOptions {
+    encode?: ParamEncoder;
+    serialize?: CustomParamsSerializer;
+  }
+
+  type MaxUploadRate = number;
+
+  type MaxDownloadRate = number;
+
+  type BrowserProgressEvent = any;
+
+  interface AxiosProgressEvent {
+    loaded: number;
+    total?: number;
+    progress?: number;
+    bytes: number;
+    rate?: number;
+    estimated?: number;
+    upload?: boolean;
+    download?: boolean;
+    event?: BrowserProgressEvent;
+    lengthComputable: boolean;
+  }
+
+  type Milliseconds = number;
+
+  type AxiosAdapterName = 'fetch' | 'xhr' | 'http' | string;
+
+  type AxiosAdapterConfig = AxiosAdapter | AxiosAdapterName;
+
+  type AddressFamily = 4 | 6 | undefined;
+
+  interface LookupAddressEntry {
+    address: string;
+    family?: AddressFamily;
+  }
+
+  type LookupAddress = string | LookupAddressEntry;
+
+  interface AxiosRequestConfig<D = any> {
+    url?: string;
+    method?: Method | string;
+    baseURL?: string;
+    transformRequest?: AxiosRequestTransformer | AxiosRequestTransformer[];
+    transformResponse?: AxiosResponseTransformer | AxiosResponseTransformer[];
+    headers?: (RawAxiosRequestHeaders & MethodsHeaders) | AxiosHeaders;
+    params?: any;
+    paramsSerializer?: ParamsSerializerOptions | CustomParamsSerializer;
+    data?: D;
+    timeout?: Milliseconds;
+    timeoutErrorMessage?: string;
+    withCredentials?: boolean;
+    adapter?: AxiosAdapterConfig | AxiosAdapterConfig[];
+    auth?: AxiosBasicCredentials;
+    responseType?: ResponseType;
+    responseEncoding?: responseEncoding | string;
+    xsrfCookieName?: string;
+    xsrfHeaderName?: string;
+    onUploadProgress?: (progressEvent: AxiosProgressEvent) => void;
+    onDownloadProgress?: (progressEvent: AxiosProgressEvent) => void;
+    maxContentLength?: number;
+    validateStatus?: ((status: number) => boolean) | null;
+    maxBodyLength?: number;
+    maxRedirects?: number;
+    maxRate?: number | [MaxUploadRate, MaxDownloadRate];
+    beforeRedirect?: (options: Record<string, any>, responseDetails: {headers: Record<string, string>, statusCode: HttpStatusCode}) => void;
+    socketPath?: string | null;
+    transport?: any;
+    httpAgent?: any;
+    httpsAgent?: any;
+    proxy?: AxiosProxyConfig | false;
+    cancelToken?: CancelToken;
+    decompress?: boolean;
+    transitional?: TransitionalOptions;
+    signal?: GenericAbortSignal;
+    insecureHTTPParser?: boolean;
+    env?: {
+      FormData?: new (...args: any[]) => object;
+    };
+    formSerializer?: FormSerializerOptions;
+    family?: AddressFamily;
+    lookup?: ((hostname: string, options: object, cb: (err: Error | null, address: LookupAddress | LookupAddress[], family?: AddressFamily) => void) => void) |
+        ((hostname: string, options: object) => Promise<[address: LookupAddressEntry | LookupAddressEntry[], family?: AddressFamily] | LookupAddress>);
+    withXSRFToken?: boolean | ((config: InternalAxiosRequestConfig) => boolean | undefined);
+    fetchOptions?: Record<string, any>;
+  }
+
+  // Alias
+  type RawAxiosRequestConfig<D = any> = AxiosRequestConfig<D>;
+
+  interface InternalAxiosRequestConfig<D = any> extends AxiosRequestConfig {
+    headers: AxiosRequestHeaders;
+  }
+
+  interface HeadersDefaults {
+    common: RawAxiosRequestHeaders;
+    delete: RawAxiosRequestHeaders;
+    get: RawAxiosRequestHeaders;
+    head: RawAxiosRequestHeaders;
+    post: RawAxiosRequestHeaders;
+    put: RawAxiosRequestHeaders;
+    patch: RawAxiosRequestHeaders;
+    options?: RawAxiosRequestHeaders;
+    purge?: RawAxiosRequestHeaders;
+    link?: RawAxiosRequestHeaders;
+    unlink?: RawAxiosRequestHeaders;
+  }
+
+  interface AxiosDefaults<D = any> extends Omit<AxiosRequestConfig<D>, 'headers'> {
+    headers: HeadersDefaults;
+  }
+
+  interface CreateAxiosDefaults<D = any> extends Omit<AxiosRequestConfig<D>, 'headers'> {
+    headers?: RawAxiosRequestHeaders | AxiosHeaders | Partial<HeadersDefaults>;
+  }
+
+  interface AxiosResponse<T = any, D = any>  {
+    data: T;
+    status: number;
+    statusText: string;
+    headers: RawAxiosResponseHeaders | AxiosResponseHeaders;
+    config: InternalAxiosRequestConfig<D>;
+    request?: any;
+  }
+
+  type AxiosPromise<T = any> = Promise<AxiosResponse<T>>;
+
+  interface CancelStatic {
+    new (message?: string): Cancel;
+  }
+
+  interface Cancel {
+    message: string | undefined;
+  }
+
+  interface Canceler {
+    (message?: string, config?: AxiosRequestConfig, request?: any): void;
+  }
+
+  interface CancelTokenStatic {
+    new (executor: (cancel: Canceler) => void): CancelToken;
+    source(): CancelTokenSource;
+  }
+
+  interface CancelToken {
+    promise: Promise<Cancel>;
+    reason?: Cancel;
+    throwIfRequested(): void;
+  }
+
+  interface CancelTokenSource {
+    token: CancelToken;
+    cancel: Canceler;
+  }
+
+  interface AxiosInterceptorOptions {
+    synchronous?: boolean;
+    runWhen?: (config: InternalAxiosRequestConfig) => boolean;
+  }
+
+  interface AxiosInterceptorManager<V> {
+    use(onFulfilled?: (value: V) => V | Promise<V>, onRejected?: (error: any) => any, options?: AxiosInterceptorOptions): number;
+    eject(id: number): void;
+    clear(): void;
+  }
+
+  interface AxiosInstance extends Axios {
+    <T = any, R = AxiosResponse<T>, D = any>(config: AxiosRequestConfig<D>): Promise<R>;
+    <T = any, R = AxiosResponse<T>, D = any>(url: string, config?: AxiosRequestConfig<D>): Promise<R>;
+
+    defaults: Omit<AxiosDefaults, 'headers'> & {
+      headers: HeadersDefaults & {
+        [key: string]: AxiosHeaderValue
+      }
+    };
+  }
+
+  interface GenericFormData {
+    append(name: string, value: any, options?: any): any;
+  }
+
+  interface GenericHTMLFormElement {
+    name: string;
+    method: string;
+    submit(): void;
+  }
+
+  interface AxiosStatic extends AxiosInstance {
+    create(config?: CreateAxiosDefaults): AxiosInstance;
+    Cancel: CancelStatic;
+    CancelToken: CancelTokenStatic;
+    Axios: typeof Axios;
+    AxiosError: typeof AxiosError;
+    CanceledError: typeof CanceledError;
+    HttpStatusCode: typeof HttpStatusCode;
+    readonly VERSION: string;
+    isCancel(value: any): value is Cancel;
+    all<T>(values: Array<T | Promise<T>>): Promise<T[]>;
+    spread<T, R>(callback: (...args: T[]) => R): (array: T[]) => R;
+    isAxiosError<T = any, D = any>(payload: any): payload is AxiosError<T, D>;
+    toFormData(sourceObj: object, targetFormData?: GenericFormData, options?: FormSerializerOptions): GenericFormData;
+    formToJSON(form: GenericFormData|GenericHTMLFormElement): object;
+    getAdapter(adapters: AxiosAdapterConfig | AxiosAdapterConfig[] | undefined): AxiosAdapter;
+    AxiosHeaders: typeof AxiosHeaders;
+  }
+}
+
+declare const axios: axios.AxiosStatic;
+
+export = axios;
diff --git a/tortoise/node_modules/axios/index.d.ts b/tortoise/node_modules/axios/index.d.ts
new file mode 100644
index 0000000..dbb7dca
--- /dev/null
+++ b/tortoise/node_modules/axios/index.d.ts
@@ -0,0 +1,562 @@
+// TypeScript Version: 4.7
+export type AxiosHeaderValue = AxiosHeaders | string | string[] | number | boolean | null;
+
+interface RawAxiosHeaders {
+  [key: string]: AxiosHeaderValue;
+}
+
+type MethodsHeaders = Partial<{
+  [Key in Method as Lowercase<Key>]: AxiosHeaders;
+} & {common: AxiosHeaders}>;
+
+type AxiosHeaderMatcher = string | RegExp | ((this: AxiosHeaders, value: string, name: string) => boolean);
+
+type AxiosHeaderParser = (this: AxiosHeaders, value: AxiosHeaderValue, header: string) => any;
+
+export class AxiosHeaders {
+  constructor(
+      headers?: RawAxiosHeaders | AxiosHeaders | string
+  );
+
+  [key: string]: any;
+
+  set(headerName?: string, value?: AxiosHeaderValue, rewrite?: boolean | AxiosHeaderMatcher): AxiosHeaders;
+  set(headers?: RawAxiosHeaders | AxiosHeaders | string, rewrite?: boolean): AxiosHeaders;
+
+  get(headerName: string, parser: RegExp): RegExpExecArray | null;
+  get(headerName: string, matcher?: true | AxiosHeaderParser): AxiosHeaderValue;
+
+  has(header: string, matcher?: AxiosHeaderMatcher): boolean;
+
+  delete(header: string | string[], matcher?: AxiosHeaderMatcher): boolean;
+
+  clear(matcher?: AxiosHeaderMatcher): boolean;
+
+  normalize(format: boolean): AxiosHeaders;
+
+  concat(...targets: Array<AxiosHeaders | RawAxiosHeaders | string | undefined | null>): AxiosHeaders;
+
+  toJSON(asStrings?: boolean): RawAxiosHeaders;
+
+  static from(thing?: AxiosHeaders | RawAxiosHeaders | string): AxiosHeaders;
+
+  static accessor(header: string | string[]): AxiosHeaders;
+
+  static concat(...targets: Array<AxiosHeaders | RawAxiosHeaders | string | undefined | null>): AxiosHeaders;
+
+  setContentType(value: ContentType, rewrite?: boolean | AxiosHeaderMatcher): AxiosHeaders;
+  getContentType(parser?: RegExp): RegExpExecArray | null;
+  getContentType(matcher?: AxiosHeaderMatcher): AxiosHeaderValue;
+  hasContentType(matcher?: AxiosHeaderMatcher): boolean;
+
+  setContentLength(value: AxiosHeaderValue, rewrite?: boolean | AxiosHeaderMatcher): AxiosHeaders;
+  getContentLength(parser?: RegExp): RegExpExecArray | null;
+  getContentLength(matcher?: AxiosHeaderMatcher): AxiosHeaderValue;
+  hasContentLength(matcher?: AxiosHeaderMatcher): boolean;
+
+  setAccept(value: AxiosHeaderValue, rewrite?: boolean | AxiosHeaderMatcher): AxiosHeaders;
+  getAccept(parser?: RegExp): RegExpExecArray | null;
+  getAccept(matcher?: AxiosHeaderMatcher): AxiosHeaderValue;
+  hasAccept(matcher?: AxiosHeaderMatcher): boolean;
+
+  setUserAgent(value: AxiosHeaderValue, rewrite?: boolean | AxiosHeaderMatcher): AxiosHeaders;
+  getUserAgent(parser?: RegExp): RegExpExecArray | null;
+  getUserAgent(matcher?: AxiosHeaderMatcher): AxiosHeaderValue;
+  hasUserAgent(matcher?: AxiosHeaderMatcher): boolean;
+
+  setContentEncoding(value: AxiosHeaderValue, rewrite?: boolean | AxiosHeaderMatcher): AxiosHeaders;
+  getContentEncoding(parser?: RegExp): RegExpExecArray | null;
+  getContentEncoding(matcher?: AxiosHeaderMatcher): AxiosHeaderValue;
+  hasContentEncoding(matcher?: AxiosHeaderMatcher): boolean;
+
+  setAuthorization(value: AxiosHeaderValue, rewrite?: boolean | AxiosHeaderMatcher): AxiosHeaders;
+  getAuthorization(parser?: RegExp): RegExpExecArray | null;
+  getAuthorization(matcher?: AxiosHeaderMatcher): AxiosHeaderValue;
+  hasAuthorization(matcher?: AxiosHeaderMatcher): boolean;
+
+  [Symbol.iterator](): IterableIterator<[string, AxiosHeaderValue]>;
+}
+
+type CommonRequestHeadersList = 'Accept' | 'Content-Length' | 'User-Agent' | 'Content-Encoding' | 'Authorization';
+
+type ContentType = AxiosHeaderValue | 'text/html' | 'text/plain' | 'multipart/form-data' | 'application/json' | 'application/x-www-form-urlencoded' | 'application/octet-stream';
+
+export type RawAxiosRequestHeaders = Partial<RawAxiosHeaders & {
+  [Key in CommonRequestHeadersList]: AxiosHeaderValue;
+} & {
+  'Content-Type': ContentType
+}>;
+
+export type AxiosRequestHeaders = RawAxiosRequestHeaders & AxiosHeaders;
+
+type CommonResponseHeadersList = 'Server' | 'Content-Type' | 'Content-Length' | 'Cache-Control'| 'Content-Encoding';
+
+type RawCommonResponseHeaders = {
+  [Key in CommonResponseHeadersList]: AxiosHeaderValue;
+} & {
+  "set-cookie": string[];
+};
+
+export type RawAxiosResponseHeaders = Partial<RawAxiosHeaders & RawCommonResponseHeaders>;
+
+export type AxiosResponseHeaders = RawAxiosResponseHeaders & AxiosHeaders;
+
+export interface AxiosRequestTransformer {
+  (this: InternalAxiosRequestConfig, data: any, headers: AxiosRequestHeaders): any;
+}
+
+export interface AxiosResponseTransformer {
+  (this: InternalAxiosRequestConfig, data: any, headers: AxiosResponseHeaders, status?: number): any;
+}
+
+export interface AxiosAdapter {
+  (config: InternalAxiosRequestConfig): AxiosPromise;
+}
+
+export interface AxiosBasicCredentials {
+  username: string;
+  password: string;
+}
+
+export interface AxiosProxyConfig {
+  host: string;
+  port: number;
+  auth?: AxiosBasicCredentials;
+  protocol?: string;
+}
+
+export enum HttpStatusCode {
+  Continue = 100,
+  SwitchingProtocols = 101,
+  Processing = 102,
+  EarlyHints = 103,
+  Ok = 200,
+  Created = 201,
+  Accepted = 202,
+  NonAuthoritativeInformation = 203,
+  NoContent = 204,
+  ResetContent = 205,
+  PartialContent = 206,
+  MultiStatus = 207,
+  AlreadyReported = 208,
+  ImUsed = 226,
+  MultipleChoices = 300,
+  MovedPermanently = 301,
+  Found = 302,
+  SeeOther = 303,
+  NotModified = 304,
+  UseProxy = 305,
+  Unused = 306,
+  TemporaryRedirect = 307,
+  PermanentRedirect = 308,
+  BadRequest = 400,
+  Unauthorized = 401,
+  PaymentRequired = 402,
+  Forbidden = 403,
+  NotFound = 404,
+  MethodNotAllowed = 405,
+  NotAcceptable = 406,
+  ProxyAuthenticationRequired = 407,
+  RequestTimeout = 408,
+  Conflict = 409,
+  Gone = 410,
+  LengthRequired = 411,
+  PreconditionFailed = 412,
+  PayloadTooLarge = 413,
+  UriTooLong = 414,
+  UnsupportedMediaType = 415,
+  RangeNotSatisfiable = 416,
+  ExpectationFailed = 417,
+  ImATeapot = 418,
+  MisdirectedRequest = 421,
+  UnprocessableEntity = 422,
+  Locked = 423,
+  FailedDependency = 424,
+  TooEarly = 425,
+  UpgradeRequired = 426,
+  PreconditionRequired = 428,
+  TooManyRequests = 429,
+  RequestHeaderFieldsTooLarge = 431,
+  UnavailableForLegalReasons = 451,
+  InternalServerError = 500,
+  NotImplemented = 501,
+  BadGateway = 502,
+  ServiceUnavailable = 503,
+  GatewayTimeout = 504,
+  HttpVersionNotSupported = 505,
+  VariantAlsoNegotiates = 506,
+  InsufficientStorage = 507,
+  LoopDetected = 508,
+  NotExtended = 510,
+  NetworkAuthenticationRequired = 511,
+}
+
+export type Method =
+    | 'get' | 'GET'
+    | 'delete' | 'DELETE'
+    | 'head' | 'HEAD'
+    | 'options' | 'OPTIONS'
+    | 'post' | 'POST'
+    | 'put' | 'PUT'
+    | 'patch' | 'PATCH'
+    | 'purge' | 'PURGE'
+    | 'link' | 'LINK'
+    | 'unlink' | 'UNLINK';
+
+export type ResponseType =
+    | 'arraybuffer'
+    | 'blob'
+    | 'document'
+    | 'json'
+    | 'text'
+    | 'stream'
+    | 'formdata';
+
+export type responseEncoding =
+    | 'ascii' | 'ASCII'
+    | 'ansi' | 'ANSI'
+    | 'binary' | 'BINARY'
+    | 'base64' | 'BASE64'
+    | 'base64url' | 'BASE64URL'
+    | 'hex' | 'HEX'
+    | 'latin1' | 'LATIN1'
+    | 'ucs-2' | 'UCS-2'
+    | 'ucs2' | 'UCS2'
+    | 'utf-8' | 'UTF-8'
+    | 'utf8' | 'UTF8'
+    | 'utf16le' | 'UTF16LE';
+
+export interface TransitionalOptions {
+  silentJSONParsing?: boolean;
+  forcedJSONParsing?: boolean;
+  clarifyTimeoutError?: boolean;
+}
+
+export interface GenericAbortSignal {
+  readonly aborted: boolean;
+  onabort?: ((...args: any) => any) | null;
+  addEventListener?: (...args: any) => any;
+  removeEventListener?: (...args: any) => any;
+}
+
+export interface FormDataVisitorHelpers {
+  defaultVisitor: SerializerVisitor;
+  convertValue: (value: any) => any;
+  isVisitable: (value: any) => boolean;
+}
+
+export interface SerializerVisitor {
+  (
+      this: GenericFormData,
+      value: any,
+      key: string | number,
+      path: null | Array<string | number>,
+      helpers: FormDataVisitorHelpers
+  ): boolean;
+}
+
+export interface SerializerOptions {
+  visitor?: SerializerVisitor;
+  dots?: boolean;
+  metaTokens?: boolean;
+  indexes?: boolean | null;
+}
+
+// tslint:disable-next-line
+export interface FormSerializerOptions extends SerializerOptions {
+}
+
+export interface ParamEncoder {
+  (value: any, defaultEncoder: (value: any) => any): any;
+}
+
+export interface CustomParamsSerializer {
+  (params: Record<string, any>, options?: ParamsSerializerOptions): string;
+}
+
+export interface ParamsSerializerOptions extends SerializerOptions {
+  encode?: ParamEncoder;
+  serialize?: CustomParamsSerializer;
+}
+
+type MaxUploadRate = number;
+
+type MaxDownloadRate = number;
+
+type BrowserProgressEvent = any;
+
+export interface AxiosProgressEvent {
+  loaded: number;
+  total?: number;
+  progress?: number;
+  bytes: number;
+  rate?: number;
+  estimated?: number;
+  upload?: boolean;
+  download?: boolean;
+  event?: BrowserProgressEvent;
+  lengthComputable: boolean;
+}
+
+type Milliseconds = number;
+
+type AxiosAdapterName = 'fetch' | 'xhr' | 'http' | string;
+
+type AxiosAdapterConfig = AxiosAdapter | AxiosAdapterName;
+
+export type AddressFamily = 4 | 6 | undefined;
+
+export interface LookupAddressEntry {
+  address: string;
+  family?: AddressFamily;
+}
+
+export type LookupAddress = string | LookupAddressEntry;
+
+export interface AxiosRequestConfig<D = any> {
+  url?: string;
+  method?: Method | string;
+  baseURL?: string;
+  transformRequest?: AxiosRequestTransformer | AxiosRequestTransformer[];
+  transformResponse?: AxiosResponseTransformer | AxiosResponseTransformer[];
+  headers?: (RawAxiosRequestHeaders & MethodsHeaders) | AxiosHeaders;
+  params?: any;
+  paramsSerializer?: ParamsSerializerOptions | CustomParamsSerializer;
+  data?: D;
+  timeout?: Milliseconds;
+  timeoutErrorMessage?: string;
+  withCredentials?: boolean;
+  adapter?: AxiosAdapterConfig | AxiosAdapterConfig[];
+  auth?: AxiosBasicCredentials;
+  responseType?: ResponseType;
+  responseEncoding?: responseEncoding | string;
+  xsrfCookieName?: string;
+  xsrfHeaderName?: string;
+  onUploadProgress?: (progressEvent: AxiosProgressEvent) => void;
+  onDownloadProgress?: (progressEvent: AxiosProgressEvent) => void;
+  maxContentLength?: number;
+  validateStatus?: ((status: number) => boolean) | null;
+  maxBodyLength?: number;
+  maxRedirects?: number;
+  maxRate?: number | [MaxUploadRate, MaxDownloadRate];
+  beforeRedirect?: (options: Record<string, any>, responseDetails: {headers: Record<string, string>, statusCode: HttpStatusCode}) => void;
+  socketPath?: string | null;
+  transport?: any;
+  httpAgent?: any;
+  httpsAgent?: any;
+  proxy?: AxiosProxyConfig | false;
+  cancelToken?: CancelToken;
+  decompress?: boolean;
+  transitional?: TransitionalOptions;
+  signal?: GenericAbortSignal;
+  insecureHTTPParser?: boolean;
+  env?: {
+    FormData?: new (...args: any[]) => object;
+  };
+  formSerializer?: FormSerializerOptions;
+  family?: AddressFamily;
+  lookup?: ((hostname: string, options: object, cb: (err: Error | null, address: LookupAddress | LookupAddress[], family?: AddressFamily) => void) => void) |
+      ((hostname: string, options: object) => Promise<[address: LookupAddressEntry | LookupAddressEntry[], family?: AddressFamily] | LookupAddress>);
+  withXSRFToken?: boolean | ((config: InternalAxiosRequestConfig) => boolean | undefined);
+  fetchOptions?: Record<string, any>;
+}
+
+// Alias
+export type RawAxiosRequestConfig<D = any> = AxiosRequestConfig<D>;
+
+export interface InternalAxiosRequestConfig<D = any> extends AxiosRequestConfig<D> {
+  headers: AxiosRequestHeaders;
+}
+
+export interface HeadersDefaults {
+  common: RawAxiosRequestHeaders;
+  delete: RawAxiosRequestHeaders;
+  get: RawAxiosRequestHeaders;
+  head: RawAxiosRequestHeaders;
+  post: RawAxiosRequestHeaders;
+  put: RawAxiosRequestHeaders;
+  patch: RawAxiosRequestHeaders;
+  options?: RawAxiosRequestHeaders;
+  purge?: RawAxiosRequestHeaders;
+  link?: RawAxiosRequestHeaders;
+  unlink?: RawAxiosRequestHeaders;
+}
+
+export interface AxiosDefaults<D = any> extends Omit<AxiosRequestConfig<D>, 'headers'> {
+  headers: HeadersDefaults;
+}
+
+export interface CreateAxiosDefaults<D = any> extends Omit<AxiosRequestConfig<D>, 'headers'> {
+  headers?: RawAxiosRequestHeaders | AxiosHeaders | Partial<HeadersDefaults>;
+}
+
+export interface AxiosResponse<T = any, D = any> {
+  data: T;
+  status: number;
+  statusText: string;
+  headers: RawAxiosResponseHeaders | AxiosResponseHeaders;
+  config: InternalAxiosRequestConfig<D>;
+  request?: any;
+}
+
+export class AxiosError<T = unknown, D = any> extends Error {
+  constructor(
+      message?: string,
+      code?: string,
+      config?: InternalAxiosRequestConfig<D>,
+      request?: any,
+      response?: AxiosResponse<T, D>
+  );
+
+  config?: InternalAxiosRequestConfig<D>;
+  code?: string;
+  request?: any;
+  response?: AxiosResponse<T, D>;
+  isAxiosError: boolean;
+  status?: number;
+  toJSON: () => object;
+  cause?: Error;
+  static from<T = unknown, D = any>(
+    error: Error | unknown,
+    code?: string,
+    config?: InternalAxiosRequestConfig<D>,
+    request?: any,
+    response?: AxiosResponse<T, D>,
+    customProps?: object,
+): AxiosError<T, D>;
+  static readonly ERR_FR_TOO_MANY_REDIRECTS = "ERR_FR_TOO_MANY_REDIRECTS";
+  static readonly ERR_BAD_OPTION_VALUE = "ERR_BAD_OPTION_VALUE";
+  static readonly ERR_BAD_OPTION = "ERR_BAD_OPTION";
+  static readonly ERR_NETWORK = "ERR_NETWORK";
+  static readonly ERR_DEPRECATED = "ERR_DEPRECATED";
+  static readonly ERR_BAD_RESPONSE = "ERR_BAD_RESPONSE";
+  static readonly ERR_BAD_REQUEST = "ERR_BAD_REQUEST";
+  static readonly ERR_NOT_SUPPORT = "ERR_NOT_SUPPORT";
+  static readonly ERR_INVALID_URL = "ERR_INVALID_URL";
+  static readonly ERR_CANCELED = "ERR_CANCELED";
+  static readonly ECONNABORTED = "ECONNABORTED";
+  static readonly ETIMEDOUT = "ETIMEDOUT";
+}
+
+export class CanceledError<T> extends AxiosError<T> {
+}
+
+export type AxiosPromise<T = any> = Promise<AxiosResponse<T>>;
+
+export interface CancelStatic {
+  new (message?: string): Cancel;
+}
+
+export interface Cancel {
+  message: string | undefined;
+}
+
+export interface Canceler {
+  (message?: string, config?: AxiosRequestConfig, request?: any): void;
+}
+
+export interface CancelTokenStatic {
+  new (executor: (cancel: Canceler) => void): CancelToken;
+  source(): CancelTokenSource;
+}
+
+export interface CancelToken {
+  promise: Promise<Cancel>;
+  reason?: Cancel;
+  throwIfRequested(): void;
+}
+
+export interface CancelTokenSource {
+  token: CancelToken;
+  cancel: Canceler;
+}
+
+export interface AxiosInterceptorOptions {
+  synchronous?: boolean;
+  runWhen?: (config: InternalAxiosRequestConfig) => boolean;
+}
+
+export interface AxiosInterceptorManager<V> {
+  use(onFulfilled?: ((value: V) => V | Promise<V>) | null, onRejected?: ((error: any) => any) | null, options?: AxiosInterceptorOptions): number;
+  eject(id: number): void;
+  clear(): void;
+}
+
+export class Axios {
+  constructor(config?: AxiosRequestConfig);
+  defaults: AxiosDefaults;
+  interceptors: {
+    request: AxiosInterceptorManager<InternalAxiosRequestConfig>;
+    response: AxiosInterceptorManager<AxiosResponse>;
+  };
+  getUri(config?: AxiosRequestConfig): string;
+  request<T = any, R = AxiosResponse<T>, D = any>(config: AxiosRequestConfig<D>): Promise<R>;
+  get<T = any, R = AxiosResponse<T>, D = any>(url: string, config?: AxiosRequestConfig<D>): Promise<R>;
+  delete<T = any, R = AxiosResponse<T>, D = any>(url: string, config?: AxiosRequestConfig<D>): Promise<R>;
+  head<T = any, R = AxiosResponse<T>, D = any>(url: string, config?: AxiosRequestConfig<D>): Promise<R>;
+  options<T = any, R = AxiosResponse<T>, D = any>(url: string, config?: AxiosRequestConfig<D>): Promise<R>;
+  post<T = any, R = AxiosResponse<T>, D = any>(url: string, data?: D, config?: AxiosRequestConfig<D>): Promise<R>;
+  put<T = any, R = AxiosResponse<T>, D = any>(url: string, data?: D, config?: AxiosRequestConfig<D>): Promise<R>;
+  patch<T = any, R = AxiosResponse<T>, D = any>(url: string, data?: D, config?: AxiosRequestConfig<D>): Promise<R>;
+  postForm<T = any, R = AxiosResponse<T>, D = any>(url: string, data?: D, config?: AxiosRequestConfig<D>): Promise<R>;
+  putForm<T = any, R = AxiosResponse<T>, D = any>(url: string, data?: D, config?: AxiosRequestConfig<D>): Promise<R>;
+  patchForm<T = any, R = AxiosResponse<T>, D = any>(url: string, data?: D, config?: AxiosRequestConfig<D>): Promise<R>;
+}
+
+export interface AxiosInstance extends Axios {
+  <T = any, R = AxiosResponse<T>, D = any>(config: AxiosRequestConfig<D>): Promise<R>;
+  <T = any, R = AxiosResponse<T>, D = any>(url: string, config?: AxiosRequestConfig<D>): Promise<R>;
+
+  defaults: Omit<AxiosDefaults, 'headers'> & {
+    headers: HeadersDefaults & {
+      [key: string]: AxiosHeaderValue
+    }
+  };
+}
+
+export interface GenericFormData {
+  append(name: string, value: any, options?: any): any;
+}
+
+export interface GenericHTMLFormElement {
+  name: string;
+  method: string;
+  submit(): void;
+}
+
+export function getAdapter(adapters: AxiosAdapterConfig | AxiosAdapterConfig[] | undefined): AxiosAdapter;
+
+export function toFormData(sourceObj: object, targetFormData?: GenericFormData, options?: FormSerializerOptions): GenericFormData;
+
+export function formToJSON(form: GenericFormData|GenericHTMLFormElement): object;
+
+export function isAxiosError<T = any, D = any>(payload: any): payload is AxiosError<T, D>;
+
+export function spread<T, R>(callback: (...args: T[]) => R): (array: T[]) => R;
+
+export function isCancel(value: any): value is Cancel;
+
+export function all<T>(values: Array<T | Promise<T>>): Promise<T[]>;
+
+export interface AxiosStatic extends AxiosInstance {
+  create(config?: CreateAxiosDefaults): AxiosInstance;
+  Cancel: CancelStatic;
+  CancelToken: CancelTokenStatic;
+  Axios: typeof Axios;
+  AxiosError: typeof AxiosError;
+  HttpStatusCode: typeof HttpStatusCode;
+  readonly VERSION: string;
+  isCancel: typeof isCancel;
+  all: typeof all;
+  spread: typeof spread;
+  isAxiosError: typeof isAxiosError;
+  toFormData: typeof toFormData;
+  formToJSON: typeof formToJSON;
+  getAdapter: typeof getAdapter;
+  CanceledError: typeof CanceledError;
+  AxiosHeaders: typeof AxiosHeaders;
+}
+
+declare const axios: AxiosStatic;
+
+export default axios;
diff --git a/tortoise/node_modules/axios/index.js b/tortoise/node_modules/axios/index.js
new file mode 100644
index 0000000..fba3990
--- /dev/null
+++ b/tortoise/node_modules/axios/index.js
@@ -0,0 +1,43 @@
+import axios from './lib/axios.js';
+
+// This module is intended to unwrap Axios default export as named.
+// Keep top-level export same with static properties
+// so that it can keep same with es module or cjs
+const {
+  Axios,
+  AxiosError,
+  CanceledError,
+  isCancel,
+  CancelToken,
+  VERSION,
+  all,
+  Cancel,
+  isAxiosError,
+  spread,
+  toFormData,
+  AxiosHeaders,
+  HttpStatusCode,
+  formToJSON,
+  getAdapter,
+  mergeConfig
+} = axios;
+
+export {
+  axios as default,
+  Axios,
+  AxiosError,
+  CanceledError,
+  isCancel,
+  CancelToken,
+  VERSION,
+  all,
+  Cancel,
+  isAxiosError,
+  spread,
+  toFormData,
+  AxiosHeaders,
+  HttpStatusCode,
+  formToJSON,
+  getAdapter,
+  mergeConfig
+}
diff --git a/tortoise/node_modules/axios/package.json b/tortoise/node_modules/axios/package.json
new file mode 100644
index 0000000..a759d02
--- /dev/null
+++ b/tortoise/node_modules/axios/package.json
@@ -0,0 +1,219 @@
+{
+  "name": "axios",
+  "version": "1.7.7",
+  "description": "Promise based HTTP client for the browser and node.js",
+  "main": "index.js",
+  "exports": {
+    ".": {
+      "types": {
+        "require": "./index.d.cts",
+        "default": "./index.d.ts"
+      },
+      "browser": {
+        "require": "./dist/browser/axios.cjs",
+        "default": "./index.js"
+      },
+      "default": {
+        "require": "./dist/node/axios.cjs",
+        "default": "./index.js"
+      }
+    },
+    "./lib/adapters/http.js": "./lib/adapters/http.js",
+    "./lib/adapters/xhr.js": "./lib/adapters/xhr.js",
+    "./unsafe/*": "./lib/*",
+    "./unsafe/core/settle.js": "./lib/core/settle.js",
+    "./unsafe/core/buildFullPath.js": "./lib/core/buildFullPath.js",
+    "./unsafe/helpers/isAbsoluteURL.js": "./lib/helpers/isAbsoluteURL.js",
+    "./unsafe/helpers/buildURL.js": "./lib/helpers/buildURL.js",
+    "./unsafe/helpers/combineURLs.js": "./lib/helpers/combineURLs.js",
+    "./unsafe/adapters/http.js": "./lib/adapters/http.js",
+    "./unsafe/adapters/xhr.js": "./lib/adapters/xhr.js",
+    "./unsafe/utils.js": "./lib/utils.js",
+    "./package.json": "./package.json"
+  },
+  "type": "module",
+  "types": "index.d.ts",
+  "scripts": {
+    "test": "npm run test:eslint && npm run test:mocha && npm run test:karma && npm run test:dtslint && npm run test:exports",
+    "test:eslint": "node bin/ssl_hotfix.js eslint lib/**/*.js",
+    "test:dtslint": "dtslint --localTs node_modules/typescript/lib",
+    "test:mocha": "node bin/ssl_hotfix.js mocha test/unit/**/*.js --timeout 30000 --exit",
+    "test:exports": "node bin/ssl_hotfix.js mocha test/module/test.js --timeout 30000 --exit",
+    "test:karma": "node bin/ssl_hotfix.js cross-env LISTEN_ADDR=:: karma start karma.conf.cjs --single-run",
+    "test:karma:firefox": "node bin/ssl_hotfix.js cross-env LISTEN_ADDR=:: Browsers=Firefox karma start karma.conf.cjs --single-run",
+    "test:karma:server": "node bin/ssl_hotfix.js cross-env karma start karma.conf.cjs",
+    "test:build:version": "node ./bin/check-build-version.js",
+    "start": "node ./sandbox/server.js",
+    "preversion": "gulp version",
+    "version": "npm run build && git add dist && git add package.json",
+    "prepublishOnly": "npm run test:build:version",
+    "postpublish": "git push && git push --tags",
+    "build": "gulp clear && cross-env NODE_ENV=production rollup -c -m",
+    "examples": "node ./examples/server.js",
+    "coveralls": "cat coverage/lcov.info | ./node_modules/coveralls/bin/coveralls.js",
+    "fix": "eslint --fix lib/**/*.js",
+    "prepare": "husky install && npm run prepare:hooks",
+    "prepare:hooks": "npx husky set .husky/commit-msg \"npx commitlint --edit $1\"",
+    "release:dry": "release-it --dry-run --no-npm",
+    "release:info": "release-it --release-version",
+    "release:beta:no-npm": "release-it --preRelease=beta --no-npm",
+    "release:beta": "release-it --preRelease=beta",
+    "release:no-npm": "release-it --no-npm",
+    "release:changelog:fix": "node ./bin/injectContributorsList.js && git add CHANGELOG.md",
+    "release": "release-it"
+  },
+  "repository": {
+    "type": "git",
+    "url": "https://github.com/axios/axios.git"
+  },
+  "keywords": [
+    "xhr",
+    "http",
+    "ajax",
+    "promise",
+    "node"
+  ],
+  "author": "Matt Zabriskie",
+  "license": "MIT",
+  "bugs": {
+    "url": "https://github.com/axios/axios/issues"
+  },
+  "homepage": "https://axios-http.com",
+  "devDependencies": {
+    "@babel/core": "^7.23.9",
+    "@babel/preset-env": "^7.23.9",
+    "@commitlint/cli": "^17.8.1",
+    "@commitlint/config-conventional": "^17.8.1",
+    "@release-it/conventional-changelog": "^5.1.1",
+    "@rollup/plugin-babel": "^5.3.1",
+    "@rollup/plugin-commonjs": "^15.1.0",
+    "@rollup/plugin-json": "^4.1.0",
+    "@rollup/plugin-multi-entry": "^4.1.0",
+    "@rollup/plugin-node-resolve": "^9.0.0",
+    "abortcontroller-polyfill": "^1.7.5",
+    "auto-changelog": "^2.4.0",
+    "body-parser": "^1.20.2",
+    "chalk": "^5.3.0",
+    "coveralls": "^3.1.1",
+    "cross-env": "^7.0.3",
+    "dev-null": "^0.1.1",
+    "dtslint": "^4.2.1",
+    "es6-promise": "^4.2.8",
+    "eslint": "^8.56.0",
+    "express": "^4.18.2",
+    "formdata-node": "^5.0.1",
+    "formidable": "^2.1.2",
+    "fs-extra": "^10.1.0",
+    "get-stream": "^3.0.0",
+    "gulp": "^4.0.2",
+    "gzip-size": "^7.0.0",
+    "handlebars": "^4.7.8",
+    "husky": "^8.0.3",
+    "istanbul-instrumenter-loader": "^3.0.1",
+    "jasmine-core": "^2.99.1",
+    "karma": "^6.3.17",
+    "karma-chrome-launcher": "^3.2.0",
+    "karma-firefox-launcher": "^2.1.2",
+    "karma-jasmine": "^1.1.2",
+    "karma-jasmine-ajax": "^0.1.13",
+    "karma-rollup-preprocessor": "^7.0.8",
+    "karma-safari-launcher": "^1.0.0",
+    "karma-sauce-launcher": "^4.3.6",
+    "karma-sinon": "^1.0.5",
+    "karma-sourcemap-loader": "^0.3.8",
+    "memoizee": "^0.4.15",
+    "minimist": "^1.2.8",
+    "mocha": "^10.3.0",
+    "multer": "^1.4.4",
+    "pretty-bytes": "^6.1.1",
+    "release-it": "^15.11.0",
+    "rollup": "^2.79.1",
+    "rollup-plugin-auto-external": "^2.0.0",
+    "rollup-plugin-bundle-size": "^1.0.3",
+    "rollup-plugin-terser": "^7.0.2",
+    "sinon": "^4.5.0",
+    "stream-throttle": "^0.1.3",
+    "string-replace-async": "^3.0.2",
+    "terser-webpack-plugin": "^4.2.3",
+    "typescript": "^4.9.5",
+    "@rollup/plugin-alias": "^5.1.0"
+  },
+  "browser": {
+    "./lib/adapters/http.js": "./lib/helpers/null.js",
+    "./lib/platform/node/index.js": "./lib/platform/browser/index.js",
+    "./lib/platform/node/classes/FormData.js": "./lib/helpers/null.js"
+  },
+  "jsdelivr": "dist/axios.min.js",
+  "unpkg": "dist/axios.min.js",
+  "typings": "./index.d.ts",
+  "dependencies": {
+    "follow-redirects": "^1.15.6",
+    "form-data": "^4.0.0",
+    "proxy-from-env": "^1.1.0"
+  },
+  "bundlesize": [
+    {
+      "path": "./dist/axios.min.js",
+      "threshold": "5kB"
+    }
+  ],
+  "contributors": [
+    "Matt Zabriskie (https://github.com/mzabriskie)",
+    "Nick Uraltsev (https://github.com/nickuraltsev)",
+    "Dmitriy Mozgovoy (https://github.com/DigitalBrainJS)",
+    "Jay (https://github.com/jasonsaayman)",
+    "Emily Morehouse (https://github.com/emilyemorehouse)",
+    "Rubén Norte (https://github.com/rubennorte)",
+    "Justin Beckwith (https://github.com/JustinBeckwith)",
+    "Martti Laine (https://github.com/codeclown)",
+    "Xianming Zhong (https://github.com/chinesedfan)",
+    "Rikki Gibson (https://github.com/RikkiGibson)",
+    "Remco Haszing (https://github.com/remcohaszing)",
+    "Yasu Flores (https://github.com/yasuf)",
+    "Ben Carp (https://github.com/carpben)"
+  ],
+  "sideEffects": false,
+  "release-it": {
+    "git": {
+      "commitMessage": "chore(release): v${version}",
+      "push": true,
+      "commit": true,
+      "tag": true,
+      "requireCommits": false,
+      "requireCleanWorkingDir": false
+    },
+    "github": {
+      "release": true,
+      "draft": true
+    },
+    "npm": {
+      "publish": false,
+      "ignoreVersion": false
+    },
+    "plugins": {
+      "@release-it/conventional-changelog": {
+        "preset": "angular",
+        "infile": "CHANGELOG.md",
+        "header": "# Changelog"
+      }
+    },
+    "hooks": {
+      "before:init": "npm test",
+      "after:bump": "gulp version --bump ${version} && npm run build && npm run test:build:version && git add ./dist && git add ./package-lock.json",
+      "before:release": "npm run release:changelog:fix",
+      "after:release": "echo Successfully released ${name} v${version} to ${repo.repository}."
+    }
+  },
+  "commitlint": {
+    "rules": {
+      "header-max-length": [
+        2,
+        "always",
+        130
+      ]
+    },
+    "extends": [
+      "@commitlint/config-conventional"
+    ]
+  }
+}
\ No newline at end of file
diff --git a/tortoise/node_modules/combined-stream/License b/tortoise/node_modules/combined-stream/License
new file mode 100644
index 0000000..4804b7a
--- /dev/null
+++ b/tortoise/node_modules/combined-stream/License
@@ -0,0 +1,19 @@
+Copyright (c) 2011 Debuggable Limited <felix@debuggable.com>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
diff --git a/tortoise/node_modules/combined-stream/Readme.md b/tortoise/node_modules/combined-stream/Readme.md
new file mode 100644
index 0000000..9e367b5
--- /dev/null
+++ b/tortoise/node_modules/combined-stream/Readme.md
@@ -0,0 +1,138 @@
+# combined-stream
+
+A stream that emits multiple other streams one after another.
+
+**NB** Currently `combined-stream` works with streams version 1 only. There is ongoing effort to switch this library to streams version 2. Any help is welcome. :) Meanwhile you can explore other libraries that provide streams2 support with more or less compatibility with `combined-stream`.
+
+- [combined-stream2](https://www.npmjs.com/package/combined-stream2): A drop-in streams2-compatible replacement for the combined-stream module.
+
+- [multistream](https://www.npmjs.com/package/multistream): A stream that emits multiple other streams one after another.
+
+## Installation
+
+``` bash
+npm install combined-stream
+```
+
+## Usage
+
+Here is a simple example that shows how you can use combined-stream to combine
+two files into one:
+
+``` javascript
+var CombinedStream = require('combined-stream');
+var fs = require('fs');
+
+var combinedStream = CombinedStream.create();
+combinedStream.append(fs.createReadStream('file1.txt'));
+combinedStream.append(fs.createReadStream('file2.txt'));
+
+combinedStream.pipe(fs.createWriteStream('combined.txt'));
+```
+
+While the example above works great, it will pause all source streams until
+they are needed. If you don't want that to happen, you can set `pauseStreams`
+to `false`:
+
+``` javascript
+var CombinedStream = require('combined-stream');
+var fs = require('fs');
+
+var combinedStream = CombinedStream.create({pauseStreams: false});
+combinedStream.append(fs.createReadStream('file1.txt'));
+combinedStream.append(fs.createReadStream('file2.txt'));
+
+combinedStream.pipe(fs.createWriteStream('combined.txt'));
+```
+
+However, what if you don't have all the source streams yet, or you don't want
+to allocate the resources (file descriptors, memory, etc.) for them right away?
+Well, in that case you can simply provide a callback that supplies the stream
+by calling a `next()` function:
+
+``` javascript
+var CombinedStream = require('combined-stream');
+var fs = require('fs');
+
+var combinedStream = CombinedStream.create();
+combinedStream.append(function(next) {
+  next(fs.createReadStream('file1.txt'));
+});
+combinedStream.append(function(next) {
+  next(fs.createReadStream('file2.txt'));
+});
+
+combinedStream.pipe(fs.createWriteStream('combined.txt'));
+```
+
+## API
+
+### CombinedStream.create([options])
+
+Returns a new combined stream object. Available options are:
+
+* `maxDataSize`
+* `pauseStreams`
+
+The effect of those options is described below.
+
+### combinedStream.pauseStreams = `true`
+
+Whether to apply back pressure to the underlaying streams. If set to `false`,
+the underlaying streams will never be paused. If set to `true`, the
+underlaying streams will be paused right after being appended, as well as when
+`delayedStream.pipe()` wants to throttle.
+
+### combinedStream.maxDataSize = `2 * 1024 * 1024`
+
+The maximum amount of bytes (or characters) to buffer for all source streams.
+If this value is exceeded, `combinedStream` emits an `'error'` event.
+
+### combinedStream.dataSize = `0`
+
+The amount of bytes (or characters) currently buffered by `combinedStream`.
+
+### combinedStream.append(stream)
+
+Appends the given `stream` to the combinedStream object. If `pauseStreams` is
+set to `true, this stream will also be paused right away.
+
+`streams` can also be a function that takes one parameter called `next`. `next`
+is a function that must be invoked in order to provide the `next` stream, see
+example above.
+
+Regardless of how the `stream` is appended, combined-stream always attaches an
+`'error'` listener to it, so you don't have to do that manually.
+
+Special case: `stream` can also be a String or Buffer.
+
+### combinedStream.write(data)
+
+You should not call this, `combinedStream` takes care of piping the appended
+streams into itself for you.
+
+### combinedStream.resume()
+
+Causes `combinedStream` to start drain the streams it manages. The function is
+idempotent, and also emits a `'resume'` event each time which usually goes to
+the stream that is currently being drained.
+
+### combinedStream.pause();
+
+If `combinedStream.pauseStreams` is set to `false`, this does nothing.
+Otherwise a `'pause'` event is emitted, this goes to the stream that is
+currently being drained, so you can use it to apply back pressure.
+
+### combinedStream.end();
+
+Sets `combinedStream.writable` to false, emits an `'end'` event, and removes
+all streams from the queue.
+
+### combinedStream.destroy();
+
+Same as `combinedStream.end()`, except it emits a `'close'` event instead of
+`'end'`.
+
+## License
+
+combined-stream is licensed under the MIT license.
diff --git a/tortoise/node_modules/combined-stream/package.json b/tortoise/node_modules/combined-stream/package.json
new file mode 100644
index 0000000..6982b6d
--- /dev/null
+++ b/tortoise/node_modules/combined-stream/package.json
@@ -0,0 +1,25 @@
+{
+  "author": "Felix Geisendörfer <felix@debuggable.com> (http://debuggable.com/)",
+  "name": "combined-stream",
+  "description": "A stream that emits multiple other streams one after another.",
+  "version": "1.0.8",
+  "homepage": "https://github.com/felixge/node-combined-stream",
+  "repository": {
+    "type": "git",
+    "url": "git://github.com/felixge/node-combined-stream.git"
+  },
+  "main": "./lib/combined_stream",
+  "scripts": {
+    "test": "node test/run.js"
+  },
+  "engines": {
+    "node": ">= 0.8"
+  },
+  "dependencies": {
+    "delayed-stream": "~1.0.0"
+  },
+  "devDependencies": {
+    "far": "~0.0.7"
+  },
+  "license": "MIT"
+}
diff --git a/tortoise/node_modules/combined-stream/yarn.lock b/tortoise/node_modules/combined-stream/yarn.lock
new file mode 100644
index 0000000..7edf418
--- /dev/null
+++ b/tortoise/node_modules/combined-stream/yarn.lock
@@ -0,0 +1,17 @@
+# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
+# yarn lockfile v1
+
+
+delayed-stream@~1.0.0:
+  version "1.0.0"
+  resolved "https://registry.yarnpkg.com/delayed-stream/-/delayed-stream-1.0.0.tgz#df3ae199acadfb7d440aaae0b29e2272b24ec619"
+
+far@~0.0.7:
+  version "0.0.7"
+  resolved "https://registry.yarnpkg.com/far/-/far-0.0.7.tgz#01c1fd362bcd26ce9cf161af3938aa34619f79a7"
+  dependencies:
+    oop "0.0.3"
+
+oop@0.0.3:
+  version "0.0.3"
+  resolved "https://registry.yarnpkg.com/oop/-/oop-0.0.3.tgz#70fa405a5650891a194fdc82ca68dad6dabf4401"
diff --git a/tortoise/node_modules/delayed-stream/.npmignore b/tortoise/node_modules/delayed-stream/.npmignore
new file mode 100644
index 0000000..9daeafb
--- /dev/null
+++ b/tortoise/node_modules/delayed-stream/.npmignore
@@ -0,0 +1 @@
+test
diff --git a/tortoise/node_modules/delayed-stream/License b/tortoise/node_modules/delayed-stream/License
new file mode 100644
index 0000000..4804b7a
--- /dev/null
+++ b/tortoise/node_modules/delayed-stream/License
@@ -0,0 +1,19 @@
+Copyright (c) 2011 Debuggable Limited <felix@debuggable.com>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.
diff --git a/tortoise/node_modules/delayed-stream/Makefile b/tortoise/node_modules/delayed-stream/Makefile
new file mode 100644
index 0000000..b4ff85a
--- /dev/null
+++ b/tortoise/node_modules/delayed-stream/Makefile
@@ -0,0 +1,7 @@
+SHELL := /bin/bash
+
+test:
+	@./test/run.js
+
+.PHONY: test
+
diff --git a/tortoise/node_modules/delayed-stream/Readme.md b/tortoise/node_modules/delayed-stream/Readme.md
new file mode 100644
index 0000000..aca36f9
--- /dev/null
+++ b/tortoise/node_modules/delayed-stream/Readme.md
@@ -0,0 +1,141 @@
+# delayed-stream
+
+Buffers events from a stream until you are ready to handle them.
+
+## Installation
+
+``` bash
+npm install delayed-stream
+```
+
+## Usage
+
+The following example shows how to write a http echo server that delays its
+response by 1000 ms.
+
+``` javascript
+var DelayedStream = require('delayed-stream');
+var http = require('http');
+
+http.createServer(function(req, res) {
+  var delayed = DelayedStream.create(req);
+
+  setTimeout(function() {
+    res.writeHead(200);
+    delayed.pipe(res);
+  }, 1000);
+});
+```
+
+If you are not using `Stream#pipe`, you can also manually release the buffered
+events by calling `delayedStream.resume()`:
+
+``` javascript
+var delayed = DelayedStream.create(req);
+
+setTimeout(function() {
+  // Emit all buffered events and resume underlaying source
+  delayed.resume();
+}, 1000);
+```
+
+## Implementation
+
+In order to use this meta stream properly, here are a few things you should
+know about the implementation.
+
+### Event Buffering / Proxying
+
+All events of the `source` stream are hijacked by overwriting the `source.emit`
+method. Until node implements a catch-all event listener, this is the only way.
+
+However, delayed-stream still continues to emit all events it captures on the
+`source`, regardless of whether you have released the delayed stream yet or
+not.
+
+Upon creation, delayed-stream captures all `source` events and stores them in
+an internal event buffer. Once `delayedStream.release()` is called, all
+buffered events are emitted on the `delayedStream`, and the event buffer is
+cleared. After that, delayed-stream merely acts as a proxy for the underlaying
+source.
+
+### Error handling
+
+Error events on `source` are buffered / proxied just like any other events.
+However, `delayedStream.create` attaches a no-op `'error'` listener to the
+`source`. This way you only have to handle errors on the `delayedStream`
+object, rather than in two places.
+
+### Buffer limits
+
+delayed-stream provides a `maxDataSize` property that can be used to limit
+the amount of data being buffered. In order to protect you from bad `source`
+streams that don't react to `source.pause()`, this feature is enabled by
+default.
+
+## API
+
+### DelayedStream.create(source, [options])
+
+Returns a new `delayedStream`. Available options are:
+
+* `pauseStream`
+* `maxDataSize`
+
+The description for those properties can be found below.
+
+### delayedStream.source
+
+The `source` stream managed by this object. This is useful if you are
+passing your `delayedStream` around, and you still want to access properties
+on the `source` object.
+
+### delayedStream.pauseStream = true
+
+Whether to pause the underlaying `source` when calling
+`DelayedStream.create()`. Modifying this property afterwards has no effect.
+
+### delayedStream.maxDataSize = 1024 * 1024
+
+The amount of data to buffer before emitting an `error`.
+
+If the underlaying source is emitting `Buffer` objects, the `maxDataSize`
+refers to bytes.
+
+If the underlaying source is emitting JavaScript strings, the size refers to
+characters.
+
+If you know what you are doing, you can set this property to `Infinity` to
+disable this feature. You can also modify this property during runtime.
+
+### delayedStream.dataSize = 0
+
+The amount of data buffered so far.
+
+### delayedStream.readable
+
+An ECMA5 getter that returns the value of `source.readable`.
+
+### delayedStream.resume()
+
+If the `delayedStream` has not been released so far, `delayedStream.release()`
+is called.
+
+In either case, `source.resume()` is called.
+
+### delayedStream.pause()
+
+Calls `source.pause()`.
+
+### delayedStream.pipe(dest)
+
+Calls `delayedStream.resume()` and then proxies the arguments to `source.pipe`.
+
+### delayedStream.release()
+
+Emits and clears all events that have been buffered up so far. This does not
+resume the underlaying source, use `delayedStream.resume()` instead.
+
+## License
+
+delayed-stream is licensed under the MIT license.
diff --git a/tortoise/node_modules/delayed-stream/package.json b/tortoise/node_modules/delayed-stream/package.json
new file mode 100644
index 0000000..eea3291
--- /dev/null
+++ b/tortoise/node_modules/delayed-stream/package.json
@@ -0,0 +1,27 @@
+{
+  "author": "Felix Geisendörfer <felix@debuggable.com> (http://debuggable.com/)",
+  "contributors": [
+    "Mike Atkins <apeherder@gmail.com>"
+  ],
+  "name": "delayed-stream",
+  "description": "Buffers events from a stream until you are ready to handle them.",
+  "license": "MIT",
+  "version": "1.0.0",
+  "homepage": "https://github.com/felixge/node-delayed-stream",
+  "repository": {
+    "type": "git",
+    "url": "git://github.com/felixge/node-delayed-stream.git"
+  },
+  "main": "./lib/delayed_stream",
+  "engines": {
+    "node": ">=0.4.0"
+  },
+  "scripts": {
+    "test": "make test"
+  },
+  "dependencies": {},
+  "devDependencies": {
+    "fake": "0.2.0",
+    "far": "0.0.1"
+  }
+}
diff --git a/tortoise/node_modules/follow-redirects/LICENSE b/tortoise/node_modules/follow-redirects/LICENSE
new file mode 100644
index 0000000..742cbad
--- /dev/null
+++ b/tortoise/node_modules/follow-redirects/LICENSE
@@ -0,0 +1,18 @@
+Copyright 2014–present Olivier Lalonde <olalonde@gmail.com>, James Talmage <james@talmage.io>, Ruben Verborgh
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+of the Software, and to permit persons to whom the Software is furnished to do
+so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR
+IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/tortoise/node_modules/follow-redirects/README.md b/tortoise/node_modules/follow-redirects/README.md
new file mode 100644
index 0000000..eb869a6
--- /dev/null
+++ b/tortoise/node_modules/follow-redirects/README.md
@@ -0,0 +1,155 @@
+## Follow Redirects
+
+Drop-in replacement for Node's `http` and `https` modules that automatically follows redirects.
+
+[![npm version](https://img.shields.io/npm/v/follow-redirects.svg)](https://www.npmjs.com/package/follow-redirects)
+[![Build Status](https://github.com/follow-redirects/follow-redirects/workflows/CI/badge.svg)](https://github.com/follow-redirects/follow-redirects/actions)
+[![Coverage Status](https://coveralls.io/repos/follow-redirects/follow-redirects/badge.svg?branch=master)](https://coveralls.io/r/follow-redirects/follow-redirects?branch=master)
+[![npm downloads](https://img.shields.io/npm/dm/follow-redirects.svg)](https://www.npmjs.com/package/follow-redirects)
+[![Sponsor on GitHub](https://img.shields.io/static/v1?label=Sponsor&message=%F0%9F%92%96&logo=GitHub)](https://github.com/sponsors/RubenVerborgh)
+
+`follow-redirects` provides [request](https://nodejs.org/api/http.html#http_http_request_options_callback) and [get](https://nodejs.org/api/http.html#http_http_get_options_callback)
+ methods that behave identically to those found on the native [http](https://nodejs.org/api/http.html#http_http_request_options_callback) and [https](https://nodejs.org/api/https.html#https_https_request_options_callback)
+ modules, with the exception that they will seamlessly follow redirects.
+
+```javascript
+const { http, https } = require('follow-redirects');
+
+http.get('http://bit.ly/900913', response => {
+  response.on('data', chunk => {
+    console.log(chunk);
+  });
+}).on('error', err => {
+  console.error(err);
+});
+```
+
+You can inspect the final redirected URL through the `responseUrl` property on the `response`.
+If no redirection happened, `responseUrl` is the original request URL.
+
+```javascript
+const request = https.request({
+  host: 'bitly.com',
+  path: '/UHfDGO',
+}, response => {
+  console.log(response.responseUrl);
+  // 'http://duckduckgo.com/robots.txt'
+});
+request.end();
+```
+
+## Options
+### Global options
+Global options are set directly on the `follow-redirects` module:
+
+```javascript
+const followRedirects = require('follow-redirects');
+followRedirects.maxRedirects = 10;
+followRedirects.maxBodyLength = 20 * 1024 * 1024; // 20 MB
+```
+
+The following global options are supported:
+
+- `maxRedirects` (default: `21`) – sets the maximum number of allowed redirects; if exceeded, an error will be emitted.
+
+- `maxBodyLength` (default: 10MB) – sets the maximum size of the request body; if exceeded, an error will be emitted.
+
+### Per-request options
+Per-request options are set by passing an `options` object:
+
+```javascript
+const url = require('url');
+const { http, https } = require('follow-redirects');
+
+const options = url.parse('http://bit.ly/900913');
+options.maxRedirects = 10;
+options.beforeRedirect = (options, response, request) => {
+  // Use this to adjust the request options upon redirecting,
+  // to inspect the latest response headers,
+  // or to cancel the request by throwing an error
+
+  // response.headers = the redirect response headers
+  // response.statusCode = the redirect response code (eg. 301, 307, etc.)
+
+  // request.url = the requested URL that resulted in a redirect
+  // request.headers = the headers in the request that resulted in a redirect
+  // request.method = the method of the request that resulted in a redirect
+  if (options.hostname === "example.com") {
+    options.auth = "user:password";
+  }
+};
+http.request(options);
+```
+
+In addition to the [standard HTTP](https://nodejs.org/api/http.html#http_http_request_options_callback) and [HTTPS options](https://nodejs.org/api/https.html#https_https_request_options_callback),
+the following per-request options are supported:
+- `followRedirects` (default: `true`) – whether redirects should be followed.
+
+- `maxRedirects` (default: `21`) – sets the maximum number of allowed redirects; if exceeded, an error will be emitted.
+
+- `maxBodyLength` (default: 10MB) – sets the maximum size of the request body; if exceeded, an error will be emitted.
+
+- `beforeRedirect` (default: `undefined`) – optionally change the request `options` on redirects, or abort the request by throwing an error.
+
+- `agents` (default: `undefined`) – sets the `agent` option per protocol, since HTTP and HTTPS use different agents. Example value: `{ http: new http.Agent(), https: new https.Agent() }`
+
+- `trackRedirects` (default: `false`) – whether to store the redirected response details into the `redirects` array on the response object.
+
+
+### Advanced usage
+By default, `follow-redirects` will use the Node.js default implementations
+of [`http`](https://nodejs.org/api/http.html)
+and [`https`](https://nodejs.org/api/https.html).
+To enable features such as caching and/or intermediate request tracking,
+you might instead want to wrap `follow-redirects` around custom protocol implementations:
+
+```javascript
+const { http, https } = require('follow-redirects').wrap({
+  http: require('your-custom-http'),
+  https: require('your-custom-https'),
+});
+```
+
+Such custom protocols only need an implementation of the `request` method.
+
+## Browser Usage
+
+Due to the way the browser works,
+the `http` and `https` browser equivalents perform redirects by default.
+
+By requiring `follow-redirects` this way:
+```javascript
+const http = require('follow-redirects/http');
+const https = require('follow-redirects/https');
+```
+you can easily tell webpack and friends to replace
+`follow-redirect` by the built-in versions:
+
+```json
+{
+  "follow-redirects/http"  : "http",
+  "follow-redirects/https" : "https"
+}
+```
+
+## Contributing
+
+Pull Requests are always welcome. Please [file an issue](https://github.com/follow-redirects/follow-redirects/issues)
+ detailing your proposal before you invest your valuable time. Additional features and bug fixes should be accompanied
+ by tests. You can run the test suite locally with a simple `npm test` command.
+
+## Debug Logging
+
+`follow-redirects` uses the excellent [debug](https://www.npmjs.com/package/debug) for logging. To turn on logging
+ set the environment variable `DEBUG=follow-redirects` for debug output from just this module. When running the test
+ suite it is sometimes advantageous to set `DEBUG=*` to see output from the express server as well.
+
+## Authors
+
+- [Ruben Verborgh](https://ruben.verborgh.org/)
+- [Olivier Lalonde](mailto:olalonde@gmail.com)
+- [James Talmage](mailto:james@talmage.io)
+
+## License
+
+[MIT License](https://github.com/follow-redirects/follow-redirects/blob/master/LICENSE)
diff --git a/tortoise/node_modules/follow-redirects/debug.js b/tortoise/node_modules/follow-redirects/debug.js
new file mode 100644
index 0000000..decb77d
--- /dev/null
+++ b/tortoise/node_modules/follow-redirects/debug.js
@@ -0,0 +1,15 @@
+var debug;
+
+module.exports = function () {
+  if (!debug) {
+    try {
+      /* eslint global-require: off */
+      debug = require("debug")("follow-redirects");
+    }
+    catch (error) { /* */ }
+    if (typeof debug !== "function") {
+      debug = function () { /* */ };
+    }
+  }
+  debug.apply(null, arguments);
+};
diff --git a/tortoise/node_modules/follow-redirects/http.js b/tortoise/node_modules/follow-redirects/http.js
new file mode 100644
index 0000000..695e356
--- /dev/null
+++ b/tortoise/node_modules/follow-redirects/http.js
@@ -0,0 +1 @@
+module.exports = require("./").http;
diff --git a/tortoise/node_modules/follow-redirects/https.js b/tortoise/node_modules/follow-redirects/https.js
new file mode 100644
index 0000000..d21c921
--- /dev/null
+++ b/tortoise/node_modules/follow-redirects/https.js
@@ -0,0 +1 @@
+module.exports = require("./").https;
diff --git a/tortoise/node_modules/follow-redirects/index.js b/tortoise/node_modules/follow-redirects/index.js
new file mode 100644
index 0000000..a30b32c
--- /dev/null
+++ b/tortoise/node_modules/follow-redirects/index.js
@@ -0,0 +1,686 @@
+var url = require("url");
+var URL = url.URL;
+var http = require("http");
+var https = require("https");
+var Writable = require("stream").Writable;
+var assert = require("assert");
+var debug = require("./debug");
+
+// Preventive platform detection
+// istanbul ignore next
+(function detectUnsupportedEnvironment() {
+  var looksLikeNode = typeof process !== "undefined";
+  var looksLikeBrowser = typeof window !== "undefined" && typeof document !== "undefined";
+  var looksLikeV8 = isFunction(Error.captureStackTrace);
+  if (!looksLikeNode && (looksLikeBrowser || !looksLikeV8)) {
+    console.warn("The follow-redirects package should be excluded from browser builds.");
+  }
+}());
+
+// Whether to use the native URL object or the legacy url module
+var useNativeURL = false;
+try {
+  assert(new URL(""));
+}
+catch (error) {
+  useNativeURL = error.code === "ERR_INVALID_URL";
+}
+
+// URL fields to preserve in copy operations
+var preservedUrlFields = [
+  "auth",
+  "host",
+  "hostname",
+  "href",
+  "path",
+  "pathname",
+  "port",
+  "protocol",
+  "query",
+  "search",
+  "hash",
+];
+
+// Create handlers that pass events from native requests
+var events = ["abort", "aborted", "connect", "error", "socket", "timeout"];
+var eventHandlers = Object.create(null);
+events.forEach(function (event) {
+  eventHandlers[event] = function (arg1, arg2, arg3) {
+    this._redirectable.emit(event, arg1, arg2, arg3);
+  };
+});
+
+// Error types with codes
+var InvalidUrlError = createErrorType(
+  "ERR_INVALID_URL",
+  "Invalid URL",
+  TypeError
+);
+var RedirectionError = createErrorType(
+  "ERR_FR_REDIRECTION_FAILURE",
+  "Redirected request failed"
+);
+var TooManyRedirectsError = createErrorType(
+  "ERR_FR_TOO_MANY_REDIRECTS",
+  "Maximum number of redirects exceeded",
+  RedirectionError
+);
+var MaxBodyLengthExceededError = createErrorType(
+  "ERR_FR_MAX_BODY_LENGTH_EXCEEDED",
+  "Request body larger than maxBodyLength limit"
+);
+var WriteAfterEndError = createErrorType(
+  "ERR_STREAM_WRITE_AFTER_END",
+  "write after end"
+);
+
+// istanbul ignore next
+var destroy = Writable.prototype.destroy || noop;
+
+// An HTTP(S) request that can be redirected
+function RedirectableRequest(options, responseCallback) {
+  // Initialize the request
+  Writable.call(this);
+  this._sanitizeOptions(options);
+  this._options = options;
+  this._ended = false;
+  this._ending = false;
+  this._redirectCount = 0;
+  this._redirects = [];
+  this._requestBodyLength = 0;
+  this._requestBodyBuffers = [];
+
+  // Attach a callback if passed
+  if (responseCallback) {
+    this.on("response", responseCallback);
+  }
+
+  // React to responses of native requests
+  var self = this;
+  this._onNativeResponse = function (response) {
+    try {
+      self._processResponse(response);
+    }
+    catch (cause) {
+      self.emit("error", cause instanceof RedirectionError ?
+        cause : new RedirectionError({ cause: cause }));
+    }
+  };
+
+  // Perform the first request
+  this._performRequest();
+}
+RedirectableRequest.prototype = Object.create(Writable.prototype);
+
+RedirectableRequest.prototype.abort = function () {
+  destroyRequest(this._currentRequest);
+  this._currentRequest.abort();
+  this.emit("abort");
+};
+
+RedirectableRequest.prototype.destroy = function (error) {
+  destroyRequest(this._currentRequest, error);
+  destroy.call(this, error);
+  return this;
+};
+
+// Writes buffered data to the current native request
+RedirectableRequest.prototype.write = function (data, encoding, callback) {
+  // Writing is not allowed if end has been called
+  if (this._ending) {
+    throw new WriteAfterEndError();
+  }
+
+  // Validate input and shift parameters if necessary
+  if (!isString(data) && !isBuffer(data)) {
+    throw new TypeError("data should be a string, Buffer or Uint8Array");
+  }
+  if (isFunction(encoding)) {
+    callback = encoding;
+    encoding = null;
+  }
+
+  // Ignore empty buffers, since writing them doesn't invoke the callback
+  // https://github.com/nodejs/node/issues/22066
+  if (data.length === 0) {
+    if (callback) {
+      callback();
+    }
+    return;
+  }
+  // Only write when we don't exceed the maximum body length
+  if (this._requestBodyLength + data.length <= this._options.maxBodyLength) {
+    this._requestBodyLength += data.length;
+    this._requestBodyBuffers.push({ data: data, encoding: encoding });
+    this._currentRequest.write(data, encoding, callback);
+  }
+  // Error when we exceed the maximum body length
+  else {
+    this.emit("error", new MaxBodyLengthExceededError());
+    this.abort();
+  }
+};
+
+// Ends the current native request
+RedirectableRequest.prototype.end = function (data, encoding, callback) {
+  // Shift parameters if necessary
+  if (isFunction(data)) {
+    callback = data;
+    data = encoding = null;
+  }
+  else if (isFunction(encoding)) {
+    callback = encoding;
+    encoding = null;
+  }
+
+  // Write data if needed and end
+  if (!data) {
+    this._ended = this._ending = true;
+    this._currentRequest.end(null, null, callback);
+  }
+  else {
+    var self = this;
+    var currentRequest = this._currentRequest;
+    this.write(data, encoding, function () {
+      self._ended = true;
+      currentRequest.end(null, null, callback);
+    });
+    this._ending = true;
+  }
+};
+
+// Sets a header value on the current native request
+RedirectableRequest.prototype.setHeader = function (name, value) {
+  this._options.headers[name] = value;
+  this._currentRequest.setHeader(name, value);
+};
+
+// Clears a header value on the current native request
+RedirectableRequest.prototype.removeHeader = function (name) {
+  delete this._options.headers[name];
+  this._currentRequest.removeHeader(name);
+};
+
+// Global timeout for all underlying requests
+RedirectableRequest.prototype.setTimeout = function (msecs, callback) {
+  var self = this;
+
+  // Destroys the socket on timeout
+  function destroyOnTimeout(socket) {
+    socket.setTimeout(msecs);
+    socket.removeListener("timeout", socket.destroy);
+    socket.addListener("timeout", socket.destroy);
+  }
+
+  // Sets up a timer to trigger a timeout event
+  function startTimer(socket) {
+    if (self._timeout) {
+      clearTimeout(self._timeout);
+    }
+    self._timeout = setTimeout(function () {
+      self.emit("timeout");
+      clearTimer();
+    }, msecs);
+    destroyOnTimeout(socket);
+  }
+
+  // Stops a timeout from triggering
+  function clearTimer() {
+    // Clear the timeout
+    if (self._timeout) {
+      clearTimeout(self._timeout);
+      self._timeout = null;
+    }
+
+    // Clean up all attached listeners
+    self.removeListener("abort", clearTimer);
+    self.removeListener("error", clearTimer);
+    self.removeListener("response", clearTimer);
+    self.removeListener("close", clearTimer);
+    if (callback) {
+      self.removeListener("timeout", callback);
+    }
+    if (!self.socket) {
+      self._currentRequest.removeListener("socket", startTimer);
+    }
+  }
+
+  // Attach callback if passed
+  if (callback) {
+    this.on("timeout", callback);
+  }
+
+  // Start the timer if or when the socket is opened
+  if (this.socket) {
+    startTimer(this.socket);
+  }
+  else {
+    this._currentRequest.once("socket", startTimer);
+  }
+
+  // Clean up on events
+  this.on("socket", destroyOnTimeout);
+  this.on("abort", clearTimer);
+  this.on("error", clearTimer);
+  this.on("response", clearTimer);
+  this.on("close", clearTimer);
+
+  return this;
+};
+
+// Proxy all other public ClientRequest methods
+[
+  "flushHeaders", "getHeader",
+  "setNoDelay", "setSocketKeepAlive",
+].forEach(function (method) {
+  RedirectableRequest.prototype[method] = function (a, b) {
+    return this._currentRequest[method](a, b);
+  };
+});
+
+// Proxy all public ClientRequest properties
+["aborted", "connection", "socket"].forEach(function (property) {
+  Object.defineProperty(RedirectableRequest.prototype, property, {
+    get: function () { return this._currentRequest[property]; },
+  });
+});
+
+RedirectableRequest.prototype._sanitizeOptions = function (options) {
+  // Ensure headers are always present
+  if (!options.headers) {
+    options.headers = {};
+  }
+
+  // Since http.request treats host as an alias of hostname,
+  // but the url module interprets host as hostname plus port,
+  // eliminate the host property to avoid confusion.
+  if (options.host) {
+    // Use hostname if set, because it has precedence
+    if (!options.hostname) {
+      options.hostname = options.host;
+    }
+    delete options.host;
+  }
+
+  // Complete the URL object when necessary
+  if (!options.pathname && options.path) {
+    var searchPos = options.path.indexOf("?");
+    if (searchPos < 0) {
+      options.pathname = options.path;
+    }
+    else {
+      options.pathname = options.path.substring(0, searchPos);
+      options.search = options.path.substring(searchPos);
+    }
+  }
+};
+
+
+// Executes the next native request (initial or redirect)
+RedirectableRequest.prototype._performRequest = function () {
+  // Load the native protocol
+  var protocol = this._options.protocol;
+  var nativeProtocol = this._options.nativeProtocols[protocol];
+  if (!nativeProtocol) {
+    throw new TypeError("Unsupported protocol " + protocol);
+  }
+
+  // If specified, use the agent corresponding to the protocol
+  // (HTTP and HTTPS use different types of agents)
+  if (this._options.agents) {
+    var scheme = protocol.slice(0, -1);
+    this._options.agent = this._options.agents[scheme];
+  }
+
+  // Create the native request and set up its event handlers
+  var request = this._currentRequest =
+        nativeProtocol.request(this._options, this._onNativeResponse);
+  request._redirectable = this;
+  for (var event of events) {
+    request.on(event, eventHandlers[event]);
+  }
+
+  // RFC7230§5.3.1: When making a request directly to an origin server, […]
+  // a client MUST send only the absolute path […] as the request-target.
+  this._currentUrl = /^\//.test(this._options.path) ?
+    url.format(this._options) :
+    // When making a request to a proxy, […]
+    // a client MUST send the target URI in absolute-form […].
+    this._options.path;
+
+  // End a redirected request
+  // (The first request must be ended explicitly with RedirectableRequest#end)
+  if (this._isRedirect) {
+    // Write the request entity and end
+    var i = 0;
+    var self = this;
+    var buffers = this._requestBodyBuffers;
+    (function writeNext(error) {
+      // Only write if this request has not been redirected yet
+      // istanbul ignore else
+      if (request === self._currentRequest) {
+        // Report any write errors
+        // istanbul ignore if
+        if (error) {
+          self.emit("error", error);
+        }
+        // Write the next buffer if there are still left
+        else if (i < buffers.length) {
+          var buffer = buffers[i++];
+          // istanbul ignore else
+          if (!request.finished) {
+            request.write(buffer.data, buffer.encoding, writeNext);
+          }
+        }
+        // End the request if `end` has been called on us
+        else if (self._ended) {
+          request.end();
+        }
+      }
+    }());
+  }
+};
+
+// Processes a response from the current native request
+RedirectableRequest.prototype._processResponse = function (response) {
+  // Store the redirected response
+  var statusCode = response.statusCode;
+  if (this._options.trackRedirects) {
+    this._redirects.push({
+      url: this._currentUrl,
+      headers: response.headers,
+      statusCode: statusCode,
+    });
+  }
+
+  // RFC7231§6.4: The 3xx (Redirection) class of status code indicates
+  // that further action needs to be taken by the user agent in order to
+  // fulfill the request. If a Location header field is provided,
+  // the user agent MAY automatically redirect its request to the URI
+  // referenced by the Location field value,
+  // even if the specific status code is not understood.
+
+  // If the response is not a redirect; return it as-is
+  var location = response.headers.location;
+  if (!location || this._options.followRedirects === false ||
+      statusCode < 300 || statusCode >= 400) {
+    response.responseUrl = this._currentUrl;
+    response.redirects = this._redirects;
+    this.emit("response", response);
+
+    // Clean up
+    this._requestBodyBuffers = [];
+    return;
+  }
+
+  // The response is a redirect, so abort the current request
+  destroyRequest(this._currentRequest);
+  // Discard the remainder of the response to avoid waiting for data
+  response.destroy();
+
+  // RFC7231§6.4: A client SHOULD detect and intervene
+  // in cyclical redirections (i.e., "infinite" redirection loops).
+  if (++this._redirectCount > this._options.maxRedirects) {
+    throw new TooManyRedirectsError();
+  }
+
+  // Store the request headers if applicable
+  var requestHeaders;
+  var beforeRedirect = this._options.beforeRedirect;
+  if (beforeRedirect) {
+    requestHeaders = Object.assign({
+      // The Host header was set by nativeProtocol.request
+      Host: response.req.getHeader("host"),
+    }, this._options.headers);
+  }
+
+  // RFC7231§6.4: Automatic redirection needs to done with
+  // care for methods not known to be safe, […]
+  // RFC7231§6.4.2–3: For historical reasons, a user agent MAY change
+  // the request method from POST to GET for the subsequent request.
+  var method = this._options.method;
+  if ((statusCode === 301 || statusCode === 302) && this._options.method === "POST" ||
+      // RFC7231§6.4.4: The 303 (See Other) status code indicates that
+      // the server is redirecting the user agent to a different resource […]
+      // A user agent can perform a retrieval request targeting that URI
+      // (a GET or HEAD request if using HTTP) […]
+      (statusCode === 303) && !/^(?:GET|HEAD)$/.test(this._options.method)) {
+    this._options.method = "GET";
+    // Drop a possible entity and headers related to it
+    this._requestBodyBuffers = [];
+    removeMatchingHeaders(/^content-/i, this._options.headers);
+  }
+
+  // Drop the Host header, as the redirect might lead to a different host
+  var currentHostHeader = removeMatchingHeaders(/^host$/i, this._options.headers);
+
+  // If the redirect is relative, carry over the host of the last request
+  var currentUrlParts = parseUrl(this._currentUrl);
+  var currentHost = currentHostHeader || currentUrlParts.host;
+  var currentUrl = /^\w+:/.test(location) ? this._currentUrl :
+    url.format(Object.assign(currentUrlParts, { host: currentHost }));
+
+  // Create the redirected request
+  var redirectUrl = resolveUrl(location, currentUrl);
+  debug("redirecting to", redirectUrl.href);
+  this._isRedirect = true;
+  spreadUrlObject(redirectUrl, this._options);
+
+  // Drop confidential headers when redirecting to a less secure protocol
+  // or to a different domain that is not a superdomain
+  if (redirectUrl.protocol !== currentUrlParts.protocol &&
+     redirectUrl.protocol !== "https:" ||
+     redirectUrl.host !== currentHost &&
+     !isSubdomain(redirectUrl.host, currentHost)) {
+    removeMatchingHeaders(/^(?:(?:proxy-)?authorization|cookie)$/i, this._options.headers);
+  }
+
+  // Evaluate the beforeRedirect callback
+  if (isFunction(beforeRedirect)) {
+    var responseDetails = {
+      headers: response.headers,
+      statusCode: statusCode,
+    };
+    var requestDetails = {
+      url: currentUrl,
+      method: method,
+      headers: requestHeaders,
+    };
+    beforeRedirect(this._options, responseDetails, requestDetails);
+    this._sanitizeOptions(this._options);
+  }
+
+  // Perform the redirected request
+  this._performRequest();
+};
+
+// Wraps the key/value object of protocols with redirect functionality
+function wrap(protocols) {
+  // Default settings
+  var exports = {
+    maxRedirects: 21,
+    maxBodyLength: 10 * 1024 * 1024,
+  };
+
+  // Wrap each protocol
+  var nativeProtocols = {};
+  Object.keys(protocols).forEach(function (scheme) {
+    var protocol = scheme + ":";
+    var nativeProtocol = nativeProtocols[protocol] = protocols[scheme];
+    var wrappedProtocol = exports[scheme] = Object.create(nativeProtocol);
+
+    // Executes a request, following redirects
+    function request(input, options, callback) {
+      // Parse parameters, ensuring that input is an object
+      if (isURL(input)) {
+        input = spreadUrlObject(input);
+      }
+      else if (isString(input)) {
+        input = spreadUrlObject(parseUrl(input));
+      }
+      else {
+        callback = options;
+        options = validateUrl(input);
+        input = { protocol: protocol };
+      }
+      if (isFunction(options)) {
+        callback = options;
+        options = null;
+      }
+
+      // Set defaults
+      options = Object.assign({
+        maxRedirects: exports.maxRedirects,
+        maxBodyLength: exports.maxBodyLength,
+      }, input, options);
+      options.nativeProtocols = nativeProtocols;
+      if (!isString(options.host) && !isString(options.hostname)) {
+        options.hostname = "::1";
+      }
+
+      assert.equal(options.protocol, protocol, "protocol mismatch");
+      debug("options", options);
+      return new RedirectableRequest(options, callback);
+    }
+
+    // Executes a GET request, following redirects
+    function get(input, options, callback) {
+      var wrappedRequest = wrappedProtocol.request(input, options, callback);
+      wrappedRequest.end();
+      return wrappedRequest;
+    }
+
+    // Expose the properties on the wrapped protocol
+    Object.defineProperties(wrappedProtocol, {
+      request: { value: request, configurable: true, enumerable: true, writable: true },
+      get: { value: get, configurable: true, enumerable: true, writable: true },
+    });
+  });
+  return exports;
+}
+
+function noop() { /* empty */ }
+
+function parseUrl(input) {
+  var parsed;
+  // istanbul ignore else
+  if (useNativeURL) {
+    parsed = new URL(input);
+  }
+  else {
+    // Ensure the URL is valid and absolute
+    parsed = validateUrl(url.parse(input));
+    if (!isString(parsed.protocol)) {
+      throw new InvalidUrlError({ input });
+    }
+  }
+  return parsed;
+}
+
+function resolveUrl(relative, base) {
+  // istanbul ignore next
+  return useNativeURL ? new URL(relative, base) : parseUrl(url.resolve(base, relative));
+}
+
+function validateUrl(input) {
+  if (/^\[/.test(input.hostname) && !/^\[[:0-9a-f]+\]$/i.test(input.hostname)) {
+    throw new InvalidUrlError({ input: input.href || input });
+  }
+  if (/^\[/.test(input.host) && !/^\[[:0-9a-f]+\](:\d+)?$/i.test(input.host)) {
+    throw new InvalidUrlError({ input: input.href || input });
+  }
+  return input;
+}
+
+function spreadUrlObject(urlObject, target) {
+  var spread = target || {};
+  for (var key of preservedUrlFields) {
+    spread[key] = urlObject[key];
+  }
+
+  // Fix IPv6 hostname
+  if (spread.hostname.startsWith("[")) {
+    spread.hostname = spread.hostname.slice(1, -1);
+  }
+  // Ensure port is a number
+  if (spread.port !== "") {
+    spread.port = Number(spread.port);
+  }
+  // Concatenate path
+  spread.path = spread.search ? spread.pathname + spread.search : spread.pathname;
+
+  return spread;
+}
+
+function removeMatchingHeaders(regex, headers) {
+  var lastValue;
+  for (var header in headers) {
+    if (regex.test(header)) {
+      lastValue = headers[header];
+      delete headers[header];
+    }
+  }
+  return (lastValue === null || typeof lastValue === "undefined") ?
+    undefined : String(lastValue).trim();
+}
+
+function createErrorType(code, message, baseClass) {
+  // Create constructor
+  function CustomError(properties) {
+    // istanbul ignore else
+    if (isFunction(Error.captureStackTrace)) {
+      Error.captureStackTrace(this, this.constructor);
+    }
+    Object.assign(this, properties || {});
+    this.code = code;
+    this.message = this.cause ? message + ": " + this.cause.message : message;
+  }
+
+  // Attach constructor and set default properties
+  CustomError.prototype = new (baseClass || Error)();
+  Object.defineProperties(CustomError.prototype, {
+    constructor: {
+      value: CustomError,
+      enumerable: false,
+    },
+    name: {
+      value: "Error [" + code + "]",
+      enumerable: false,
+    },
+  });
+  return CustomError;
+}
+
+function destroyRequest(request, error) {
+  for (var event of events) {
+    request.removeListener(event, eventHandlers[event]);
+  }
+  request.on("error", noop);
+  request.destroy(error);
+}
+
+function isSubdomain(subdomain, domain) {
+  assert(isString(subdomain) && isString(domain));
+  var dot = subdomain.length - domain.length - 1;
+  return dot > 0 && subdomain[dot] === "." && subdomain.endsWith(domain);
+}
+
+function isString(value) {
+  return typeof value === "string" || value instanceof String;
+}
+
+function isFunction(value) {
+  return typeof value === "function";
+}
+
+function isBuffer(value) {
+  return typeof value === "object" && ("length" in value);
+}
+
+function isURL(value) {
+  return URL && value instanceof URL;
+}
+
+// Exports
+module.exports = wrap({ http: http, https: https });
+module.exports.wrap = wrap;
diff --git a/tortoise/node_modules/follow-redirects/package.json b/tortoise/node_modules/follow-redirects/package.json
new file mode 100644
index 0000000..6f491e1
--- /dev/null
+++ b/tortoise/node_modules/follow-redirects/package.json
@@ -0,0 +1,58 @@
+{
+  "name": "follow-redirects",
+  "version": "1.15.9",
+  "description": "HTTP and HTTPS modules that follow redirects.",
+  "license": "MIT",
+  "main": "index.js",
+  "files": [
+    "*.js"
+  ],
+  "engines": {
+    "node": ">=4.0"
+  },
+  "scripts": {
+    "lint": "eslint *.js test",
+    "test": "nyc mocha"
+  },
+  "repository": {
+    "type": "git",
+    "url": "git+ssh://git@github.com/follow-redirects/follow-redirects.git"
+  },
+  "homepage": "https://github.com/follow-redirects/follow-redirects",
+  "bugs": {
+    "url": "https://github.com/follow-redirects/follow-redirects/issues"
+  },
+  "keywords": [
+    "http",
+    "https",
+    "url",
+    "redirect",
+    "client",
+    "location",
+    "utility"
+  ],
+  "author": "Ruben Verborgh <ruben@verborgh.org> (https://ruben.verborgh.org/)",
+  "contributors": [
+    "Olivier Lalonde <olalonde@gmail.com> (http://www.syskall.com)",
+    "James Talmage <james@talmage.io>"
+  ],
+  "funding": [
+    {
+      "type": "individual",
+      "url": "https://github.com/sponsors/RubenVerborgh"
+    }
+  ],
+  "peerDependenciesMeta": {
+    "debug": {
+      "optional": true
+    }
+  },
+  "devDependencies": {
+    "concat-stream": "^2.0.0",
+    "eslint": "^5.16.0",
+    "express": "^4.16.4",
+    "lolex": "^3.1.0",
+    "mocha": "^6.0.2",
+    "nyc": "^14.1.1"
+  }
+}
diff --git a/tortoise/node_modules/form-data/License b/tortoise/node_modules/form-data/License
new file mode 100644
index 0000000..c7ff12a
--- /dev/null
+++ b/tortoise/node_modules/form-data/License
@@ -0,0 +1,19 @@
+Copyright (c) 2012 Felix Geisendörfer (felix@debuggable.com) and contributors
+
+ Permission is hereby granted, free of charge, to any person obtaining a copy
+ of this software and associated documentation files (the "Software"), to deal
+ in the Software without restriction, including without limitation the rights
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ copies of the Software, and to permit persons to whom the Software is
+ furnished to do so, subject to the following conditions:
+
+ The above copyright notice and this permission notice shall be included in
+ all copies or substantial portions of the Software.
+
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ THE SOFTWARE.
diff --git a/tortoise/node_modules/form-data/Readme.md b/tortoise/node_modules/form-data/Readme.md
new file mode 100644
index 0000000..b09df6a
--- /dev/null
+++ b/tortoise/node_modules/form-data/Readme.md
@@ -0,0 +1,358 @@
+# Form-Data [![NPM Module](https://img.shields.io/npm/v/form-data.svg)](https://www.npmjs.com/package/form-data) [![Join the chat at https://gitter.im/form-data/form-data](http://form-data.github.io/images/gitterbadge.svg)](https://gitter.im/form-data/form-data)
+
+A library to create readable ```"multipart/form-data"``` streams. Can be used to submit forms and file uploads to other web applications.
+
+The API of this library is inspired by the [XMLHttpRequest-2 FormData Interface][xhr2-fd].
+
+[xhr2-fd]: http://dev.w3.org/2006/webapi/XMLHttpRequest-2/Overview.html#the-formdata-interface
+
+[![Linux Build](https://img.shields.io/travis/form-data/form-data/master.svg?label=linux:6.x-12.x)](https://travis-ci.org/form-data/form-data)
+[![MacOS Build](https://img.shields.io/travis/form-data/form-data/master.svg?label=macos:6.x-12.x)](https://travis-ci.org/form-data/form-data)
+[![Windows Build](https://img.shields.io/travis/form-data/form-data/master.svg?label=windows:6.x-12.x)](https://travis-ci.org/form-data/form-data)
+
+[![Coverage Status](https://img.shields.io/coveralls/form-data/form-data/master.svg?label=code+coverage)](https://coveralls.io/github/form-data/form-data?branch=master)
+[![Dependency Status](https://img.shields.io/david/form-data/form-data.svg)](https://david-dm.org/form-data/form-data)
+
+## Install
+
+```
+npm install --save form-data
+```
+
+## Usage
+
+In this example we are constructing a form with 3 fields that contain a string,
+a buffer and a file stream.
+
+``` javascript
+var FormData = require('form-data');
+var fs = require('fs');
+
+var form = new FormData();
+form.append('my_field', 'my value');
+form.append('my_buffer', new Buffer(10));
+form.append('my_file', fs.createReadStream('/foo/bar.jpg'));
+```
+
+Also you can use http-response stream:
+
+``` javascript
+var FormData = require('form-data');
+var http = require('http');
+
+var form = new FormData();
+
+http.request('http://nodejs.org/images/logo.png', function(response) {
+  form.append('my_field', 'my value');
+  form.append('my_buffer', new Buffer(10));
+  form.append('my_logo', response);
+});
+```
+
+Or @mikeal's [request](https://github.com/request/request) stream:
+
+``` javascript
+var FormData = require('form-data');
+var request = require('request');
+
+var form = new FormData();
+
+form.append('my_field', 'my value');
+form.append('my_buffer', new Buffer(10));
+form.append('my_logo', request('http://nodejs.org/images/logo.png'));
+```
+
+In order to submit this form to a web application, call ```submit(url, [callback])``` method:
+
+``` javascript
+form.submit('http://example.org/', function(err, res) {
+  // res – response object (http.IncomingMessage)  //
+  res.resume();
+});
+
+```
+
+For more advanced request manipulations ```submit()``` method returns ```http.ClientRequest``` object, or you can choose from one of the alternative submission methods.
+
+### Custom options
+
+You can provide custom options, such as `maxDataSize`:
+
+``` javascript
+var FormData = require('form-data');
+
+var form = new FormData({ maxDataSize: 20971520 });
+form.append('my_field', 'my value');
+form.append('my_buffer', /* something big */);
+```
+
+List of available options could be found in [combined-stream](https://github.com/felixge/node-combined-stream/blob/master/lib/combined_stream.js#L7-L15)
+
+### Alternative submission methods
+
+You can use node's http client interface:
+
+``` javascript
+var http = require('http');
+
+var request = http.request({
+  method: 'post',
+  host: 'example.org',
+  path: '/upload',
+  headers: form.getHeaders()
+});
+
+form.pipe(request);
+
+request.on('response', function(res) {
+  console.log(res.statusCode);
+});
+```
+
+Or if you would prefer the `'Content-Length'` header to be set for you:
+
+``` javascript
+form.submit('example.org/upload', function(err, res) {
+  console.log(res.statusCode);
+});
+```
+
+To use custom headers and pre-known length in parts:
+
+``` javascript
+var CRLF = '\r\n';
+var form = new FormData();
+
+var options = {
+  header: CRLF + '--' + form.getBoundary() + CRLF + 'X-Custom-Header: 123' + CRLF + CRLF,
+  knownLength: 1
+};
+
+form.append('my_buffer', buffer, options);
+
+form.submit('http://example.com/', function(err, res) {
+  if (err) throw err;
+  console.log('Done');
+});
+```
+
+Form-Data can recognize and fetch all the required information from common types of streams (```fs.readStream```, ```http.response``` and ```mikeal's request```), for some other types of streams you'd need to provide "file"-related information manually:
+
+``` javascript
+someModule.stream(function(err, stdout, stderr) {
+  if (err) throw err;
+
+  var form = new FormData();
+
+  form.append('file', stdout, {
+    filename: 'unicycle.jpg', // ... or:
+    filepath: 'photos/toys/unicycle.jpg',
+    contentType: 'image/jpeg',
+    knownLength: 19806
+  });
+
+  form.submit('http://example.com/', function(err, res) {
+    if (err) throw err;
+    console.log('Done');
+  });
+});
+```
+
+The `filepath` property overrides `filename` and may contain a relative path. This is typically used when uploading [multiple files from a directory](https://wicg.github.io/entries-api/#dom-htmlinputelement-webkitdirectory).
+
+For edge cases, like POST request to URL with query string or to pass HTTP auth credentials, object can be passed to `form.submit()` as first parameter:
+
+``` javascript
+form.submit({
+  host: 'example.com',
+  path: '/probably.php?extra=params',
+  auth: 'username:password'
+}, function(err, res) {
+  console.log(res.statusCode);
+});
+```
+
+In case you need to also send custom HTTP headers with the POST request, you can use the `headers` key in first parameter of `form.submit()`:
+
+``` javascript
+form.submit({
+  host: 'example.com',
+  path: '/surelynot.php',
+  headers: {'x-test-header': 'test-header-value'}
+}, function(err, res) {
+  console.log(res.statusCode);
+});
+```
+
+### Methods
+
+- [_Void_ append( **String** _field_, **Mixed** _value_ [, **Mixed** _options_] )](https://github.com/form-data/form-data#void-append-string-field-mixed-value--mixed-options-).
+- [_Headers_ getHeaders( [**Headers** _userHeaders_] )](https://github.com/form-data/form-data#array-getheaders-array-userheaders-)
+- [_String_ getBoundary()](https://github.com/form-data/form-data#string-getboundary)
+- [_Void_ setBoundary()](https://github.com/form-data/form-data#void-setboundary)
+- [_Buffer_ getBuffer()](https://github.com/form-data/form-data#buffer-getbuffer)
+- [_Integer_ getLengthSync()](https://github.com/form-data/form-data#integer-getlengthsync)
+- [_Integer_ getLength( **function** _callback_ )](https://github.com/form-data/form-data#integer-getlength-function-callback-)
+- [_Boolean_ hasKnownLength()](https://github.com/form-data/form-data#boolean-hasknownlength)
+- [_Request_ submit( _params_, **function** _callback_ )](https://github.com/form-data/form-data#request-submit-params-function-callback-)
+- [_String_ toString()](https://github.com/form-data/form-data#string-tostring)
+
+#### _Void_ append( **String** _field_, **Mixed** _value_ [, **Mixed** _options_] )
+Append data to the form. You can submit about any format (string, integer, boolean, buffer, etc.). However, Arrays are not supported and need to be turned into strings by the user.
+```javascript
+var form = new FormData();
+form.append( 'my_string', 'my value' );
+form.append( 'my_integer', 1 );
+form.append( 'my_boolean', true );
+form.append( 'my_buffer', new Buffer(10) );
+form.append( 'my_array_as_json', JSON.stringify( ['bird','cute'] ) )
+```
+
+You may provide a string for options, or an object.
+```javascript
+// Set filename by providing a string for options
+form.append( 'my_file', fs.createReadStream('/foo/bar.jpg'), 'bar.jpg' );
+
+// provide an object.
+form.append( 'my_file', fs.createReadStream('/foo/bar.jpg'), {filename: 'bar.jpg', contentType: 'image/jpeg', knownLength: 19806} );
+```
+
+#### _Headers_ getHeaders( [**Headers** _userHeaders_] )
+This method adds the correct `content-type` header to the provided array of `userHeaders`.
+
+#### _String_ getBoundary()
+Return the boundary of the formData. By default, the boundary consists of 26 `-` followed by 24 numbers
+for example:
+```javascript
+--------------------------515890814546601021194782
+```
+
+#### _Void_ setBoundary(String _boundary_)
+Set the boundary string, overriding the default behavior described above.
+
+_Note: The boundary must be unique and may not appear in the data._
+
+#### _Buffer_ getBuffer()
+Return the full formdata request package, as a Buffer. You can insert this Buffer in e.g. Axios to send multipart data.
+```javascript
+var form = new FormData();
+form.append( 'my_buffer', Buffer.from([0x4a,0x42,0x20,0x52,0x6f,0x63,0x6b,0x73]) );
+form.append( 'my_file', fs.readFileSync('/foo/bar.jpg') );
+
+axios.post( 'https://example.com/path/to/api',
+            form.getBuffer(),
+            form.getHeaders()
+          )
+```
+**Note:** Because the output is of type Buffer, you can only append types that are accepted by Buffer: *string, Buffer, ArrayBuffer, Array, or Array-like Object*. A ReadStream for example will result in an error.
+
+#### _Integer_ getLengthSync()
+Same as `getLength` but synchronous.
+
+_Note: getLengthSync __doesn't__ calculate streams length._
+
+#### _Integer_ getLength( **function** _callback_ )
+Returns the `Content-Length` async. The callback is used to handle errors and continue once the length has been calculated
+```javascript
+this.getLength(function(err, length) {
+  if (err) {
+    this._error(err);
+    return;
+  }
+
+  // add content length
+  request.setHeader('Content-Length', length);
+
+  ...
+}.bind(this));
+```
+
+#### _Boolean_ hasKnownLength()
+Checks if the length of added values is known.
+
+#### _Request_ submit( _params_, **function** _callback_ )
+Submit the form to a web application.
+```javascript
+var form = new FormData();
+form.append( 'my_string', 'Hello World' );
+
+form.submit( 'http://example.com/', function(err, res) {
+  // res – response object (http.IncomingMessage)  //
+  res.resume();
+} );
+```
+
+#### _String_ toString()
+Returns the form data as a string. Don't use this if you are sending files or buffers, use `getBuffer()` instead.
+
+### Integration with other libraries
+
+#### Request
+
+Form submission using  [request](https://github.com/request/request):
+
+```javascript
+var formData = {
+  my_field: 'my_value',
+  my_file: fs.createReadStream(__dirname + '/unicycle.jpg'),
+};
+
+request.post({url:'http://service.com/upload', formData: formData}, function(err, httpResponse, body) {
+  if (err) {
+    return console.error('upload failed:', err);
+  }
+  console.log('Upload successful!  Server responded with:', body);
+});
+```
+
+For more details see [request readme](https://github.com/request/request#multipartform-data-multipart-form-uploads).
+
+#### node-fetch
+
+You can also submit a form using [node-fetch](https://github.com/bitinn/node-fetch):
+
+```javascript
+var form = new FormData();
+
+form.append('a', 1);
+
+fetch('http://example.com', { method: 'POST', body: form })
+    .then(function(res) {
+        return res.json();
+    }).then(function(json) {
+        console.log(json);
+    });
+```
+
+#### axios
+
+In Node.js you can post a file using [axios](https://github.com/axios/axios):
+```javascript
+const form = new FormData();
+const stream = fs.createReadStream(PATH_TO_FILE);
+
+form.append('image', stream);
+
+// In Node.js environment you need to set boundary in the header field 'Content-Type' by calling method `getHeaders`
+const formHeaders = form.getHeaders();
+
+axios.post('http://example.com', form, {
+  headers: {
+    ...formHeaders,
+  },
+})
+.then(response => response)
+.catch(error => error)
+```
+
+## Notes
+
+- ```getLengthSync()``` method DOESN'T calculate length for streams, use ```knownLength``` options as workaround.
+- ```getLength(cb)``` will send an error as first parameter of callback if stream length cannot be calculated (e.g. send in custom streams w/o using ```knownLength```).
+- ```submit``` will not add `content-length` if form length is unknown or not calculable.
+- Starting version `2.x` FormData has dropped support for `node@0.10.x`.
+- Starting version `3.x` FormData has dropped support for `node@4.x`.
+
+## License
+
+Form-Data is released under the [MIT](License) license.
diff --git a/tortoise/node_modules/form-data/index.d.ts b/tortoise/node_modules/form-data/index.d.ts
new file mode 100644
index 0000000..295e9e9
--- /dev/null
+++ b/tortoise/node_modules/form-data/index.d.ts
@@ -0,0 +1,62 @@
+// Definitions by: Carlos Ballesteros Velasco <https://github.com/soywiz>
+//                 Leon Yu <https://github.com/leonyu>
+//                 BendingBender <https://github.com/BendingBender>
+//                 Maple Miao <https://github.com/mapleeit>
+
+/// <reference types="node" />
+import * as stream from 'stream';
+import * as http from 'http';
+
+export = FormData;
+
+// Extracted because @types/node doesn't export interfaces.
+interface ReadableOptions {
+  highWaterMark?: number;
+  encoding?: string;
+  objectMode?: boolean;
+  read?(this: stream.Readable, size: number): void;
+  destroy?(this: stream.Readable, error: Error | null, callback: (error: Error | null) => void): void;
+  autoDestroy?: boolean;
+}
+
+interface Options extends ReadableOptions {
+  writable?: boolean;
+  readable?: boolean;
+  dataSize?: number;
+  maxDataSize?: number;
+  pauseStreams?: boolean;
+}
+
+declare class FormData extends stream.Readable {
+  constructor(options?: Options);
+  append(key: string, value: any, options?: FormData.AppendOptions | string): void;
+  getHeaders(userHeaders?: FormData.Headers): FormData.Headers;
+  submit(
+    params: string | FormData.SubmitOptions,
+    callback?: (error: Error | null, response: http.IncomingMessage) => void
+  ): http.ClientRequest;
+  getBuffer(): Buffer;
+  setBoundary(boundary: string): void;
+  getBoundary(): string;
+  getLength(callback: (err: Error | null, length: number) => void): void;
+  getLengthSync(): number;
+  hasKnownLength(): boolean;
+}
+
+declare namespace FormData {
+  interface Headers {
+    [key: string]: any;
+  }
+
+  interface AppendOptions {
+    header?: string | Headers;
+    knownLength?: number;
+    filename?: string;
+    filepath?: string;
+    contentType?: string;
+  }
+
+  interface SubmitOptions extends http.RequestOptions {
+    protocol?: 'https:' | 'http:';
+  }
+}
diff --git a/tortoise/node_modules/form-data/package.json b/tortoise/node_modules/form-data/package.json
new file mode 100644
index 0000000..9bd12c9
--- /dev/null
+++ b/tortoise/node_modules/form-data/package.json
@@ -0,0 +1,71 @@
+{
+  "author": "Felix Geisendörfer <felix@debuggable.com> (http://debuggable.com/)",
+  "name": "form-data",
+  "description": "A library to create readable \"multipart/form-data\" streams. Can be used to submit forms and file uploads to other web applications.",
+  "version": "4.0.1",
+  "repository": {
+    "type": "git",
+    "url": "git://github.com/form-data/form-data.git"
+  },
+  "main": "./lib/form_data",
+  "browser": "./lib/browser",
+  "typings": "./index.d.ts",
+  "scripts": {
+    "pretest": "npm run lint",
+    "pretests-only": "rimraf coverage test/tmp",
+    "tests-only": "istanbul cover test/run.js",
+    "posttests-only": "istanbul report lcov text",
+    "test": "npm run tests-only",
+    "posttest": "npx npm@'>=10.2' audit --production",
+    "lint": "eslint --ext=js,mjs .",
+    "report": "istanbul report lcov text",
+    "ci-lint": "is-node-modern 8 && npm run lint || is-node-not-modern 8",
+    "ci-test": "npm run tests-only && npm run browser && npm run report",
+    "predebug": "rimraf coverage test/tmp",
+    "debug": "verbose=1 ./test/run.js",
+    "browser": "browserify -t browserify-istanbul test/run-browser.js | obake --coverage",
+    "check": "istanbul check-coverage coverage/coverage*.json",
+    "files": "pkgfiles --sort=name",
+    "get-version": "node -e \"console.log(require('./package.json').version)\"",
+    "update-readme": "sed -i.bak 's/\\/master\\.svg/\\/v'$(npm --silent run get-version)'.svg/g' README.md",
+    "restore-readme": "mv README.md.bak README.md",
+    "prepublish": "in-publish && npm run update-readme || not-in-publish",
+    "postpublish": "npm run restore-readme"
+  },
+  "pre-commit": [
+    "lint",
+    "ci-test",
+    "check"
+  ],
+  "engines": {
+    "node": ">= 6"
+  },
+  "dependencies": {
+    "asynckit": "^0.4.0",
+    "combined-stream": "^1.0.8",
+    "mime-types": "^2.1.12"
+  },
+  "devDependencies": {
+    "@types/node": "^12.0.10",
+    "browserify": "^13.1.1",
+    "browserify-istanbul": "^2.0.0",
+    "coveralls": "^3.0.4",
+    "cross-spawn": "^6.0.5",
+    "eslint": "^6.0.1",
+    "fake": "^0.2.2",
+    "far": "^0.0.7",
+    "formidable": "^1.0.17",
+    "in-publish": "^2.0.0",
+    "is-node-modern": "^1.0.0",
+    "istanbul": "^0.4.5",
+    "obake": "^0.1.2",
+    "puppeteer": "^1.19.0",
+    "pkgfiles": "^2.3.0",
+    "pre-commit": "^1.1.3",
+    "request": "^2.88.0",
+    "rimraf": "^2.7.1",
+    "tape": "^4.6.2",
+    "typescript": "^3.5.2"
+  },
+  "license": "MIT"
+}
diff --git a/tortoise/node_modules/mime-db/HISTORY.md b/tortoise/node_modules/mime-db/HISTORY.md
new file mode 100644
index 0000000..7436f64
--- /dev/null
+++ b/tortoise/node_modules/mime-db/HISTORY.md
@@ -0,0 +1,507 @@
+1.52.0 / 2022-02-21
+===================
+
+  * Add extensions from IANA for more `image/*` types
+  * Add extension `.asc` to `application/pgp-keys`
+  * Add extensions to various XML types
+  * Add new upstream MIME types
+
+1.51.0 / 2021-11-08
+===================
+
+  * Add new upstream MIME types
+  * Mark `image/vnd.microsoft.icon` as compressible
+  * Mark `image/vnd.ms-dds` as compressible
+
+1.50.0 / 2021-09-15
+===================
+
+  * Add deprecated iWorks mime types and extensions
+  * Add new upstream MIME types
+
+1.49.0 / 2021-07-26
+===================
+
+  * Add extension `.trig` to `application/trig`
+  * Add new upstream MIME types
+
+1.48.0 / 2021-05-30
+===================
+
+  * Add extension `.mvt` to `application/vnd.mapbox-vector-tile`
+  * Add new upstream MIME types
+  * Mark `text/yaml` as compressible
+
+1.47.0 / 2021-04-01
+===================
+
+  * Add new upstream MIME types
+  * Remove ambigious extensions from IANA for `application/*+xml` types
+  * Update primary extension to `.es` for `application/ecmascript`
+
+1.46.0 / 2021-02-13
+===================
+
+  * Add extension `.amr` to `audio/amr`
+  * Add extension `.m4s` to `video/iso.segment`
+  * Add extension `.opus` to `audio/ogg`
+  * Add new upstream MIME types
+
+1.45.0 / 2020-09-22
+===================
+
+  * Add `application/ubjson` with extension `.ubj`
+  * Add `image/avif` with extension `.avif`
+  * Add `image/ktx2` with extension `.ktx2`
+  * Add extension `.dbf` to `application/vnd.dbf`
+  * Add extension `.rar` to `application/vnd.rar`
+  * Add extension `.td` to `application/urc-targetdesc+xml`
+  * Add new upstream MIME types
+  * Fix extension of `application/vnd.apple.keynote` to be `.key`
+
+1.44.0 / 2020-04-22
+===================
+
+  * Add charsets from IANA
+  * Add extension `.cjs` to `application/node`
+  * Add new upstream MIME types
+
+1.43.0 / 2020-01-05
+===================
+
+  * Add `application/x-keepass2` with extension `.kdbx`
+  * Add extension `.mxmf` to `audio/mobile-xmf`
+  * Add extensions from IANA for `application/*+xml` types
+  * Add new upstream MIME types
+
+1.42.0 / 2019-09-25
+===================
+
+  * Add `image/vnd.ms-dds` with extension `.dds`
+  * Add new upstream MIME types
+  * Remove compressible from `multipart/mixed`
+
+1.41.0 / 2019-08-30
+===================
+
+  * Add new upstream MIME types
+  * Add `application/toml` with extension `.toml`
+  * Mark `font/ttf` as compressible
+
+1.40.0 / 2019-04-20
+===================
+
+  * Add extensions from IANA for `model/*` types
+  * Add `text/mdx` with extension `.mdx`
+
+1.39.0 / 2019-04-04
+===================
+
+  * Add extensions `.siv` and `.sieve` to `application/sieve`
+  * Add new upstream MIME types
+
+1.38.0 / 2019-02-04
+===================
+
+  * Add extension `.nq` to `application/n-quads`
+  * Add extension `.nt` to `application/n-triples`
+  * Add new upstream MIME types
+  * Mark `text/less` as compressible
+
+1.37.0 / 2018-10-19
+===================
+
+  * Add extensions to HEIC image types
+  * Add new upstream MIME types
+
+1.36.0 / 2018-08-20
+===================
+
+  * Add Apple file extensions from IANA
+  * Add extensions from IANA for `image/*` types
+  * Add new upstream MIME types
+
+1.35.0 / 2018-07-15
+===================
+
+  * Add extension `.owl` to `application/rdf+xml`
+  * Add new upstream MIME types
+    - Removes extension `.woff` from `application/font-woff`
+
+1.34.0 / 2018-06-03
+===================
+
+  * Add extension `.csl` to `application/vnd.citationstyles.style+xml`
+  * Add extension `.es` to `application/ecmascript`
+  * Add new upstream MIME types
+  * Add `UTF-8` as default charset for `text/turtle`
+  * Mark all XML-derived types as compressible
+
+1.33.0 / 2018-02-15
+===================
+
+  * Add extensions from IANA for `message/*` types
+  * Add new upstream MIME types
+  * Fix some incorrect OOXML types
+  * Remove `application/font-woff2`
+
+1.32.0 / 2017-11-29
+===================
+
+  * Add new upstream MIME types
+  * Update `text/hjson` to registered `application/hjson`
+  * Add `text/shex` with extension `.shex`
+
+1.31.0 / 2017-10-25
+===================
+
+  * Add `application/raml+yaml` with extension `.raml`
+  * Add `application/wasm` with extension `.wasm`
+  * Add new `font` type from IANA
+  * Add new upstream font extensions
+  * Add new upstream MIME types
+  * Add extensions for JPEG-2000 images
+
+1.30.0 / 2017-08-27
+===================
+
+  * Add `application/vnd.ms-outlook`
+  * Add `application/x-arj`
+  * Add extension `.mjs` to `application/javascript`
+  * Add glTF types and extensions
+  * Add new upstream MIME types
+  * Add `text/x-org`
+  * Add VirtualBox MIME types
+  * Fix `source` records for `video/*` types that are IANA
+  * Update `font/opentype` to registered `font/otf`
+
+1.29.0 / 2017-07-10
+===================
+
+  * Add `application/fido.trusted-apps+json`
+  * Add extension `.wadl` to `application/vnd.sun.wadl+xml`
+  * Add new upstream MIME types
+  * Add `UTF-8` as default charset for `text/css`
+
+1.28.0 / 2017-05-14
+===================
+
+  * Add new upstream MIME types
+  * Add extension `.gz` to `application/gzip`
+  * Update extensions `.md` and `.markdown` to be `text/markdown`
+
+1.27.0 / 2017-03-16
+===================
+
+  * Add new upstream MIME types
+  * Add `image/apng` with extension `.apng`
+
+1.26.0 / 2017-01-14
+===================
+
+  * Add new upstream MIME types
+  * Add extension `.geojson` to `application/geo+json`
+
+1.25.0 / 2016-11-11
+===================
+
+  * Add new upstream MIME types
+
+1.24.0 / 2016-09-18
+===================
+
+  * Add `audio/mp3`
+  * Add new upstream MIME types
+
+1.23.0 / 2016-05-01
+===================
+
+  * Add new upstream MIME types
+  * Add extension `.3gpp` to `audio/3gpp`
+
+1.22.0 / 2016-02-15
+===================
+
+  * Add `text/slim`
+  * Add extension `.rng` to `application/xml`
+  * Add new upstream MIME types
+  * Fix extension of `application/dash+xml` to be `.mpd`
+  * Update primary extension to `.m4a` for `audio/mp4`
+
+1.21.0 / 2016-01-06
+===================
+
+  * Add Google document types
+  * Add new upstream MIME types
+
+1.20.0 / 2015-11-10
+===================
+
+  * Add `text/x-suse-ymp`
+  * Add new upstream MIME types
+
+1.19.0 / 2015-09-17
+===================
+
+  * Add `application/vnd.apple.pkpass`
+  * Add new upstream MIME types
+
+1.18.0 / 2015-09-03
+===================
+
+  * Add new upstream MIME types
+
+1.17.0 / 2015-08-13
+===================
+
+  * Add `application/x-msdos-program`
+  * Add `audio/g711-0`
+  * Add `image/vnd.mozilla.apng`
+  * Add extension `.exe` to `application/x-msdos-program`
+
+1.16.0 / 2015-07-29
+===================
+
+  * Add `application/vnd.uri-map`
+
+1.15.0 / 2015-07-13
+===================
+
+  * Add `application/x-httpd-php`
+
+1.14.0 / 2015-06-25
+===================
+
+  * Add `application/scim+json`
+  * Add `application/vnd.3gpp.ussd+xml`
+  * Add `application/vnd.biopax.rdf+xml`
+  * Add `text/x-processing`
+
+1.13.0 / 2015-06-07
+===================
+
+  * Add nginx as a source
+  * Add `application/x-cocoa`
+  * Add `application/x-java-archive-diff`
+  * Add `application/x-makeself`
+  * Add `application/x-perl`
+  * Add `application/x-pilot`
+  * Add `application/x-redhat-package-manager`
+  * Add `application/x-sea`
+  * Add `audio/x-m4a`
+  * Add `audio/x-realaudio`
+  * Add `image/x-jng`
+  * Add `text/mathml`
+
+1.12.0 / 2015-06-05
+===================
+
+  * Add `application/bdoc`
+  * Add `application/vnd.hyperdrive+json`
+  * Add `application/x-bdoc`
+  * Add extension `.rtf` to `text/rtf`
+
+1.11.0 / 2015-05-31
+===================
+
+  * Add `audio/wav`
+  * Add `audio/wave`
+  * Add extension `.litcoffee` to `text/coffeescript`
+  * Add extension `.sfd-hdstx` to `application/vnd.hydrostatix.sof-data`
+  * Add extension `.n-gage` to `application/vnd.nokia.n-gage.symbian.install`
+
+1.10.0 / 2015-05-19
+===================
+
+  * Add `application/vnd.balsamiq.bmpr`
+  * Add `application/vnd.microsoft.portable-executable`
+  * Add `application/x-ns-proxy-autoconfig`
+
+1.9.1 / 2015-04-19
+==================
+
+  * Remove `.json` extension from `application/manifest+json`
+    - This is causing bugs downstream
+
+1.9.0 / 2015-04-19
+==================
+
+  * Add `application/manifest+json`
+  * Add `application/vnd.micro+json`
+  * Add `image/vnd.zbrush.pcx`
+  * Add `image/x-ms-bmp`
+
+1.8.0 / 2015-03-13
+==================
+
+  * Add `application/vnd.citationstyles.style+xml`
+  * Add `application/vnd.fastcopy-disk-image`
+  * Add `application/vnd.gov.sk.xmldatacontainer+xml`
+  * Add extension `.jsonld` to `application/ld+json`
+
+1.7.0 / 2015-02-08
+==================
+
+  * Add `application/vnd.gerber`
+  * Add `application/vnd.msa-disk-image`
+
+1.6.1 / 2015-02-05
+==================
+
+  * Community extensions ownership transferred from `node-mime`
+
+1.6.0 / 2015-01-29
+==================
+
+  * Add `application/jose`
+  * Add `application/jose+json`
+  * Add `application/json-seq`
+  * Add `application/jwk+json`
+  * Add `application/jwk-set+json`
+  * Add `application/jwt`
+  * Add `application/rdap+json`
+  * Add `application/vnd.gov.sk.e-form+xml`
+  * Add `application/vnd.ims.imsccv1p3`
+
+1.5.0 / 2014-12-30
+==================
+
+  * Add `application/vnd.oracle.resource+json`
+  * Fix various invalid MIME type entries
+    - `application/mbox+xml`
+    - `application/oscp-response`
+    - `application/vwg-multiplexed`
+    - `audio/g721`
+
+1.4.0 / 2014-12-21
+==================
+
+  * Add `application/vnd.ims.imsccv1p2`
+  * Fix various invalid MIME type entries
+    - `application/vnd-acucobol`
+    - `application/vnd-curl`
+    - `application/vnd-dart`
+    - `application/vnd-dxr`
+    - `application/vnd-fdf`
+    - `application/vnd-mif`
+    - `application/vnd-sema`
+    - `application/vnd-wap-wmlc`
+    - `application/vnd.adobe.flash-movie`
+    - `application/vnd.dece-zip`
+    - `application/vnd.dvb_service`
+    - `application/vnd.micrografx-igx`
+    - `application/vnd.sealed-doc`
+    - `application/vnd.sealed-eml`
+    - `application/vnd.sealed-mht`
+    - `application/vnd.sealed-ppt`
+    - `application/vnd.sealed-tiff`
+    - `application/vnd.sealed-xls`
+    - `application/vnd.sealedmedia.softseal-html`
+    - `application/vnd.sealedmedia.softseal-pdf`
+    - `application/vnd.wap-slc`
+    - `application/vnd.wap-wbxml`
+    - `audio/vnd.sealedmedia.softseal-mpeg`
+    - `image/vnd-djvu`
+    - `image/vnd-svf`
+    - `image/vnd-wap-wbmp`
+    - `image/vnd.sealed-png`
+    - `image/vnd.sealedmedia.softseal-gif`
+    - `image/vnd.sealedmedia.softseal-jpg`
+    - `model/vnd-dwf`
+    - `model/vnd.parasolid.transmit-binary`
+    - `model/vnd.parasolid.transmit-text`
+    - `text/vnd-a`
+    - `text/vnd-curl`
+    - `text/vnd.wap-wml`
+  * Remove example template MIME types
+    - `application/example`
+    - `audio/example`
+    - `image/example`
+    - `message/example`
+    - `model/example`
+    - `multipart/example`
+    - `text/example`
+    - `video/example`
+
+1.3.1 / 2014-12-16
+==================
+
+  * Fix missing extensions
+    - `application/json5`
+    - `text/hjson`
+
+1.3.0 / 2014-12-07
+==================
+
+  * Add `application/a2l`
+  * Add `application/aml`
+  * Add `application/atfx`
+  * Add `application/atxml`
+  * Add `application/cdfx+xml`
+  * Add `application/dii`
+  * Add `application/json5`
+  * Add `application/lxf`
+  * Add `application/mf4`
+  * Add `application/vnd.apache.thrift.compact`
+  * Add `application/vnd.apache.thrift.json`
+  * Add `application/vnd.coffeescript`
+  * Add `application/vnd.enphase.envoy`
+  * Add `application/vnd.ims.imsccv1p1`
+  * Add `text/csv-schema`
+  * Add `text/hjson`
+  * Add `text/markdown`
+  * Add `text/yaml`
+
+1.2.0 / 2014-11-09
+==================
+
+  * Add `application/cea`
+  * Add `application/dit`
+  * Add `application/vnd.gov.sk.e-form+zip`
+  * Add `application/vnd.tmd.mediaflex.api+xml`
+  * Type `application/epub+zip` is now IANA-registered
+
+1.1.2 / 2014-10-23
+==================
+
+  * Rebuild database for `application/x-www-form-urlencoded` change
+
+1.1.1 / 2014-10-20
+==================
+
+  * Mark `application/x-www-form-urlencoded` as compressible.
+
+1.1.0 / 2014-09-28
+==================
+
+  * Add `application/font-woff2`
+
+1.0.3 / 2014-09-25
+==================
+
+  * Fix engine requirement in package
+
+1.0.2 / 2014-09-25
+==================
+
+  * Add `application/coap-group+json`
+  * Add `application/dcd`
+  * Add `application/vnd.apache.thrift.binary`
+  * Add `image/vnd.tencent.tap`
+  * Mark all JSON-derived types as compressible
+  * Update `text/vtt` data
+
+1.0.1 / 2014-08-30
+==================
+
+  * Fix extension ordering
+
+1.0.0 / 2014-08-30
+==================
+
+  * Add `application/atf`
+  * Add `application/merge-patch+json`
+  * Add `multipart/x-mixed-replace`
+  * Add `source: 'apache'` metadata
+  * Add `source: 'iana'` metadata
+  * Remove badly-assumed charset data
diff --git a/tortoise/node_modules/mime-db/LICENSE b/tortoise/node_modules/mime-db/LICENSE
new file mode 100644
index 0000000..0751cb1
--- /dev/null
+++ b/tortoise/node_modules/mime-db/LICENSE
@@ -0,0 +1,23 @@
+(The MIT License)
+
+Copyright (c) 2014 Jonathan Ong <me@jongleberry.com>
+Copyright (c) 2015-2022 Douglas Christopher Wilson <doug@somethingdoug.com>
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+'Software'), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/tortoise/node_modules/mime-db/README.md b/tortoise/node_modules/mime-db/README.md
new file mode 100644
index 0000000..5a8fcfe
--- /dev/null
+++ b/tortoise/node_modules/mime-db/README.md
@@ -0,0 +1,100 @@
+# mime-db
+
+[![NPM Version][npm-version-image]][npm-url]
+[![NPM Downloads][npm-downloads-image]][npm-url]
+[![Node.js Version][node-image]][node-url]
+[![Build Status][ci-image]][ci-url]
+[![Coverage Status][coveralls-image]][coveralls-url]
+
+This is a large database of mime types and information about them.
+It consists of a single, public JSON file and does not include any logic,
+allowing it to remain as un-opinionated as possible with an API.
+It aggregates data from the following sources:
+
+- http://www.iana.org/assignments/media-types/media-types.xhtml
+- http://svn.apache.org/repos/asf/httpd/httpd/trunk/docs/conf/mime.types
+- http://hg.nginx.org/nginx/raw-file/default/conf/mime.types
+
+## Installation
+
+```bash
+npm install mime-db
+```
+
+### Database Download
+
+If you're crazy enough to use this in the browser, you can just grab the
+JSON file using [jsDelivr](https://www.jsdelivr.com/). It is recommended to
+replace `master` with [a release tag](https://github.com/jshttp/mime-db/tags)
+as the JSON format may change in the future.
+
+```
+https://cdn.jsdelivr.net/gh/jshttp/mime-db@master/db.json
+```
+
+## Usage
+
+```js
+var db = require('mime-db')
+
+// grab data on .js files
+var data = db['application/javascript']
+```
+
+## Data Structure
+
+The JSON file is a map lookup for lowercased mime types.
+Each mime type has the following properties:
+
+- `.source` - where the mime type is defined.
+    If not set, it's probably a custom media type.
+    - `apache` - [Apache common media types](http://svn.apache.org/repos/asf/httpd/httpd/trunk/docs/conf/mime.types)
+    - `iana` - [IANA-defined media types](http://www.iana.org/assignments/media-types/media-types.xhtml)
+    - `nginx` - [nginx media types](http://hg.nginx.org/nginx/raw-file/default/conf/mime.types)
+- `.extensions[]` - known extensions associated with this mime type.
+- `.compressible` - whether a file of this type can be gzipped.
+- `.charset` - the default charset associated with this type, if any.
+
+If unknown, every property could be `undefined`.
+
+## Contributing
+
+To edit the database, only make PRs against `src/custom-types.json` or
+`src/custom-suffix.json`.
+
+The `src/custom-types.json` file is a JSON object with the MIME type as the
+keys and the values being an object with the following keys:
+
+- `compressible` - leave out if you don't know, otherwise `true`/`false` to
+  indicate whether the data represented by the type is typically compressible.
+- `extensions` - include an array of file extensions that are associated with
+  the type.
+- `notes` - human-readable notes about the type, typically what the type is.
+- `sources` - include an array of URLs of where the MIME type and the associated
+  extensions are sourced from. This needs to be a [primary source](https://en.wikipedia.org/wiki/Primary_source);
+  links to type aggregating sites and Wikipedia are _not acceptable_.
+
+To update the build, run `npm run build`.
+
+### Adding Custom Media Types
+
+The best way to get new media types included in this library is to register
+them with the IANA. The community registration procedure is outlined in
+[RFC 6838 section 5](http://tools.ietf.org/html/rfc6838#section-5). Types
+registered with the IANA are automatically pulled into this library.
+
+If that is not possible / feasible, they can be added directly here as a
+"custom" type. To do this, it is required to have a primary source that
+definitively lists the media type. If an extension is going to be listed as
+associateed with this media type, the source must definitively link the
+media type and extension as well.
+
+[ci-image]: https://badgen.net/github/checks/jshttp/mime-db/master?label=ci
+[ci-url]: https://github.com/jshttp/mime-db/actions?query=workflow%3Aci
+[coveralls-image]: https://badgen.net/coveralls/c/github/jshttp/mime-db/master
+[coveralls-url]: https://coveralls.io/r/jshttp/mime-db?branch=master
+[node-image]: https://badgen.net/npm/node/mime-db
+[node-url]: https://nodejs.org/en/download
+[npm-downloads-image]: https://badgen.net/npm/dm/mime-db
+[npm-url]: https://npmjs.org/package/mime-db
+[npm-version-image]: https://badgen.net/npm/v/mime-db
diff --git a/tortoise/node_modules/mime-db/db.json b/tortoise/node_modules/mime-db/db.json
new file mode 100644
index 0000000..eb9c42c
--- /dev/null
+++ b/tortoise/node_modules/mime-db/db.json
@@ -0,0 +1,8519 @@
+{
+  "application/1d-interleaved-parityfec": {
+    "source": "iana"
+  },
+  "application/3gpdash-qoe-report+xml": {
+    "source": "iana",
+    "charset": "UTF-8",
+    "compressible": true
+  },
+  "application/3gpp-ims+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/3gpphal+json": {
+    "source": "iana",
+    "compressible": true
+  },
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+  "application/cbor-seq": {
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+  "application/cea": {
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+  "application/city+json": {
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+  "application/coap-group+json": {
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+  "application/commonground": {
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+    "extensions": ["cpl"]
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+    "extensions": ["dbk"]
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+    "extensions": ["emma"]
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+    "extensions": ["emotionml"]
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+    "extensions": ["epub"]
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+    "extensions": ["its"]
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+    "extensions": ["class"]
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+    "extensions": ["js","mjs"]
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+    "extensions": ["jsonml"]
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+  "application/ld+json": {
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+    "extensions": ["jsonld"]
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+    "extensions": ["lgr"]
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+  "application/load-control+xml": {
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+  "application/lost+xml": {
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+    "extensions": ["lostxml"]
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+  "application/lostsync+xml": {
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+  "application/mac-binhex40": {
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+    "extensions": ["webmanifest"]
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+    "extensions": ["mrcx"]
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+  "application/mbms-associated-procedure-description+xml": {
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+  "application/mbms-protection-description+xml": {
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+  "application/mbms-reception-report+xml": {
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+  "application/mbms-schedule+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/mbms-user-service-description+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/mbox": {
+    "source": "iana",
+    "extensions": ["mbox"]
+  },
+  "application/media-policy-dataset+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["mpf"]
+  },
+  "application/media_control+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/mediaservercontrol+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["mscml"]
+  },
+  "application/merge-patch+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/metalink+xml": {
+    "source": "apache",
+    "compressible": true,
+    "extensions": ["metalink"]
+  },
+  "application/metalink4+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["meta4"]
+  },
+  "application/mets+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["mets"]
+  },
+  "application/mf4": {
+    "source": "iana"
+  },
+  "application/mikey": {
+    "source": "iana"
+  },
+  "application/mipc": {
+    "source": "iana"
+  },
+  "application/missing-blocks+cbor-seq": {
+    "source": "iana"
+  },
+  "application/mmt-aei+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["maei"]
+  },
+  "application/mmt-usd+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["musd"]
+  },
+  "application/mods+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["mods"]
+  },
+  "application/moss-keys": {
+    "source": "iana"
+  },
+  "application/moss-signature": {
+    "source": "iana"
+  },
+  "application/mosskey-data": {
+    "source": "iana"
+  },
+  "application/mosskey-request": {
+    "source": "iana"
+  },
+  "application/mp21": {
+    "source": "iana",
+    "extensions": ["m21","mp21"]
+  },
+  "application/mp4": {
+    "source": "iana",
+    "extensions": ["mp4s","m4p"]
+  },
+  "application/mpeg4-generic": {
+    "source": "iana"
+  },
+  "application/mpeg4-iod": {
+    "source": "iana"
+  },
+  "application/mpeg4-iod-xmt": {
+    "source": "iana"
+  },
+  "application/mrb-consumer+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/mrb-publish+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/msc-ivr+xml": {
+    "source": "iana",
+    "charset": "UTF-8",
+    "compressible": true
+  },
+  "application/msc-mixer+xml": {
+    "source": "iana",
+    "charset": "UTF-8",
+    "compressible": true
+  },
+  "application/msword": {
+    "source": "iana",
+    "compressible": false,
+    "extensions": ["doc","dot"]
+  },
+  "application/mud+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/multipart-core": {
+    "source": "iana"
+  },
+  "application/mxf": {
+    "source": "iana",
+    "extensions": ["mxf"]
+  },
+  "application/n-quads": {
+    "source": "iana",
+    "extensions": ["nq"]
+  },
+  "application/n-triples": {
+    "source": "iana",
+    "extensions": ["nt"]
+  },
+  "application/nasdata": {
+    "source": "iana"
+  },
+  "application/news-checkgroups": {
+    "source": "iana",
+    "charset": "US-ASCII"
+  },
+  "application/news-groupinfo": {
+    "source": "iana",
+    "charset": "US-ASCII"
+  },
+  "application/news-transmission": {
+    "source": "iana"
+  },
+  "application/nlsml+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/node": {
+    "source": "iana",
+    "extensions": ["cjs"]
+  },
+  "application/nss": {
+    "source": "iana"
+  },
+  "application/oauth-authz-req+jwt": {
+    "source": "iana"
+  },
+  "application/oblivious-dns-message": {
+    "source": "iana"
+  },
+  "application/ocsp-request": {
+    "source": "iana"
+  },
+  "application/ocsp-response": {
+    "source": "iana"
+  },
+  "application/octet-stream": {
+    "source": "iana",
+    "compressible": false,
+    "extensions": ["bin","dms","lrf","mar","so","dist","distz","pkg","bpk","dump","elc","deploy","exe","dll","deb","dmg","iso","img","msi","msp","msm","buffer"]
+  },
+  "application/oda": {
+    "source": "iana",
+    "extensions": ["oda"]
+  },
+  "application/odm+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/odx": {
+    "source": "iana"
+  },
+  "application/oebps-package+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["opf"]
+  },
+  "application/ogg": {
+    "source": "iana",
+    "compressible": false,
+    "extensions": ["ogx"]
+  },
+  "application/omdoc+xml": {
+    "source": "apache",
+    "compressible": true,
+    "extensions": ["omdoc"]
+  },
+  "application/onenote": {
+    "source": "apache",
+    "extensions": ["onetoc","onetoc2","onetmp","onepkg"]
+  },
+  "application/opc-nodeset+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/oscore": {
+    "source": "iana"
+  },
+  "application/oxps": {
+    "source": "iana",
+    "extensions": ["oxps"]
+  },
+  "application/p21": {
+    "source": "iana"
+  },
+  "application/p21+zip": {
+    "source": "iana",
+    "compressible": false
+  },
+  "application/p2p-overlay+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["relo"]
+  },
+  "application/parityfec": {
+    "source": "iana"
+  },
+  "application/passport": {
+    "source": "iana"
+  },
+  "application/patch-ops-error+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["xer"]
+  },
+  "application/pdf": {
+    "source": "iana",
+    "compressible": false,
+    "extensions": ["pdf"]
+  },
+  "application/pdx": {
+    "source": "iana"
+  },
+  "application/pem-certificate-chain": {
+    "source": "iana"
+  },
+  "application/pgp-encrypted": {
+    "source": "iana",
+    "compressible": false,
+    "extensions": ["pgp"]
+  },
+  "application/pgp-keys": {
+    "source": "iana",
+    "extensions": ["asc"]
+  },
+  "application/pgp-signature": {
+    "source": "iana",
+    "extensions": ["asc","sig"]
+  },
+  "application/pics-rules": {
+    "source": "apache",
+    "extensions": ["prf"]
+  },
+  "application/pidf+xml": {
+    "source": "iana",
+    "charset": "UTF-8",
+    "compressible": true
+  },
+  "application/pidf-diff+xml": {
+    "source": "iana",
+    "charset": "UTF-8",
+    "compressible": true
+  },
+  "application/pkcs10": {
+    "source": "iana",
+    "extensions": ["p10"]
+  },
+  "application/pkcs12": {
+    "source": "iana"
+  },
+  "application/pkcs7-mime": {
+    "source": "iana",
+    "extensions": ["p7m","p7c"]
+  },
+  "application/pkcs7-signature": {
+    "source": "iana",
+    "extensions": ["p7s"]
+  },
+  "application/pkcs8": {
+    "source": "iana",
+    "extensions": ["p8"]
+  },
+  "application/pkcs8-encrypted": {
+    "source": "iana"
+  },
+  "application/pkix-attr-cert": {
+    "source": "iana",
+    "extensions": ["ac"]
+  },
+  "application/pkix-cert": {
+    "source": "iana",
+    "extensions": ["cer"]
+  },
+  "application/pkix-crl": {
+    "source": "iana",
+    "extensions": ["crl"]
+  },
+  "application/pkix-pkipath": {
+    "source": "iana",
+    "extensions": ["pkipath"]
+  },
+  "application/pkixcmp": {
+    "source": "iana",
+    "extensions": ["pki"]
+  },
+  "application/pls+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["pls"]
+  },
+  "application/poc-settings+xml": {
+    "source": "iana",
+    "charset": "UTF-8",
+    "compressible": true
+  },
+  "application/postscript": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["ai","eps","ps"]
+  },
+  "application/ppsp-tracker+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/problem+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/problem+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/provenance+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["provx"]
+  },
+  "application/prs.alvestrand.titrax-sheet": {
+    "source": "iana"
+  },
+  "application/prs.cww": {
+    "source": "iana",
+    "extensions": ["cww"]
+  },
+  "application/prs.cyn": {
+    "source": "iana",
+    "charset": "7-BIT"
+  },
+  "application/prs.hpub+zip": {
+    "source": "iana",
+    "compressible": false
+  },
+  "application/prs.nprend": {
+    "source": "iana"
+  },
+  "application/prs.plucker": {
+    "source": "iana"
+  },
+  "application/prs.rdf-xml-crypt": {
+    "source": "iana"
+  },
+  "application/prs.xsf+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/pskc+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["pskcxml"]
+  },
+  "application/pvd+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/qsig": {
+    "source": "iana"
+  },
+  "application/raml+yaml": {
+    "compressible": true,
+    "extensions": ["raml"]
+  },
+  "application/raptorfec": {
+    "source": "iana"
+  },
+  "application/rdap+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/rdf+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["rdf","owl"]
+  },
+  "application/reginfo+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["rif"]
+  },
+  "application/relax-ng-compact-syntax": {
+    "source": "iana",
+    "extensions": ["rnc"]
+  },
+  "application/remote-printing": {
+    "source": "iana"
+  },
+  "application/reputon+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/resource-lists+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["rl"]
+  },
+  "application/resource-lists-diff+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["rld"]
+  },
+  "application/rfc+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/riscos": {
+    "source": "iana"
+  },
+  "application/rlmi+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/rls-services+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["rs"]
+  },
+  "application/route-apd+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["rapd"]
+  },
+  "application/route-s-tsid+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["sls"]
+  },
+  "application/route-usd+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["rusd"]
+  },
+  "application/rpki-ghostbusters": {
+    "source": "iana",
+    "extensions": ["gbr"]
+  },
+  "application/rpki-manifest": {
+    "source": "iana",
+    "extensions": ["mft"]
+  },
+  "application/rpki-publication": {
+    "source": "iana"
+  },
+  "application/rpki-roa": {
+    "source": "iana",
+    "extensions": ["roa"]
+  },
+  "application/rpki-updown": {
+    "source": "iana"
+  },
+  "application/rsd+xml": {
+    "source": "apache",
+    "compressible": true,
+    "extensions": ["rsd"]
+  },
+  "application/rss+xml": {
+    "source": "apache",
+    "compressible": true,
+    "extensions": ["rss"]
+  },
+  "application/rtf": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["rtf"]
+  },
+  "application/rtploopback": {
+    "source": "iana"
+  },
+  "application/rtx": {
+    "source": "iana"
+  },
+  "application/samlassertion+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/samlmetadata+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/sarif+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/sarif-external-properties+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/sbe": {
+    "source": "iana"
+  },
+  "application/sbml+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["sbml"]
+  },
+  "application/scaip+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/scim+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/scvp-cv-request": {
+    "source": "iana",
+    "extensions": ["scq"]
+  },
+  "application/scvp-cv-response": {
+    "source": "iana",
+    "extensions": ["scs"]
+  },
+  "application/scvp-vp-request": {
+    "source": "iana",
+    "extensions": ["spq"]
+  },
+  "application/scvp-vp-response": {
+    "source": "iana",
+    "extensions": ["spp"]
+  },
+  "application/sdp": {
+    "source": "iana",
+    "extensions": ["sdp"]
+  },
+  "application/secevent+jwt": {
+    "source": "iana"
+  },
+  "application/senml+cbor": {
+    "source": "iana"
+  },
+  "application/senml+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/senml+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["senmlx"]
+  },
+  "application/senml-etch+cbor": {
+    "source": "iana"
+  },
+  "application/senml-etch+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/senml-exi": {
+    "source": "iana"
+  },
+  "application/sensml+cbor": {
+    "source": "iana"
+  },
+  "application/sensml+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/sensml+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["sensmlx"]
+  },
+  "application/sensml-exi": {
+    "source": "iana"
+  },
+  "application/sep+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/sep-exi": {
+    "source": "iana"
+  },
+  "application/session-info": {
+    "source": "iana"
+  },
+  "application/set-payment": {
+    "source": "iana"
+  },
+  "application/set-payment-initiation": {
+    "source": "iana",
+    "extensions": ["setpay"]
+  },
+  "application/set-registration": {
+    "source": "iana"
+  },
+  "application/set-registration-initiation": {
+    "source": "iana",
+    "extensions": ["setreg"]
+  },
+  "application/sgml": {
+    "source": "iana"
+  },
+  "application/sgml-open-catalog": {
+    "source": "iana"
+  },
+  "application/shf+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["shf"]
+  },
+  "application/sieve": {
+    "source": "iana",
+    "extensions": ["siv","sieve"]
+  },
+  "application/simple-filter+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/simple-message-summary": {
+    "source": "iana"
+  },
+  "application/simplesymbolcontainer": {
+    "source": "iana"
+  },
+  "application/sipc": {
+    "source": "iana"
+  },
+  "application/slate": {
+    "source": "iana"
+  },
+  "application/smil": {
+    "source": "iana"
+  },
+  "application/smil+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["smi","smil"]
+  },
+  "application/smpte336m": {
+    "source": "iana"
+  },
+  "application/soap+fastinfoset": {
+    "source": "iana"
+  },
+  "application/soap+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/sparql-query": {
+    "source": "iana",
+    "extensions": ["rq"]
+  },
+  "application/sparql-results+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["srx"]
+  },
+  "application/spdx+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/spirits-event+xml": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/sql": {
+    "source": "iana"
+  },
+  "application/srgs": {
+    "source": "iana",
+    "extensions": ["gram"]
+  },
+  "application/srgs+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["grxml"]
+  },
+  "application/sru+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["sru"]
+  },
+  "application/ssdl+xml": {
+    "source": "apache",
+    "compressible": true,
+    "extensions": ["ssdl"]
+  },
+  "application/ssml+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["ssml"]
+  },
+  "application/stix+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/swid+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["swidtag"]
+  },
+  "application/tamp-apex-update": {
+    "source": "iana"
+  },
+  "application/tamp-apex-update-confirm": {
+    "source": "iana"
+  },
+  "application/tamp-community-update": {
+    "source": "iana"
+  },
+  "application/tamp-community-update-confirm": {
+    "source": "iana"
+  },
+  "application/tamp-error": {
+    "source": "iana"
+  },
+  "application/tamp-sequence-adjust": {
+    "source": "iana"
+  },
+  "application/tamp-sequence-adjust-confirm": {
+    "source": "iana"
+  },
+  "application/tamp-status-query": {
+    "source": "iana"
+  },
+  "application/tamp-status-response": {
+    "source": "iana"
+  },
+  "application/tamp-update": {
+    "source": "iana"
+  },
+  "application/tamp-update-confirm": {
+    "source": "iana"
+  },
+  "application/tar": {
+    "compressible": true
+  },
+  "application/taxii+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/td+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/tei+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["tei","teicorpus"]
+  },
+  "application/tetra_isi": {
+    "source": "iana"
+  },
+  "application/thraud+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["tfi"]
+  },
+  "application/timestamp-query": {
+    "source": "iana"
+  },
+  "application/timestamp-reply": {
+    "source": "iana"
+  },
+  "application/timestamped-data": {
+    "source": "iana",
+    "extensions": ["tsd"]
+  },
+  "application/tlsrpt+gzip": {
+    "source": "iana"
+  },
+  "application/tlsrpt+json": {
+    "source": "iana",
+    "compressible": true
+  },
+  "application/tnauthlist": {
+    "source": "iana"
+  },
+  "application/token-introspection+jwt": {
+    "source": "iana"
+  },
+  "application/toml": {
+    "compressible": true,
+    "extensions": ["toml"]
+  },
+  "application/trickle-ice-sdpfrag": {
+    "source": "iana"
+  },
+  "application/trig": {
+    "source": "iana",
+    "extensions": ["trig"]
+  },
+  "application/ttml+xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["ttml"]
+  },
+  "application/tve-trigger": {
+    "source": "iana"
+  },
+  "application/tzif": {
+    "source": "iana"
+  },
+  "application/tzif-leap": {
+    "source": "iana"
+  },
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+    "extensions": ["rep"]
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+    "extensions": ["cdxml"]
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+    "compressible": false
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+    "extensions": ["cdbcmsg"]
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+    "extensions": ["p7r"]
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+    "extensions": ["rar"]
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+    "extensions": ["webapp"]
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+    "extensions": ["xlf"]
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+    "extensions": ["xpi"]
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+  "application/x-xz": {
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+    "extensions": ["xz"]
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+    "extensions": ["z1","z2","z3","z4","z5","z6","z7","z8"]
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+    "extensions": ["xaml"]
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+    "extensions": ["xav"]
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+    "extensions": ["xca"]
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+  },
+  "text/vcard": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["vcard"]
+  },
+  "text/vnd.a": {
+    "source": "iana"
+  },
+  "text/vnd.abc": {
+    "source": "iana"
+  },
+  "text/vnd.ascii-art": {
+    "source": "iana"
+  },
+  "text/vnd.curl": {
+    "source": "iana",
+    "extensions": ["curl"]
+  },
+  "text/vnd.curl.dcurl": {
+    "source": "apache",
+    "extensions": ["dcurl"]
+  },
+  "text/vnd.curl.mcurl": {
+    "source": "apache",
+    "extensions": ["mcurl"]
+  },
+  "text/vnd.curl.scurl": {
+    "source": "apache",
+    "extensions": ["scurl"]
+  },
+  "text/vnd.debian.copyright": {
+    "source": "iana",
+    "charset": "UTF-8"
+  },
+  "text/vnd.dmclientscript": {
+    "source": "iana"
+  },
+  "text/vnd.dvb.subtitle": {
+    "source": "iana",
+    "extensions": ["sub"]
+  },
+  "text/vnd.esmertec.theme-descriptor": {
+    "source": "iana",
+    "charset": "UTF-8"
+  },
+  "text/vnd.familysearch.gedcom": {
+    "source": "iana",
+    "extensions": ["ged"]
+  },
+  "text/vnd.ficlab.flt": {
+    "source": "iana"
+  },
+  "text/vnd.fly": {
+    "source": "iana",
+    "extensions": ["fly"]
+  },
+  "text/vnd.fmi.flexstor": {
+    "source": "iana",
+    "extensions": ["flx"]
+  },
+  "text/vnd.gml": {
+    "source": "iana"
+  },
+  "text/vnd.graphviz": {
+    "source": "iana",
+    "extensions": ["gv"]
+  },
+  "text/vnd.hans": {
+    "source": "iana"
+  },
+  "text/vnd.hgl": {
+    "source": "iana"
+  },
+  "text/vnd.in3d.3dml": {
+    "source": "iana",
+    "extensions": ["3dml"]
+  },
+  "text/vnd.in3d.spot": {
+    "source": "iana",
+    "extensions": ["spot"]
+  },
+  "text/vnd.iptc.newsml": {
+    "source": "iana"
+  },
+  "text/vnd.iptc.nitf": {
+    "source": "iana"
+  },
+  "text/vnd.latex-z": {
+    "source": "iana"
+  },
+  "text/vnd.motorola.reflex": {
+    "source": "iana"
+  },
+  "text/vnd.ms-mediapackage": {
+    "source": "iana"
+  },
+  "text/vnd.net2phone.commcenter.command": {
+    "source": "iana"
+  },
+  "text/vnd.radisys.msml-basic-layout": {
+    "source": "iana"
+  },
+  "text/vnd.senx.warpscript": {
+    "source": "iana"
+  },
+  "text/vnd.si.uricatalogue": {
+    "source": "iana"
+  },
+  "text/vnd.sosi": {
+    "source": "iana"
+  },
+  "text/vnd.sun.j2me.app-descriptor": {
+    "source": "iana",
+    "charset": "UTF-8",
+    "extensions": ["jad"]
+  },
+  "text/vnd.trolltech.linguist": {
+    "source": "iana",
+    "charset": "UTF-8"
+  },
+  "text/vnd.wap.si": {
+    "source": "iana"
+  },
+  "text/vnd.wap.sl": {
+    "source": "iana"
+  },
+  "text/vnd.wap.wml": {
+    "source": "iana",
+    "extensions": ["wml"]
+  },
+  "text/vnd.wap.wmlscript": {
+    "source": "iana",
+    "extensions": ["wmls"]
+  },
+  "text/vtt": {
+    "source": "iana",
+    "charset": "UTF-8",
+    "compressible": true,
+    "extensions": ["vtt"]
+  },
+  "text/x-asm": {
+    "source": "apache",
+    "extensions": ["s","asm"]
+  },
+  "text/x-c": {
+    "source": "apache",
+    "extensions": ["c","cc","cxx","cpp","h","hh","dic"]
+  },
+  "text/x-component": {
+    "source": "nginx",
+    "extensions": ["htc"]
+  },
+  "text/x-fortran": {
+    "source": "apache",
+    "extensions": ["f","for","f77","f90"]
+  },
+  "text/x-gwt-rpc": {
+    "compressible": true
+  },
+  "text/x-handlebars-template": {
+    "extensions": ["hbs"]
+  },
+  "text/x-java-source": {
+    "source": "apache",
+    "extensions": ["java"]
+  },
+  "text/x-jquery-tmpl": {
+    "compressible": true
+  },
+  "text/x-lua": {
+    "extensions": ["lua"]
+  },
+  "text/x-markdown": {
+    "compressible": true,
+    "extensions": ["mkd"]
+  },
+  "text/x-nfo": {
+    "source": "apache",
+    "extensions": ["nfo"]
+  },
+  "text/x-opml": {
+    "source": "apache",
+    "extensions": ["opml"]
+  },
+  "text/x-org": {
+    "compressible": true,
+    "extensions": ["org"]
+  },
+  "text/x-pascal": {
+    "source": "apache",
+    "extensions": ["p","pas"]
+  },
+  "text/x-processing": {
+    "compressible": true,
+    "extensions": ["pde"]
+  },
+  "text/x-sass": {
+    "extensions": ["sass"]
+  },
+  "text/x-scss": {
+    "extensions": ["scss"]
+  },
+  "text/x-setext": {
+    "source": "apache",
+    "extensions": ["etx"]
+  },
+  "text/x-sfv": {
+    "source": "apache",
+    "extensions": ["sfv"]
+  },
+  "text/x-suse-ymp": {
+    "compressible": true,
+    "extensions": ["ymp"]
+  },
+  "text/x-uuencode": {
+    "source": "apache",
+    "extensions": ["uu"]
+  },
+  "text/x-vcalendar": {
+    "source": "apache",
+    "extensions": ["vcs"]
+  },
+  "text/x-vcard": {
+    "source": "apache",
+    "extensions": ["vcf"]
+  },
+  "text/xml": {
+    "source": "iana",
+    "compressible": true,
+    "extensions": ["xml"]
+  },
+  "text/xml-external-parsed-entity": {
+    "source": "iana"
+  },
+  "text/yaml": {
+    "compressible": true,
+    "extensions": ["yaml","yml"]
+  },
+  "video/1d-interleaved-parityfec": {
+    "source": "iana"
+  },
+  "video/3gpp": {
+    "source": "iana",
+    "extensions": ["3gp","3gpp"]
+  },
+  "video/3gpp-tt": {
+    "source": "iana"
+  },
+  "video/3gpp2": {
+    "source": "iana",
+    "extensions": ["3g2"]
+  },
+  "video/av1": {
+    "source": "iana"
+  },
+  "video/bmpeg": {
+    "source": "iana"
+  },
+  "video/bt656": {
+    "source": "iana"
+  },
+  "video/celb": {
+    "source": "iana"
+  },
+  "video/dv": {
+    "source": "iana"
+  },
+  "video/encaprtp": {
+    "source": "iana"
+  },
+  "video/ffv1": {
+    "source": "iana"
+  },
+  "video/flexfec": {
+    "source": "iana"
+  },
+  "video/h261": {
+    "source": "iana",
+    "extensions": ["h261"]
+  },
+  "video/h263": {
+    "source": "iana",
+    "extensions": ["h263"]
+  },
+  "video/h263-1998": {
+    "source": "iana"
+  },
+  "video/h263-2000": {
+    "source": "iana"
+  },
+  "video/h264": {
+    "source": "iana",
+    "extensions": ["h264"]
+  },
+  "video/h264-rcdo": {
+    "source": "iana"
+  },
+  "video/h264-svc": {
+    "source": "iana"
+  },
+  "video/h265": {
+    "source": "iana"
+  },
+  "video/iso.segment": {
+    "source": "iana",
+    "extensions": ["m4s"]
+  },
+  "video/jpeg": {
+    "source": "iana",
+    "extensions": ["jpgv"]
+  },
+  "video/jpeg2000": {
+    "source": "iana"
+  },
+  "video/jpm": {
+    "source": "apache",
+    "extensions": ["jpm","jpgm"]
+  },
+  "video/jxsv": {
+    "source": "iana"
+  },
+  "video/mj2": {
+    "source": "iana",
+    "extensions": ["mj2","mjp2"]
+  },
+  "video/mp1s": {
+    "source": "iana"
+  },
+  "video/mp2p": {
+    "source": "iana"
+  },
+  "video/mp2t": {
+    "source": "iana",
+    "extensions": ["ts"]
+  },
+  "video/mp4": {
+    "source": "iana",
+    "compressible": false,
+    "extensions": ["mp4","mp4v","mpg4"]
+  },
+  "video/mp4v-es": {
+    "source": "iana"
+  },
+  "video/mpeg": {
+    "source": "iana",
+    "compressible": false,
+    "extensions": ["mpeg","mpg","mpe","m1v","m2v"]
+  },
+  "video/mpeg4-generic": {
+    "source": "iana"
+  },
+  "video/mpv": {
+    "source": "iana"
+  },
+  "video/nv": {
+    "source": "iana"
+  },
+  "video/ogg": {
+    "source": "iana",
+    "compressible": false,
+    "extensions": ["ogv"]
+  },
+  "video/parityfec": {
+    "source": "iana"
+  },
+  "video/pointer": {
+    "source": "iana"
+  },
+  "video/quicktime": {
+    "source": "iana",
+    "compressible": false,
+    "extensions": ["qt","mov"]
+  },
+  "video/raptorfec": {
+    "source": "iana"
+  },
+  "video/raw": {
+    "source": "iana"
+  },
+  "video/rtp-enc-aescm128": {
+    "source": "iana"
+  },
+  "video/rtploopback": {
+    "source": "iana"
+  },
+  "video/rtx": {
+    "source": "iana"
+  },
+  "video/scip": {
+    "source": "iana"
+  },
+  "video/smpte291": {
+    "source": "iana"
+  },
+  "video/smpte292m": {
+    "source": "iana"
+  },
+  "video/ulpfec": {
+    "source": "iana"
+  },
+  "video/vc1": {
+    "source": "iana"
+  },
+  "video/vc2": {
+    "source": "iana"
+  },
+  "video/vnd.cctv": {
+    "source": "iana"
+  },
+  "video/vnd.dece.hd": {
+    "source": "iana",
+    "extensions": ["uvh","uvvh"]
+  },
+  "video/vnd.dece.mobile": {
+    "source": "iana",
+    "extensions": ["uvm","uvvm"]
+  },
+  "video/vnd.dece.mp4": {
+    "source": "iana"
+  },
+  "video/vnd.dece.pd": {
+    "source": "iana",
+    "extensions": ["uvp","uvvp"]
+  },
+  "video/vnd.dece.sd": {
+    "source": "iana",
+    "extensions": ["uvs","uvvs"]
+  },
+  "video/vnd.dece.video": {
+    "source": "iana",
+    "extensions": ["uvv","uvvv"]
+  },
+  "video/vnd.directv.mpeg": {
+    "source": "iana"
+  },
+  "video/vnd.directv.mpeg-tts": {
+    "source": "iana"
+  },
+  "video/vnd.dlna.mpeg-tts": {
+    "source": "iana"
+  },
+  "video/vnd.dvb.file": {
+    "source": "iana",
+    "extensions": ["dvb"]
+  },
+  "video/vnd.fvt": {
+    "source": "iana",
+    "extensions": ["fvt"]
+  },
+  "video/vnd.hns.video": {
+    "source": "iana"
+  },
+  "video/vnd.iptvforum.1dparityfec-1010": {
+    "source": "iana"
+  },
+  "video/vnd.iptvforum.1dparityfec-2005": {
+    "source": "iana"
+  },
+  "video/vnd.iptvforum.2dparityfec-1010": {
+    "source": "iana"
+  },
+  "video/vnd.iptvforum.2dparityfec-2005": {
+    "source": "iana"
+  },
+  "video/vnd.iptvforum.ttsavc": {
+    "source": "iana"
+  },
+  "video/vnd.iptvforum.ttsmpeg2": {
+    "source": "iana"
+  },
+  "video/vnd.motorola.video": {
+    "source": "iana"
+  },
+  "video/vnd.motorola.videop": {
+    "source": "iana"
+  },
+  "video/vnd.mpegurl": {
+    "source": "iana",
+    "extensions": ["mxu","m4u"]
+  },
+  "video/vnd.ms-playready.media.pyv": {
+    "source": "iana",
+    "extensions": ["pyv"]
+  },
+  "video/vnd.nokia.interleaved-multimedia": {
+    "source": "iana"
+  },
+  "video/vnd.nokia.mp4vr": {
+    "source": "iana"
+  },
+  "video/vnd.nokia.videovoip": {
+    "source": "iana"
+  },
+  "video/vnd.objectvideo": {
+    "source": "iana"
+  },
+  "video/vnd.radgamettools.bink": {
+    "source": "iana"
+  },
+  "video/vnd.radgamettools.smacker": {
+    "source": "iana"
+  },
+  "video/vnd.sealed.mpeg1": {
+    "source": "iana"
+  },
+  "video/vnd.sealed.mpeg4": {
+    "source": "iana"
+  },
+  "video/vnd.sealed.swf": {
+    "source": "iana"
+  },
+  "video/vnd.sealedmedia.softseal.mov": {
+    "source": "iana"
+  },
+  "video/vnd.uvvu.mp4": {
+    "source": "iana",
+    "extensions": ["uvu","uvvu"]
+  },
+  "video/vnd.vivo": {
+    "source": "iana",
+    "extensions": ["viv"]
+  },
+  "video/vnd.youtube.yt": {
+    "source": "iana"
+  },
+  "video/vp8": {
+    "source": "iana"
+  },
+  "video/vp9": {
+    "source": "iana"
+  },
+  "video/webm": {
+    "source": "apache",
+    "compressible": false,
+    "extensions": ["webm"]
+  },
+  "video/x-f4v": {
+    "source": "apache",
+    "extensions": ["f4v"]
+  },
+  "video/x-fli": {
+    "source": "apache",
+    "extensions": ["fli"]
+  },
+  "video/x-flv": {
+    "source": "apache",
+    "compressible": false,
+    "extensions": ["flv"]
+  },
+  "video/x-m4v": {
+    "source": "apache",
+    "extensions": ["m4v"]
+  },
+  "video/x-matroska": {
+    "source": "apache",
+    "compressible": false,
+    "extensions": ["mkv","mk3d","mks"]
+  },
+  "video/x-mng": {
+    "source": "apache",
+    "extensions": ["mng"]
+  },
+  "video/x-ms-asf": {
+    "source": "apache",
+    "extensions": ["asf","asx"]
+  },
+  "video/x-ms-vob": {
+    "source": "apache",
+    "extensions": ["vob"]
+  },
+  "video/x-ms-wm": {
+    "source": "apache",
+    "extensions": ["wm"]
+  },
+  "video/x-ms-wmv": {
+    "source": "apache",
+    "compressible": false,
+    "extensions": ["wmv"]
+  },
+  "video/x-ms-wmx": {
+    "source": "apache",
+    "extensions": ["wmx"]
+  },
+  "video/x-ms-wvx": {
+    "source": "apache",
+    "extensions": ["wvx"]
+  },
+  "video/x-msvideo": {
+    "source": "apache",
+    "extensions": ["avi"]
+  },
+  "video/x-sgi-movie": {
+    "source": "apache",
+    "extensions": ["movie"]
+  },
+  "video/x-smv": {
+    "source": "apache",
+    "extensions": ["smv"]
+  },
+  "x-conference/x-cooltalk": {
+    "source": "apache",
+    "extensions": ["ice"]
+  },
+  "x-shader/x-fragment": {
+    "compressible": true
+  },
+  "x-shader/x-vertex": {
+    "compressible": true
+  }
+}
diff --git a/tortoise/node_modules/mime-db/index.js b/tortoise/node_modules/mime-db/index.js
new file mode 100644
index 0000000..ec2be30
--- /dev/null
+++ b/tortoise/node_modules/mime-db/index.js
@@ -0,0 +1,12 @@
+/*!
+ * mime-db
+ * Copyright(c) 2014 Jonathan Ong
+ * Copyright(c) 2015-2022 Douglas Christopher Wilson
+ * MIT Licensed
+ */
+
+/**
+ * Module exports.
+ */
+
+module.exports = require('./db.json')
diff --git a/tortoise/node_modules/mime-db/package.json b/tortoise/node_modules/mime-db/package.json
new file mode 100644
index 0000000..32c14b8
--- /dev/null
+++ b/tortoise/node_modules/mime-db/package.json
@@ -0,0 +1,60 @@
+{
+  "name": "mime-db",
+  "description": "Media Type Database",
+  "version": "1.52.0",
+  "contributors": [
+    "Douglas Christopher Wilson <doug@somethingdoug.com>",
+    "Jonathan Ong <me@jongleberry.com> (http://jongleberry.com)",
+    "Robert Kieffer <robert@broofa.com> (http://github.com/broofa)"
+  ],
+  "license": "MIT",
+  "keywords": [
+    "mime",
+    "db",
+    "type",
+    "types",
+    "database",
+    "charset",
+    "charsets"
+  ],
+  "repository": "jshttp/mime-db",
+  "devDependencies": {
+    "bluebird": "3.7.2",
+    "co": "4.6.0",
+    "cogent": "1.0.1",
+    "csv-parse": "4.16.3",
+    "eslint": "7.32.0",
+    "eslint-config-standard": "15.0.1",
+    "eslint-plugin-import": "2.25.4",
+    "eslint-plugin-markdown": "2.2.1",
+    "eslint-plugin-node": "11.1.0",
+    "eslint-plugin-promise": "5.1.1",
+    "eslint-plugin-standard": "4.1.0",
+    "gnode": "0.1.2",
+    "media-typer": "1.1.0",
+    "mocha": "9.2.1",
+    "nyc": "15.1.0",
+    "raw-body": "2.5.0",
+    "stream-to-array": "2.3.0"
+  },
+  "files": [
+    "HISTORY.md",
+    "LICENSE",
+    "README.md",
+    "db.json",
+    "index.js"
+  ],
+  "engines": {
+    "node": ">= 0.6"
+  },
+  "scripts": {
+    "build": "node scripts/build",
+    "fetch": "node scripts/fetch-apache && gnode scripts/fetch-iana && node scripts/fetch-nginx",
+    "lint": "eslint .",
+    "test": "mocha --reporter spec --bail --check-leaks test/",
+    "test-ci": "nyc --reporter=lcov --reporter=text npm test",
+    "test-cov": "nyc --reporter=html --reporter=text npm test",
+    "update": "npm run fetch && npm run build",
+    "version": "node scripts/version-history.js && git add HISTORY.md"
+  }
+}
diff --git a/tortoise/node_modules/mime-types/HISTORY.md b/tortoise/node_modules/mime-types/HISTORY.md
new file mode 100644
index 0000000..c5043b7
--- /dev/null
+++ b/tortoise/node_modules/mime-types/HISTORY.md
@@ -0,0 +1,397 @@
+2.1.35 / 2022-03-12
+===================
+
+  * deps: mime-db@1.52.0
+    - Add extensions from IANA for more `image/*` types
+    - Add extension `.asc` to `application/pgp-keys`
+    - Add extensions to various XML types
+    - Add new upstream MIME types
+
+2.1.34 / 2021-11-08
+===================
+
+  * deps: mime-db@1.51.0
+    - Add new upstream MIME types
+
+2.1.33 / 2021-10-01
+===================
+
+  * deps: mime-db@1.50.0
+    - Add deprecated iWorks mime types and extensions
+    - Add new upstream MIME types
+
+2.1.32 / 2021-07-27
+===================
+
+  * deps: mime-db@1.49.0
+    - Add extension `.trig` to `application/trig`
+    - Add new upstream MIME types
+
+2.1.31 / 2021-06-01
+===================
+
+  * deps: mime-db@1.48.0
+    - Add extension `.mvt` to `application/vnd.mapbox-vector-tile`
+    - Add new upstream MIME types
+
+2.1.30 / 2021-04-02
+===================
+
+  * deps: mime-db@1.47.0
+    - Add extension `.amr` to `audio/amr`
+    - Remove ambigious extensions from IANA for `application/*+xml` types
+    - Update primary extension to `.es` for `application/ecmascript`
+
+2.1.29 / 2021-02-17
+===================
+
+  * deps: mime-db@1.46.0
+    - Add extension `.amr` to `audio/amr`
+    - Add extension `.m4s` to `video/iso.segment`
+    - Add extension `.opus` to `audio/ogg`
+    - Add new upstream MIME types
+
+2.1.28 / 2021-01-01
+===================
+
+  * deps: mime-db@1.45.0
+    - Add `application/ubjson` with extension `.ubj`
+    - Add `image/avif` with extension `.avif`
+    - Add `image/ktx2` with extension `.ktx2`
+    - Add extension `.dbf` to `application/vnd.dbf`
+    - Add extension `.rar` to `application/vnd.rar`
+    - Add extension `.td` to `application/urc-targetdesc+xml`
+    - Add new upstream MIME types
+    - Fix extension of `application/vnd.apple.keynote` to be `.key`
+
+2.1.27 / 2020-04-23
+===================
+
+  * deps: mime-db@1.44.0
+    - Add charsets from IANA
+    - Add extension `.cjs` to `application/node`
+    - Add new upstream MIME types
+
+2.1.26 / 2020-01-05
+===================
+
+  * deps: mime-db@1.43.0
+    - Add `application/x-keepass2` with extension `.kdbx`
+    - Add extension `.mxmf` to `audio/mobile-xmf`
+    - Add extensions from IANA for `application/*+xml` types
+    - Add new upstream MIME types
+
+2.1.25 / 2019-11-12
+===================
+
+  * deps: mime-db@1.42.0
+    - Add new upstream MIME types
+    - Add `application/toml` with extension `.toml`
+    - Add `image/vnd.ms-dds` with extension `.dds`
+
+2.1.24 / 2019-04-20
+===================
+
+  * deps: mime-db@1.40.0
+    - Add extensions from IANA for `model/*` types
+    - Add `text/mdx` with extension `.mdx`
+
+2.1.23 / 2019-04-17
+===================
+
+  * deps: mime-db@~1.39.0
+    - Add extensions `.siv` and `.sieve` to `application/sieve`
+    - Add new upstream MIME types
+
+2.1.22 / 2019-02-14
+===================
+
+  * deps: mime-db@~1.38.0
+    - Add extension `.nq` to `application/n-quads`
+    - Add extension `.nt` to `application/n-triples`
+    - Add new upstream MIME types
+
+2.1.21 / 2018-10-19
+===================
+
+  * deps: mime-db@~1.37.0
+    - Add extensions to HEIC image types
+    - Add new upstream MIME types
+
+2.1.20 / 2018-08-26
+===================
+
+  * deps: mime-db@~1.36.0
+    - Add Apple file extensions from IANA
+    - Add extensions from IANA for `image/*` types
+    - Add new upstream MIME types
+
+2.1.19 / 2018-07-17
+===================
+
+  * deps: mime-db@~1.35.0
+    - Add extension `.csl` to `application/vnd.citationstyles.style+xml`
+    - Add extension `.es` to `application/ecmascript`
+    - Add extension `.owl` to `application/rdf+xml`
+    - Add new upstream MIME types
+    - Add UTF-8 as default charset for `text/turtle`
+
+2.1.18 / 2018-02-16
+===================
+
+  * deps: mime-db@~1.33.0
+    - Add `application/raml+yaml` with extension `.raml`
+    - Add `application/wasm` with extension `.wasm`
+    - Add `text/shex` with extension `.shex`
+    - Add extensions for JPEG-2000 images
+    - Add extensions from IANA for `message/*` types
+    - Add new upstream MIME types
+    - Update font MIME types
+    - Update `text/hjson` to registered `application/hjson`
+
+2.1.17 / 2017-09-01
+===================
+
+  * deps: mime-db@~1.30.0
+    - Add `application/vnd.ms-outlook`
+    - Add `application/x-arj`
+    - Add extension `.mjs` to `application/javascript`
+    - Add glTF types and extensions
+    - Add new upstream MIME types
+    - Add `text/x-org`
+    - Add VirtualBox MIME types
+    - Fix `source` records for `video/*` types that are IANA
+    - Update `font/opentype` to registered `font/otf`
+
+2.1.16 / 2017-07-24
+===================
+
+  * deps: mime-db@~1.29.0
+    - Add `application/fido.trusted-apps+json`
+    - Add extension `.wadl` to `application/vnd.sun.wadl+xml`
+    - Add extension `.gz` to `application/gzip`
+    - Add new upstream MIME types
+    - Update extensions `.md` and `.markdown` to be `text/markdown`
+
+2.1.15 / 2017-03-23
+===================
+
+  * deps: mime-db@~1.27.0
+    - Add new mime types
+    - Add `image/apng`
+
+2.1.14 / 2017-01-14
+===================
+
+  * deps: mime-db@~1.26.0
+    - Add new mime types
+
+2.1.13 / 2016-11-18
+===================
+
+  * deps: mime-db@~1.25.0
+    - Add new mime types
+
+2.1.12 / 2016-09-18
+===================
+
+  * deps: mime-db@~1.24.0
+    - Add new mime types
+    - Add `audio/mp3`
+
+2.1.11 / 2016-05-01
+===================
+
+  * deps: mime-db@~1.23.0
+    - Add new mime types
+
+2.1.10 / 2016-02-15
+===================
+
+  * deps: mime-db@~1.22.0
+    - Add new mime types
+    - Fix extension of `application/dash+xml`
+    - Update primary extension for `audio/mp4`
+
+2.1.9 / 2016-01-06
+==================
+
+  * deps: mime-db@~1.21.0
+    - Add new mime types
+
+2.1.8 / 2015-11-30
+==================
+
+  * deps: mime-db@~1.20.0
+    - Add new mime types
+
+2.1.7 / 2015-09-20
+==================
+
+  * deps: mime-db@~1.19.0
+    - Add new mime types
+
+2.1.6 / 2015-09-03
+==================
+
+  * deps: mime-db@~1.18.0
+    - Add new mime types
+
+2.1.5 / 2015-08-20
+==================
+
+  * deps: mime-db@~1.17.0
+    - Add new mime types
+
+2.1.4 / 2015-07-30
+==================
+
+  * deps: mime-db@~1.16.0
+    - Add new mime types
+
+2.1.3 / 2015-07-13
+==================
+
+  * deps: mime-db@~1.15.0
+    - Add new mime types
+
+2.1.2 / 2015-06-25
+==================
+
+  * deps: mime-db@~1.14.0
+    - Add new mime types
+
+2.1.1 / 2015-06-08
+==================
+
+  * perf: fix deopt during mapping
+
+2.1.0 / 2015-06-07
+==================
+
+  * Fix incorrectly treating extension-less file name as extension
+    - i.e. `'path/to/json'` will no longer return `application/json`
+  * Fix `.charset(type)` to accept parameters
+  * Fix `.charset(type)` to match case-insensitive
+  * Improve generation of extension to MIME mapping
+  * Refactor internals for readability and no argument reassignment
+  * Prefer `application/*` MIME types from the same source
+  * Prefer any type over `application/octet-stream`
+  * deps: mime-db@~1.13.0
+    - Add nginx as a source
+    - Add new mime types
+
+2.0.14 / 2015-06-06
+===================
+
+  * deps: mime-db@~1.12.0
+    - Add new mime types
+
+2.0.13 / 2015-05-31
+===================
+
+  * deps: mime-db@~1.11.0
+    - Add new mime types
+
+2.0.12 / 2015-05-19
+===================
+
+  * deps: mime-db@~1.10.0
+    - Add new mime types
+
+2.0.11 / 2015-05-05
+===================
+
+  * deps: mime-db@~1.9.1
+    - Add new mime types
+
+2.0.10 / 2015-03-13
+===================
+
+  * deps: mime-db@~1.8.0
+    - Add new mime types
+
+2.0.9 / 2015-02-09
+==================
+
+  * deps: mime-db@~1.7.0
+    - Add new mime types
+    - Community extensions ownership transferred from `node-mime`
+
+2.0.8 / 2015-01-29
+==================
+
+  * deps: mime-db@~1.6.0
+    - Add new mime types
+
+2.0.7 / 2014-12-30
+==================
+
+  * deps: mime-db@~1.5.0
+    - Add new mime types
+    - Fix various invalid MIME type entries
+
+2.0.6 / 2014-12-30
+==================
+
+  * deps: mime-db@~1.4.0
+    - Add new mime types
+    - Fix various invalid MIME type entries
+    - Remove example template MIME types
+
+2.0.5 / 2014-12-29
+==================
+
+  * deps: mime-db@~1.3.1
+    - Fix missing extensions
+
+2.0.4 / 2014-12-10
+==================
+
+  * deps: mime-db@~1.3.0
+    - Add new mime types
+
+2.0.3 / 2014-11-09
+==================
+
+  * deps: mime-db@~1.2.0
+    - Add new mime types
+
+2.0.2 / 2014-09-28
+==================
+
+  * deps: mime-db@~1.1.0
+    - Add new mime types
+    - Update charsets
+
+2.0.1 / 2014-09-07
+==================
+
+  * Support Node.js 0.6
+
+2.0.0 / 2014-09-02
+==================
+
+  * Use `mime-db`
+  * Remove `.define()`
+
+1.0.2 / 2014-08-04
+==================
+
+  * Set charset=utf-8 for `text/javascript`
+
+1.0.1 / 2014-06-24
+==================
+
+  * Add `text/jsx` type
+
+1.0.0 / 2014-05-12
+==================
+
+  * Return `false` for unknown types
+  * Set charset=utf-8 for `application/json`
+
+0.1.0 / 2014-05-02
+==================
+
+  * Initial release
diff --git a/tortoise/node_modules/mime-types/LICENSE b/tortoise/node_modules/mime-types/LICENSE
new file mode 100644
index 0000000..0616607
--- /dev/null
+++ b/tortoise/node_modules/mime-types/LICENSE
@@ -0,0 +1,23 @@
+(The MIT License)
+
+Copyright (c) 2014 Jonathan Ong <me@jongleberry.com>
+Copyright (c) 2015 Douglas Christopher Wilson <doug@somethingdoug.com>
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+'Software'), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/tortoise/node_modules/mime-types/README.md b/tortoise/node_modules/mime-types/README.md
new file mode 100644
index 0000000..48d2fb4
--- /dev/null
+++ b/tortoise/node_modules/mime-types/README.md
@@ -0,0 +1,113 @@
+# mime-types
+
+[![NPM Version][npm-version-image]][npm-url]
+[![NPM Downloads][npm-downloads-image]][npm-url]
+[![Node.js Version][node-version-image]][node-version-url]
+[![Build Status][ci-image]][ci-url]
+[![Test Coverage][coveralls-image]][coveralls-url]
+
+The ultimate javascript content-type utility.
+
+Similar to [the `mime@1.x` module](https://www.npmjs.com/package/mime), except:
+
+- __No fallbacks.__ Instead of naively returning the first available type,
+  `mime-types` simply returns `false`, so do
+  `var type = mime.lookup('unrecognized') || 'application/octet-stream'`.
+- No `new Mime()` business, so you could do `var lookup = require('mime-types').lookup`.
+- No `.define()` functionality
+- Bug fixes for `.lookup(path)`
+
+Otherwise, the API is compatible with `mime` 1.x.
+
+## Install
+
+This is a [Node.js](https://nodejs.org/en/) module available through the
+[npm registry](https://www.npmjs.com/). Installation is done using the
+[`npm install` command](https://docs.npmjs.com/getting-started/installing-npm-packages-locally):
+
+```sh
+$ npm install mime-types
+```
+
+## Adding Types
+
+All mime types are based on [mime-db](https://www.npmjs.com/package/mime-db),
+so open a PR there if you'd like to add mime types.
+
+## API
+
+```js
+var mime = require('mime-types')
+```
+
+All functions return `false` if input is invalid or not found.
+
+### mime.lookup(path)
+
+Lookup the content-type associated with a file.
+
+```js
+mime.lookup('json') // 'application/json'
+mime.lookup('.md') // 'text/markdown'
+mime.lookup('file.html') // 'text/html'
+mime.lookup('folder/file.js') // 'application/javascript'
+mime.lookup('folder/.htaccess') // false
+
+mime.lookup('cats') // false
+```
+
+### mime.contentType(type)
+
+Create a full content-type header given a content-type or extension.
+When given an extension, `mime.lookup` is used to get the matching
+content-type, otherwise the given content-type is used. Then if the
+content-type does not already have a `charset` parameter, `mime.charset`
+is used to get the default charset and add to the returned content-type.
+
+```js
+mime.contentType('markdown') // 'text/x-markdown; charset=utf-8'
+mime.contentType('file.json') // 'application/json; charset=utf-8'
+mime.contentType('text/html') // 'text/html; charset=utf-8'
+mime.contentType('text/html; charset=iso-8859-1') // 'text/html; charset=iso-8859-1'
+
+// from a full path
+mime.contentType(path.extname('/path/to/file.json')) // 'application/json; charset=utf-8'
+```
+
+### mime.extension(type)
+
+Get the default extension for a content-type.
+
+```js
+mime.extension('application/octet-stream') // 'bin'
+```
+
+### mime.charset(type)
+
+Lookup the implied default charset of a content-type.
+
+```js
+mime.charset('text/markdown') // 'UTF-8'
+```
+
+### var type = mime.types[extension]
+
+A map of content-types by extension.
+
+### [extensions...] = mime.extensions[type]
+
+A map of extensions by content-type.
+
+## License
+
+[MIT](LICENSE)
+
+[ci-image]: https://badgen.net/github/checks/jshttp/mime-types/master?label=ci
+[ci-url]: https://github.com/jshttp/mime-types/actions/workflows/ci.yml
+[coveralls-image]: https://badgen.net/coveralls/c/github/jshttp/mime-types/master
+[coveralls-url]: https://coveralls.io/r/jshttp/mime-types?branch=master
+[node-version-image]: https://badgen.net/npm/node/mime-types
+[node-version-url]: https://nodejs.org/en/download
+[npm-downloads-image]: https://badgen.net/npm/dm/mime-types
+[npm-url]: https://npmjs.org/package/mime-types
+[npm-version-image]: https://badgen.net/npm/v/mime-types
diff --git a/tortoise/node_modules/mime-types/index.js b/tortoise/node_modules/mime-types/index.js
new file mode 100644
index 0000000..b9f34d5
--- /dev/null
+++ b/tortoise/node_modules/mime-types/index.js
@@ -0,0 +1,188 @@
+/*!
+ * mime-types
+ * Copyright(c) 2014 Jonathan Ong
+ * Copyright(c) 2015 Douglas Christopher Wilson
+ * MIT Licensed
+ */
+
+'use strict'
+
+/**
+ * Module dependencies.
+ * @private
+ */
+
+var db = require('mime-db')
+var extname = require('path').extname
+
+/**
+ * Module variables.
+ * @private
+ */
+
+var EXTRACT_TYPE_REGEXP = /^\s*([^;\s]*)(?:;|\s|$)/
+var TEXT_TYPE_REGEXP = /^text\//i
+
+/**
+ * Module exports.
+ * @public
+ */
+
+exports.charset = charset
+exports.charsets = { lookup: charset }
+exports.contentType = contentType
+exports.extension = extension
+exports.extensions = Object.create(null)
+exports.lookup = lookup
+exports.types = Object.create(null)
+
+// Populate the extensions/types maps
+populateMaps(exports.extensions, exports.types)
+
+/**
+ * Get the default charset for a MIME type.
+ *
+ * @param {string} type
+ * @return {boolean|string}
+ */
+
+function charset (type) {
+  if (!type || typeof type !== 'string') {
+    return false
+  }
+
+  // TODO: use media-typer
+  var match = EXTRACT_TYPE_REGEXP.exec(type)
+  var mime = match && db[match[1].toLowerCase()]
+
+  if (mime && mime.charset) {
+    return mime.charset
+  }
+
+  // default text/* to utf-8
+  if (match && TEXT_TYPE_REGEXP.test(match[1])) {
+    return 'UTF-8'
+  }
+
+  return false
+}
+
+/**
+ * Create a full Content-Type header given a MIME type or extension.
+ *
+ * @param {string} str
+ * @return {boolean|string}
+ */
+
+function contentType (str) {
+  // TODO: should this even be in this module?
+  if (!str || typeof str !== 'string') {
+    return false
+  }
+
+  var mime = str.indexOf('/') === -1
+    ? exports.lookup(str)
+    : str
+
+  if (!mime) {
+    return false
+  }
+
+  // TODO: use content-type or other module
+  if (mime.indexOf('charset') === -1) {
+    var charset = exports.charset(mime)
+    if (charset) mime += '; charset=' + charset.toLowerCase()
+  }
+
+  return mime
+}
+
+/**
+ * Get the default extension for a MIME type.
+ *
+ * @param {string} type
+ * @return {boolean|string}
+ */
+
+function extension (type) {
+  if (!type || typeof type !== 'string') {
+    return false
+  }
+
+  // TODO: use media-typer
+  var match = EXTRACT_TYPE_REGEXP.exec(type)
+
+  // get extensions
+  var exts = match && exports.extensions[match[1].toLowerCase()]
+
+  if (!exts || !exts.length) {
+    return false
+  }
+
+  return exts[0]
+}
+
+/**
+ * Lookup the MIME type for a file path/extension.
+ *
+ * @param {string} path
+ * @return {boolean|string}
+ */
+
+function lookup (path) {
+  if (!path || typeof path !== 'string') {
+    return false
+  }
+
+  // get the extension ("ext" or ".ext" or full path)
+  var extension = extname('x.' + path)
+    .toLowerCase()
+    .substr(1)
+
+  if (!extension) {
+    return false
+  }
+
+  return exports.types[extension] || false
+}
+
+/**
+ * Populate the extensions and types maps.
+ * @private
+ */
+
+function populateMaps (extensions, types) {
+  // source preference (least -> most)
+  var preference = ['nginx', 'apache', undefined, 'iana']
+
+  Object.keys(db).forEach(function forEachMimeType (type) {
+    var mime = db[type]
+    var exts = mime.extensions
+
+    if (!exts || !exts.length) {
+      return
+    }
+
+    // mime -> extensions
+    extensions[type] = exts
+
+    // extension -> mime
+    for (var i = 0; i < exts.length; i++) {
+      var extension = exts[i]
+
+      if (types[extension]) {
+        var from = preference.indexOf(db[types[extension]].source)
+        var to = preference.indexOf(mime.source)
+
+        if (types[extension] !== 'application/octet-stream' &&
+          (from > to || (from === to && types[extension].substr(0, 12) === 'application/'))) {
+          // skip the remapping
+          continue
+        }
+      }
+
+      // set the extension -> mime
+      types[extension] = type
+    }
+  })
+}
diff --git a/tortoise/node_modules/mime-types/package.json b/tortoise/node_modules/mime-types/package.json
new file mode 100644
index 0000000..bbef696
--- /dev/null
+++ b/tortoise/node_modules/mime-types/package.json
@@ -0,0 +1,44 @@
+{
+  "name": "mime-types",
+  "description": "The ultimate javascript content-type utility.",
+  "version": "2.1.35",
+  "contributors": [
+    "Douglas Christopher Wilson <doug@somethingdoug.com>",
+    "Jeremiah Senkpiel <fishrock123@rocketmail.com> (https://searchbeam.jit.su)",
+    "Jonathan Ong <me@jongleberry.com> (http://jongleberry.com)"
+  ],
+  "license": "MIT",
+  "keywords": [
+    "mime",
+    "types"
+  ],
+  "repository": "jshttp/mime-types",
+  "dependencies": {
+    "mime-db": "1.52.0"
+  },
+  "devDependencies": {
+    "eslint": "7.32.0",
+    "eslint-config-standard": "14.1.1",
+    "eslint-plugin-import": "2.25.4",
+    "eslint-plugin-markdown": "2.2.1",
+    "eslint-plugin-node": "11.1.0",
+    "eslint-plugin-promise": "5.2.0",
+    "eslint-plugin-standard": "4.1.0",
+    "mocha": "9.2.2",
+    "nyc": "15.1.0"
+  },
+  "files": [
+    "HISTORY.md",
+    "LICENSE",
+    "index.js"
+  ],
+  "engines": {
+    "node": ">= 0.6"
+  },
+  "scripts": {
+    "lint": "eslint .",
+    "test": "mocha --reporter spec test/test.js",
+    "test-ci": "nyc --reporter=lcov --reporter=text npm test",
+    "test-cov": "nyc --reporter=html --reporter=text npm test"
+  }
+}
diff --git a/tortoise/node_modules/proxy-from-env/.eslintrc b/tortoise/node_modules/proxy-from-env/.eslintrc
new file mode 100644
index 0000000..a51449b
--- /dev/null
+++ b/tortoise/node_modules/proxy-from-env/.eslintrc
@@ -0,0 +1,29 @@
+{
+  "env": {
+    "node": true
+  },
+  "rules": {
+    "array-bracket-spacing": [2, "never"],
+    "block-scoped-var": 2,
+    "brace-style": [2, "1tbs"],
+    "camelcase": 1,
+    "computed-property-spacing": [2, "never"],
+    "curly": 2,
+    "eol-last": 2,
+    "eqeqeq": [2, "smart"],
+    "max-depth": [1, 3],
+    "max-len": [1, 80],
+    "max-statements": [1, 15],
+    "new-cap": 1,
+    "no-extend-native": 2,
+    "no-mixed-spaces-and-tabs": 2,
+    "no-trailing-spaces": 2,
+    "no-unused-vars": 1,
+    "no-use-before-define": [2, "nofunc"],
+    "object-curly-spacing": [2, "never"],
+    "quotes": [2, "single", "avoid-escape"],
+    "semi": [2, "always"],
+    "keyword-spacing": [2, {"before": true, "after": true}],
+    "space-unary-ops": 2
+  }
+}
diff --git a/tortoise/node_modules/proxy-from-env/.travis.yml b/tortoise/node_modules/proxy-from-env/.travis.yml
new file mode 100644
index 0000000..64a05f9
--- /dev/null
+++ b/tortoise/node_modules/proxy-from-env/.travis.yml
@@ -0,0 +1,10 @@
+language: node_js
+node_js:
+  - node
+  - lts/*
+script:
+  - npm run lint
+  # test-coverage will also run the tests, but does not print helpful output upon test failure.
+  # So we also run the tests separately.
+  - npm run test
+  - npm run test-coverage && cat coverage/lcov.info | ./node_modules/.bin/coveralls && rm -rf coverage
diff --git a/tortoise/node_modules/proxy-from-env/LICENSE b/tortoise/node_modules/proxy-from-env/LICENSE
new file mode 100644
index 0000000..8f25097
--- /dev/null
+++ b/tortoise/node_modules/proxy-from-env/LICENSE
@@ -0,0 +1,20 @@
+The MIT License
+
+Copyright (C) 2016-2018 Rob Wu <rob@robwu.nl>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
+of the Software, and to permit persons to whom the Software is furnished to do
+so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
diff --git a/tortoise/node_modules/proxy-from-env/README.md b/tortoise/node_modules/proxy-from-env/README.md
new file mode 100644
index 0000000..e82520c
--- /dev/null
+++ b/tortoise/node_modules/proxy-from-env/README.md
@@ -0,0 +1,131 @@
+# proxy-from-env
+
+[![Build Status](https://travis-ci.org/Rob--W/proxy-from-env.svg?branch=master)](https://travis-ci.org/Rob--W/proxy-from-env)
+[![Coverage Status](https://coveralls.io/repos/github/Rob--W/proxy-from-env/badge.svg?branch=master)](https://coveralls.io/github/Rob--W/proxy-from-env?branch=master)
+
+`proxy-from-env` is a Node.js package that exports a function (`getProxyForUrl`)
+that takes an input URL (a string or
+[`url.parse`](https://nodejs.org/docs/latest/api/url.html#url_url_parsing)'s
+return value) and returns the desired proxy URL (also a string) based on
+standard proxy environment variables. If no proxy is set, an empty string is
+returned.
+
+It is your responsibility to actually proxy the request using the given URL.
+
+Installation:
+
+```sh
+npm install proxy-from-env
+```
+
+## Example
+This example shows how the data for a URL can be fetched via the
+[`http` module](https://nodejs.org/api/http.html), in a proxy-aware way.
+
+```javascript
+var http = require('http');
+var parseUrl = require('url').parse;
+var getProxyForUrl = require('proxy-from-env').getProxyForUrl;
+
+var some_url = 'http://example.com/something';
+
+// // Example, if there is a proxy server at 10.0.0.1:1234, then setting the
+// // http_proxy environment variable causes the request to go through a proxy.
+// process.env.http_proxy = 'http://10.0.0.1:1234';
+// 
+// // But if the host to be proxied is listed in NO_PROXY, then the request is
+// // not proxied (but a direct request is made).
+// process.env.no_proxy = 'example.com';
+
+var proxy_url = getProxyForUrl(some_url);  // <-- Our magic.
+if (proxy_url) {
+  // Should be proxied through proxy_url.
+  var parsed_some_url = parseUrl(some_url);
+  var parsed_proxy_url = parseUrl(proxy_url);
+  // A HTTP proxy is quite simple. It is similar to a normal request, except the
+  // path is an absolute URL, and the proxied URL's host is put in the header
+  // instead of the server's actual host.
+  httpOptions = {
+    protocol: parsed_proxy_url.protocol,
+    hostname: parsed_proxy_url.hostname,
+    port: parsed_proxy_url.port,
+    path: parsed_some_url.href,
+    headers: {
+      Host: parsed_some_url.host,  // = host name + optional port.
+    },
+  };
+} else {
+  // Direct request.
+  httpOptions = some_url;
+}
+http.get(httpOptions, function(res) {
+  var responses = [];
+  res.on('data', function(chunk) { responses.push(chunk); });
+  res.on('end', function() { console.log(responses.join(''));  });
+});
+
+```
+
+## Environment variables
+The environment variables can be specified in lowercase or uppercase, with the
+lowercase name having precedence over the uppercase variant. A variable that is
+not set has the same meaning as a variable that is set but has no value.
+
+### NO\_PROXY
+
+`NO_PROXY` is a list of host names (optionally with a port). If the input URL
+matches any of the entries in `NO_PROXY`, then the input URL should be fetched
+by a direct request (i.e. without a proxy).
+
+Matching follows the following rules:
+
+- `NO_PROXY=*` disables all proxies.
+- Space and commas may be used to separate the entries in the `NO_PROXY` list.
+- If `NO_PROXY` does not contain any entries, then proxies are never disabled.
+- If a port is added after the host name, then the ports must match. If the URL
+  does not have an explicit port name, the protocol's default port is used.
+- Generally, the proxy is only disabled if the host name is an exact match for
+  an entry in the `NO_PROXY` list. The only exceptions are entries that start
+  with a dot or with a wildcard; then the proxy is disabled if the host name
+  ends with the entry.
+
+See `test.js` for examples of what should match and what does not.
+
+### \*\_PROXY
+
+The environment variable used for the proxy depends on the protocol of the URL.
+For example, `https://example.com` uses the "https" protocol, and therefore the
+proxy to be used is `HTTPS_PROXY` (_NOT_ `HTTP_PROXY`, which is _only_ used for
+http:-URLs).
+
+The library is not limited to http(s), other schemes such as
+`FTP_PROXY` (ftp:),
+`WSS_PROXY` (wss:),
+`WS_PROXY` (ws:)
+are also supported.
+
+If present, `ALL_PROXY` is used as fallback if there is no other match.
+
+
+## External resources
+The exact way of parsing the environment variables is not codified in any
+standard. This library is designed to be compatible with formats as expected by
+existing software.
+The following resources were used to determine the desired behavior:
+
+- cURL:
+  https://curl.haxx.se/docs/manpage.html#ENVIRONMENT  
+  https://github.com/curl/curl/blob/4af40b3646d3b09f68e419f7ca866ff395d1f897/lib/url.c#L4446-L4514  
+  https://github.com/curl/curl/blob/4af40b3646d3b09f68e419f7ca866ff395d1f897/lib/url.c#L4608-L4638  
+
+- wget: 
+  https://www.gnu.org/software/wget/manual/wget.html#Proxies  
+  http://git.savannah.gnu.org/cgit/wget.git/tree/src/init.c?id=636a5f9a1c508aa39e35a3a8e9e54520a284d93d#n383  
+  http://git.savannah.gnu.org/cgit/wget.git/tree/src/retr.c?id=93c1517c4071c4288ba5a4b038e7634e4c6b5482#n1278  
+
+- W3:
+  https://www.w3.org/Daemon/User/Proxies/ProxyClients.html  
+
+- Python's urllib:
+  https://github.com/python/cpython/blob/936135bb97fe04223aa30ca6e98eac8f3ed6b349/Lib/urllib/request.py#L755-L782  
+  https://github.com/python/cpython/blob/936135bb97fe04223aa30ca6e98eac8f3ed6b349/Lib/urllib/request.py#L2444-L2479
diff --git a/tortoise/node_modules/proxy-from-env/index.js b/tortoise/node_modules/proxy-from-env/index.js
new file mode 100644
index 0000000..df75004
--- /dev/null
+++ b/tortoise/node_modules/proxy-from-env/index.js
@@ -0,0 +1,108 @@
+'use strict';
+
+var parseUrl = require('url').parse;
+
+var DEFAULT_PORTS = {
+  ftp: 21,
+  gopher: 70,
+  http: 80,
+  https: 443,
+  ws: 80,
+  wss: 443,
+};
+
+var stringEndsWith = String.prototype.endsWith || function(s) {
+  return s.length <= this.length &&
+    this.indexOf(s, this.length - s.length) !== -1;
+};
+
+/**
+ * @param {string|object} url - The URL, or the result from url.parse.
+ * @return {string} The URL of the proxy that should handle the request to the
+ *  given URL. If no proxy is set, this will be an empty string.
+ */
+function getProxyForUrl(url) {
+  var parsedUrl = typeof url === 'string' ? parseUrl(url) : url || {};
+  var proto = parsedUrl.protocol;
+  var hostname = parsedUrl.host;
+  var port = parsedUrl.port;
+  if (typeof hostname !== 'string' || !hostname || typeof proto !== 'string') {
+    return '';  // Don't proxy URLs without a valid scheme or host.
+  }
+
+  proto = proto.split(':', 1)[0];
+  // Stripping ports in this way instead of using parsedUrl.hostname to make
+  // sure that the brackets around IPv6 addresses are kept.
+  hostname = hostname.replace(/:\d*$/, '');
+  port = parseInt(port) || DEFAULT_PORTS[proto] || 0;
+  if (!shouldProxy(hostname, port)) {
+    return '';  // Don't proxy URLs that match NO_PROXY.
+  }
+
+  var proxy =
+    getEnv('npm_config_' + proto + '_proxy') ||
+    getEnv(proto + '_proxy') ||
+    getEnv('npm_config_proxy') ||
+    getEnv('all_proxy');
+  if (proxy && proxy.indexOf('://') === -1) {
+    // Missing scheme in proxy, default to the requested URL's scheme.
+    proxy = proto + '://' + proxy;
+  }
+  return proxy;
+}
+
+/**
+ * Determines whether a given URL should be proxied.
+ *
+ * @param {string} hostname - The host name of the URL.
+ * @param {number} port - The effective port of the URL.
+ * @returns {boolean} Whether the given URL should be proxied.
+ * @private
+ */
+function shouldProxy(hostname, port) {
+  var NO_PROXY =
+    (getEnv('npm_config_no_proxy') || getEnv('no_proxy')).toLowerCase();
+  if (!NO_PROXY) {
+    return true;  // Always proxy if NO_PROXY is not set.
+  }
+  if (NO_PROXY === '*') {
+    return false;  // Never proxy if wildcard is set.
+  }
+
+  return NO_PROXY.split(/[,\s]/).every(function(proxy) {
+    if (!proxy) {
+      return true;  // Skip zero-length hosts.
+    }
+    var parsedProxy = proxy.match(/^(.+):(\d+)$/);
+    var parsedProxyHostname = parsedProxy ? parsedProxy[1] : proxy;
+    var parsedProxyPort = parsedProxy ? parseInt(parsedProxy[2]) : 0;
+    if (parsedProxyPort && parsedProxyPort !== port) {
+      return true;  // Skip if ports don't match.
+    }
+
+    if (!/^[.*]/.test(parsedProxyHostname)) {
+      // No wildcards, so stop proxying if there is an exact match.
+      return hostname !== parsedProxyHostname;
+    }
+
+    if (parsedProxyHostname.charAt(0) === '*') {
+      // Remove leading wildcard.
+      parsedProxyHostname = parsedProxyHostname.slice(1);
+    }
+    // Stop proxying if the hostname ends with the no_proxy host.
+    return !stringEndsWith.call(hostname, parsedProxyHostname);
+  });
+}
+
+/**
+ * Get the value for an environment variable.
+ *
+ * @param {string} key - The name of the environment variable.
+ * @return {string} The value of the environment variable.
+ * @private
+ */
+function getEnv(key) {
+  return process.env[key.toLowerCase()] || process.env[key.toUpperCase()] || '';
+}
+
+exports.getProxyForUrl = getProxyForUrl;
diff --git a/tortoise/node_modules/proxy-from-env/package.json b/tortoise/node_modules/proxy-from-env/package.json
new file mode 100644
index 0000000..be2b845
--- /dev/null
+++ b/tortoise/node_modules/proxy-from-env/package.json
@@ -0,0 +1,34 @@
+{
+  "name": "proxy-from-env",
+  "version": "1.1.0",
+  "description": "Offers getProxyForUrl to get the proxy URL for a URL, respecting the *_PROXY (e.g. HTTP_PROXY) and NO_PROXY environment variables.",
+  "main": "index.js",
+  "scripts": {
+    "lint": "eslint *.js",
+    "test": "mocha ./test.js --reporter spec",
+    "test-coverage": "istanbul cover ./node_modules/.bin/_mocha -- --reporter spec"
+  },
+  "repository": {
+    "type": "git",
+    "url": "https://github.com/Rob--W/proxy-from-env.git"
+  },
+  "keywords": [
+    "proxy",
+    "http_proxy",
+    "https_proxy",
+    "no_proxy",
+    "environment"
+  ],
+  "author": "Rob Wu <rob@robwu.nl> (https://robwu.nl/)",
+  "license": "MIT",
+  "bugs": {
+    "url": "https://github.com/Rob--W/proxy-from-env/issues"
+  },
+  "homepage": "https://github.com/Rob--W/proxy-from-env#readme",
+  "devDependencies": {
+    "coveralls": "^3.0.9",
+    "eslint": "^6.8.0",
+    "istanbul": "^0.4.5",
+    "mocha": "^7.1.0"
+  }
+}
diff --git a/tortoise/node_modules/proxy-from-env/test.js b/tortoise/node_modules/proxy-from-env/test.js
new file mode 100644
index 0000000..abf6542
--- /dev/null
+++ b/tortoise/node_modules/proxy-from-env/test.js
@@ -0,0 +1,483 @@
+/* eslint max-statements:0 */
+'use strict';
+
+var assert = require('assert');
+var parseUrl = require('url').parse;
+
+var getProxyForUrl = require('./').getProxyForUrl;
+
+// Runs the callback with process.env temporarily set to env.
+function runWithEnv(env, callback) {
+  var originalEnv = process.env;
+  process.env = env;
+  try {
+    callback();
+  } finally {
+    process.env = originalEnv;
+  }
+}
+
+// Defines a test case that checks whether getProxyForUrl(input) === expected.
+function testProxyUrl(env, expected, input) {
+  assert(typeof env === 'object' && env !== null);
+  // Copy object to make sure that the in param does not get modified between
+  // the call of this function and the use of it below.
+  env = JSON.parse(JSON.stringify(env));
+
+  var title = 'getProxyForUrl(' + JSON.stringify(input) + ')' +
+     ' === ' + JSON.stringify(expected);
+
+  // Save call stack for later use.
+  var stack = {};
+  Error.captureStackTrace(stack, testProxyUrl);
+  // Only use the last stack frame because that shows where this function is
+  // called, and that is sufficient for our purpose. No need to flood the logs
+  // with an uninteresting stack trace.
+  stack = stack.stack.split('\n', 2)[1];
+
+  it(title, function() {
+    var actual;
+    runWithEnv(env, function() {
+      actual = getProxyForUrl(input);
+    });
+    if (expected === actual) {
+      return;  // Good!
+    }
+    try {
+      assert.strictEqual(expected, actual); // Create a formatted error message.
+      // Should not happen because previously we determined expected !== actual.
+      throw new Error('assert.strictEqual passed. This is impossible!');
+    } catch (e) {
+      // Use the original stack trace, so we can see a helpful line number.
+      e.stack = e.message + stack;
+      throw e;
+    }
+  });
+}
+
+describe('getProxyForUrl', function() {
+  describe('No proxy variables', function() {
+    var env = {};
+    testProxyUrl(env, '', 'http://example.com');
+    testProxyUrl(env, '', 'https://example.com');
+    testProxyUrl(env, '', 'ftp://example.com');
+  });
+
+  describe('Invalid URLs', function() {
+    var env = {};
+    env.ALL_PROXY = 'http://unexpected.proxy';
+    testProxyUrl(env, '', 'bogus');
+    testProxyUrl(env, '', '//example.com');
+    testProxyUrl(env, '', '://example.com');
+    testProxyUrl(env, '', '://');
+    testProxyUrl(env, '', '/path');
+    testProxyUrl(env, '', '');
+    testProxyUrl(env, '', 'http:');
+    testProxyUrl(env, '', 'http:/');
+    testProxyUrl(env, '', 'http://');
+    testProxyUrl(env, '', 'prototype://');
+    testProxyUrl(env, '', 'hasOwnProperty://');
+    testProxyUrl(env, '', '__proto__://');
+    testProxyUrl(env, '', undefined);
+    testProxyUrl(env, '', null);
+    testProxyUrl(env, '', {});
+    testProxyUrl(env, '', {host: 'x', protocol: 1});
+    testProxyUrl(env, '', {host: 1, protocol: 'x'});
+  });
+
+  describe('http_proxy and HTTP_PROXY', function() {
+    var env = {};
+    env.HTTP_PROXY = 'http://http-proxy';
+
+    testProxyUrl(env, '', 'https://example');
+    testProxyUrl(env, 'http://http-proxy', 'http://example');
+    testProxyUrl(env, 'http://http-proxy', parseUrl('http://example'));
+
+    // eslint-disable-next-line camelcase
+    env.http_proxy = 'http://priority';
+    testProxyUrl(env, 'http://priority', 'http://example');
+  });
+
+  describe('http_proxy with non-sensical value', function() {
+    var env = {};
+    // Crazy values should be passed as-is. It is the responsibility of the
+    // one who launches the application that the value makes sense.
+    // TODO: Should we be stricter and perform validation?
+    env.HTTP_PROXY = 'Crazy \n!() { ::// }';
+    testProxyUrl(env, 'Crazy \n!() { ::// }', 'http://wow');
+
+    // The implementation assumes that the HTTP_PROXY environment variable is
+    // somewhat reasonable, and if the scheme is missing, it is added.
+    // Garbage in, garbage out some would say...
+    env.HTTP_PROXY = 'crazy without colon slash slash';
+    testProxyUrl(env, 'http://crazy without colon slash slash', 'http://wow');
+  });
+
+  describe('https_proxy and HTTPS_PROXY', function() {
+    var env = {};
+    // Assert that there is no fall back to http_proxy
+    env.HTTP_PROXY = 'http://unexpected.proxy';
+    testProxyUrl(env, '', 'https://example');
+
+    env.HTTPS_PROXY = 'http://https-proxy';
+    testProxyUrl(env, 'http://https-proxy', 'https://example');
+
+    // eslint-disable-next-line camelcase
+    env.https_proxy = 'http://priority';
+    testProxyUrl(env, 'http://priority', 'https://example');
+  });
+
+  describe('ftp_proxy', function() {
+    var env = {};
+    // Something else than http_proxy / https, as a sanity check.
+    env.FTP_PROXY = 'http://ftp-proxy';
+
+    testProxyUrl(env, 'http://ftp-proxy', 'ftp://example');
+    testProxyUrl(env, '', 'ftps://example');
+  });
+
+  describe('all_proxy', function() {
+    var env = {};
+    env.ALL_PROXY = 'http://catch-all';
+    testProxyUrl(env, 'http://catch-all', 'https://example');
+
+    // eslint-disable-next-line camelcase
+    env.all_proxy = 'http://priority';
+    testProxyUrl(env, 'http://priority', 'https://example');
+  });
+
+  describe('all_proxy without scheme', function() {
+    var env = {};
+    env.ALL_PROXY = 'noscheme';
+    testProxyUrl(env, 'http://noscheme', 'http://example');
+    testProxyUrl(env, 'https://noscheme', 'https://example');
+
+    // The module does not impose restrictions on the scheme.
+    testProxyUrl(env, 'bogus-scheme://noscheme', 'bogus-scheme://example');
+
+    // But the URL should still be valid.
+    testProxyUrl(env, '', 'bogus');
+  });
+
+  describe('no_proxy empty', function() {
+    var env = {};
+    env.HTTPS_PROXY = 'http://proxy';
+
+    // NO_PROXY set but empty.
+    env.NO_PROXY = '';
+    testProxyUrl(env, 'http://proxy', 'https://example');
+
+    // No entries in NO_PROXY (comma).
+    env.NO_PROXY = ',';
+    testProxyUrl(env, 'http://proxy', 'https://example');
+
+    // No entries in NO_PROXY (whitespace).
+    env.NO_PROXY = ' ';
+    testProxyUrl(env, 'http://proxy', 'https://example');
+
+    // No entries in NO_PROXY (multiple whitespace / commas).
+    env.NO_PROXY = ',\t,,,\n,  ,\r';
+    testProxyUrl(env, 'http://proxy', 'https://example');
+  });
+
+  describe('no_proxy=example (single host)', function() {
+    var env = {};
+    env.HTTP_PROXY = 'http://proxy';
+
+    env.NO_PROXY = 'example';
+    testProxyUrl(env, '', 'http://example');
+    testProxyUrl(env, '', 'http://example:80');
+    testProxyUrl(env, '', 'http://example:0');
+    testProxyUrl(env, '', 'http://example:1337');
+    testProxyUrl(env, 'http://proxy', 'http://sub.example');
+    testProxyUrl(env, 'http://proxy', 'http://prefexample');
+    testProxyUrl(env, 'http://proxy', 'http://example.no');
+    testProxyUrl(env, 'http://proxy', 'http://a.b.example');
+    testProxyUrl(env, 'http://proxy', 'http://host/example');
+  });
+
+  describe('no_proxy=sub.example (subdomain)', function() {
+    var env = {};
+    env.HTTP_PROXY = 'http://proxy';
+
+    env.NO_PROXY = 'sub.example';
+    testProxyUrl(env, 'http://proxy', 'http://example');
+    testProxyUrl(env, 'http://proxy', 'http://example:80');
+    testProxyUrl(env, 'http://proxy', 'http://example:0');
+    testProxyUrl(env, 'http://proxy', 'http://example:1337');
+    testProxyUrl(env, '', 'http://sub.example');
+    testProxyUrl(env, 'http://proxy', 'http://no.sub.example');
+    testProxyUrl(env, 'http://proxy', 'http://sub-example');
+    testProxyUrl(env, 'http://proxy', 'http://example.sub');
+  });
+
+  describe('no_proxy=example:80 (host + port)', function() {
+    var env = {};
+    env.HTTP_PROXY = 'http://proxy';
+
+    env.NO_PROXY = 'example:80';
+    testProxyUrl(env, '', 'http://example');
+    testProxyUrl(env, '', 'http://example:80');
+    testProxyUrl(env, '', 'http://example:0');
+    testProxyUrl(env, 'http://proxy', 'http://example:1337');
+    testProxyUrl(env, 'http://proxy', 'http://sub.example');
+    testProxyUrl(env, 'http://proxy', 'http://prefexample');
+    testProxyUrl(env, 'http://proxy', 'http://example.no');
+    testProxyUrl(env, 'http://proxy', 'http://a.b.example');
+  });
+
+  describe('no_proxy=.example (host suffix)', function() {
+    var env = {};
+    env.HTTP_PROXY = 'http://proxy';
+
+    env.NO_PROXY = '.example';
+    testProxyUrl(env, 'http://proxy', 'http://example');
+    testProxyUrl(env, 'http://proxy', 'http://example:80');
+    testProxyUrl(env, 'http://proxy', 'http://example:1337');
+    testProxyUrl(env, '', 'http://sub.example');
+    testProxyUrl(env, '', 'http://sub.example:80');
+    testProxyUrl(env, '', 'http://sub.example:1337');
+    testProxyUrl(env, 'http://proxy', 'http://prefexample');
+    testProxyUrl(env, 'http://proxy', 'http://example.no');
+    testProxyUrl(env, '', 'http://a.b.example');
+  });
+
+  describe('no_proxy=*', function() {
+    var env = {};
+    env.HTTP_PROXY = 'http://proxy';
+    env.NO_PROXY = '*';
+    testProxyUrl(env, '', 'http://example.com');
+  });
+
+  describe('no_proxy=*.example (host suffix with *.)', function() {
+    var env = {};
+    env.HTTP_PROXY = 'http://proxy';
+
+    env.NO_PROXY = '*.example';
+    testProxyUrl(env, 'http://proxy', 'http://example');
+    testProxyUrl(env, 'http://proxy', 'http://example:80');
+    testProxyUrl(env, 'http://proxy', 'http://example:1337');
+    testProxyUrl(env, '', 'http://sub.example');
+    testProxyUrl(env, '', 'http://sub.example:80');
+    testProxyUrl(env, '', 'http://sub.example:1337');
+    testProxyUrl(env, 'http://proxy', 'http://prefexample');
+    testProxyUrl(env, 'http://proxy', 'http://example.no');
+    testProxyUrl(env, '', 'http://a.b.example');
+  });
+
+  describe('no_proxy=*example (substring suffix)', function() {
+    var env = {};
+    env.HTTP_PROXY = 'http://proxy';
+
+    env.NO_PROXY = '*example';
+    testProxyUrl(env, '', 'http://example');
+    testProxyUrl(env, '', 'http://example:80');
+    testProxyUrl(env, '', 'http://example:1337');
+    testProxyUrl(env, '', 'http://sub.example');
+    testProxyUrl(env, '', 'http://sub.example:80');
+    testProxyUrl(env, '', 'http://sub.example:1337');
+    testProxyUrl(env, '', 'http://prefexample');
+    testProxyUrl(env, '', 'http://a.b.example');
+    testProxyUrl(env, 'http://proxy', 'http://example.no');
+    testProxyUrl(env, 'http://proxy', 'http://host/example');
+  });
+
+  describe('no_proxy=.*example (arbitrary wildcards are NOT supported)',
+      function() {
+    var env = {};
+    env.HTTP_PROXY = 'http://proxy';
+
+    env.NO_PROXY = '.*example';
+    testProxyUrl(env, 'http://proxy', 'http://example');
+    testProxyUrl(env, 'http://proxy', 'http://sub.example');
+    testProxyUrl(env, 'http://proxy', 'http://sub.example');
+    testProxyUrl(env, 'http://proxy', 'http://prefexample');
+    testProxyUrl(env, 'http://proxy', 'http://x.prefexample');
+    testProxyUrl(env, 'http://proxy', 'http://a.b.example');
+  });
+
+  describe('no_proxy=[::1],[::2]:80,10.0.0.1,10.0.0.2:80 (IP addresses)',
+      function() {
+    var env = {};
+    env.HTTP_PROXY = 'http://proxy';
+
+    env.NO_PROXY = '[::1],[::2]:80,10.0.0.1,10.0.0.2:80';
+    testProxyUrl(env, '', 'http://[::1]/');
+    testProxyUrl(env, '', 'http://[::1]:80/');
+    testProxyUrl(env, '', 'http://[::1]:1337/');
+
+    testProxyUrl(env, '', 'http://[::2]/');
+    testProxyUrl(env, '', 'http://[::2]:80/');
+    testProxyUrl(env, 'http://proxy', 'http://[::2]:1337/');
+
+    testProxyUrl(env, '', 'http://10.0.0.1/');
+    testProxyUrl(env, '', 'http://10.0.0.1:80/');
+    testProxyUrl(env, '', 'http://10.0.0.1:1337/');
+
+    testProxyUrl(env, '', 'http://10.0.0.2/');
+    testProxyUrl(env, '', 'http://10.0.0.2:80/');
+    testProxyUrl(env, 'http://proxy', 'http://10.0.0.2:1337/');
+  });
+
+  describe('no_proxy=127.0.0.1/32 (CIDR is NOT supported)', function() {
+    var env = {};
+    env.HTTP_PROXY = 'http://proxy';
+
+    env.NO_PROXY = '127.0.0.1/32';
+    testProxyUrl(env, 'http://proxy', 'http://127.0.0.1');
+    testProxyUrl(env, 'http://proxy', 'http://127.0.0.1/32');
+  });
+
+  describe('no_proxy=127.0.0.1 does NOT match localhost', function() {
+    var env = {};
+    env.HTTP_PROXY = 'http://proxy';
+
+    env.NO_PROXY = '127.0.0.1';
+    testProxyUrl(env, '', 'http://127.0.0.1');
+    // We're not performing DNS queries, so this shouldn't match.
+    testProxyUrl(env, 'http://proxy', 'http://localhost');
+  });
+
+  describe('no_proxy with protocols that have a default port', function() {
+    var env = {};
+    env.WS_PROXY = 'http://ws';
+    env.WSS_PROXY = 'http://wss';
+    env.HTTP_PROXY = 'http://http';
+    env.HTTPS_PROXY = 'http://https';
+    env.GOPHER_PROXY = 'http://gopher';
+    env.FTP_PROXY = 'http://ftp';
+    env.ALL_PROXY = 'http://all';
+
+    env.NO_PROXY = 'xxx:21,xxx:70,xxx:80,xxx:443';
+
+    testProxyUrl(env, '', 'http://xxx');
+    testProxyUrl(env, '', 'http://xxx:80');
+    testProxyUrl(env, 'http://http', 'http://xxx:1337');
+
+    testProxyUrl(env, '', 'ws://xxx');
+    testProxyUrl(env, '', 'ws://xxx:80');
+    testProxyUrl(env, 'http://ws', 'ws://xxx:1337');
+
+    testProxyUrl(env, '', 'https://xxx');
+    testProxyUrl(env, '', 'https://xxx:443');
+    testProxyUrl(env, 'http://https', 'https://xxx:1337');
+
+    testProxyUrl(env, '', 'wss://xxx');
+    testProxyUrl(env, '', 'wss://xxx:443');
+    testProxyUrl(env, 'http://wss', 'wss://xxx:1337');
+
+    testProxyUrl(env, '', 'gopher://xxx');
+    testProxyUrl(env, '', 'gopher://xxx:70');
+    testProxyUrl(env, 'http://gopher', 'gopher://xxx:1337');
+
+    testProxyUrl(env, '', 'ftp://xxx');
+    testProxyUrl(env, '', 'ftp://xxx:21');
+    testProxyUrl(env, 'http://ftp', 'ftp://xxx:1337');
+  });
+
+  describe('no_proxy should not be case-sensitive', function() {
+    var env = {};
+    env.HTTP_PROXY = 'http://proxy';
+    env.NO_PROXY = 'XXX,YYY,ZzZ';
+
+    testProxyUrl(env, '', 'http://xxx');
+    testProxyUrl(env, '', 'http://XXX');
+    testProxyUrl(env, '', 'http://yyy');
+    testProxyUrl(env, '', 'http://YYY');
+    testProxyUrl(env, '', 'http://ZzZ');
+    testProxyUrl(env, '', 'http://zZz');
+  });
+
+  describe('NPM proxy configuration', function() {
+    describe('npm_config_http_proxy should work', function() {
+      var env = {};
+      // eslint-disable-next-line camelcase
+      env.npm_config_http_proxy = 'http://http-proxy';
+
+      testProxyUrl(env, '', 'https://example');
+      testProxyUrl(env, 'http://http-proxy', 'http://example');
+
+      // eslint-disable-next-line camelcase
+      env.npm_config_http_proxy = 'http://priority';
+      testProxyUrl(env, 'http://priority', 'http://example');
+    });
+    // eslint-disable-next-line max-len
+    describe('npm_config_http_proxy should take precedence over HTTP_PROXY and npm_config_proxy', function() {
+      var env = {};
+      // eslint-disable-next-line camelcase
+      env.npm_config_http_proxy = 'http://http-proxy';
+      // eslint-disable-next-line camelcase
+      env.npm_config_proxy = 'http://unexpected-proxy';
+      env.HTTP_PROXY = 'http://unexpected-proxy';
+
+      testProxyUrl(env, 'http://http-proxy', 'http://example');
+    });
+    describe('npm_config_https_proxy should work', function() {
+      var env = {};
+      // eslint-disable-next-line camelcase
+      env.npm_config_http_proxy = 'http://unexpected.proxy';
+      testProxyUrl(env, '', 'https://example');
+
+      // eslint-disable-next-line camelcase
+      env.npm_config_https_proxy = 'http://https-proxy';
+      testProxyUrl(env, 'http://https-proxy', 'https://example');
+
+      // eslint-disable-next-line camelcase
+      env.npm_config_https_proxy = 'http://priority';
+      testProxyUrl(env, 'http://priority', 'https://example');
+    });
+    // eslint-disable-next-line max-len
+    describe('npm_config_https_proxy should take precedence over HTTPS_PROXY and npm_config_proxy', function() {
+      var env = {};
+      // eslint-disable-next-line camelcase
+      env.npm_config_https_proxy = 'http://https-proxy';
+      // eslint-disable-next-line camelcase
+      env.npm_config_proxy = 'http://unexpected-proxy';
+      env.HTTPS_PROXY = 'http://unexpected-proxy';
+
+      testProxyUrl(env, 'http://https-proxy', 'https://example');
+    });
+    describe('npm_config_proxy should work', function() {
+      var env = {};
+      // eslint-disable-next-line camelcase
+      env.npm_config_proxy = 'http://http-proxy';
+      testProxyUrl(env, 'http://http-proxy', 'http://example');
+      testProxyUrl(env, 'http://http-proxy', 'https://example');
+
+      // eslint-disable-next-line camelcase
+      env.npm_config_proxy = 'http://priority';
+      testProxyUrl(env, 'http://priority', 'http://example');
+      testProxyUrl(env, 'http://priority', 'https://example');
+    });
+    // eslint-disable-next-line max-len
+    describe('HTTP_PROXY and HTTPS_PROXY should take precedence over npm_config_proxy', function() {
+      var env = {};
+      env.HTTP_PROXY = 'http://http-proxy';
+      env.HTTPS_PROXY = 'http://https-proxy';
+      // eslint-disable-next-line camelcase
+      env.npm_config_proxy = 'http://unexpected-proxy';
+      testProxyUrl(env, 'http://http-proxy', 'http://example');
+      testProxyUrl(env, 'http://https-proxy', 'https://example');
+    });
+    describe('npm_config_no_proxy should work', function() {
+      var env = {};
+      env.HTTP_PROXY = 'http://proxy';
+      // eslint-disable-next-line camelcase
+      env.npm_config_no_proxy = 'example';
+
+      testProxyUrl(env, '', 'http://example');
+      testProxyUrl(env, 'http://proxy', 'http://otherwebsite');
+    });
+    // eslint-disable-next-line max-len
+    describe('npm_config_no_proxy should take precedence over NO_PROXY', function() {
+      var env = {};
+      env.HTTP_PROXY = 'http://proxy';
+      env.NO_PROXY = 'otherwebsite';
+      // eslint-disable-next-line camelcase
+      env.npm_config_no_proxy = 'example';
+
+      testProxyUrl(env, '', 'http://example');
+      testProxyUrl(env, 'http://proxy', 'http://otherwebsite');
+    });
+  });
+});
diff --git a/tortoise/package-lock.json b/tortoise/package-lock.json
new file mode 100644
index 0000000..b4dd33c
--- /dev/null
+++ b/tortoise/package-lock.json
@@ -0,0 +1,111 @@
+{
+  "name": "tortoise",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "dependencies": {
+        "axios": "^1.7.7"
+      }
+    },
+    "node_modules/asynckit": {
+      "version": "0.4.0",
+      "resolved": "https://registry.npmjs.org/asynckit/-/asynckit-0.4.0.tgz",
+      "integrity": "sha512-Oei9OH4tRh0YqU3GxhX79dM/mwVgvbZJaSNaRk+bshkj0S5cfHcgYakreBjrHwatXKbz+IoIdYLxrKim2MjW0Q==",
+      "license": "MIT"
+    },
+    "node_modules/axios": {
+      "version": "1.7.7",
+      "resolved": "https://registry.npmjs.org/axios/-/axios-1.7.7.tgz",
+      "integrity": "sha512-S4kL7XrjgBmvdGut0sN3yJxqYzrDOnivkBiN0OFs6hLiUam3UPvswUo0kqGyhqUZGEOytHyumEdXsAkgCOUf3Q==",
+      "license": "MIT",
+      "dependencies": {
+        "follow-redirects": "^1.15.6",
+        "form-data": "^4.0.0",
+        "proxy-from-env": "^1.1.0"
+      }
+    },
+    "node_modules/combined-stream": {
+      "version": "1.0.8",
+      "resolved": "https://registry.npmjs.org/combined-stream/-/combined-stream-1.0.8.tgz",
+      "integrity": "sha512-FQN4MRfuJeHf7cBbBMJFXhKSDq+2kAArBlmRBvcvFE5BB1HZKXtSFASDhdlz9zOYwxh8lDdnvmMOe/+5cdoEdg==",
+      "license": "MIT",
+      "dependencies": {
+        "delayed-stream": "~1.0.0"
+      },
+      "engines": {
+        "node": ">= 0.8"
+      }
+    },
+    "node_modules/delayed-stream": {
+      "version": "1.0.0",
+      "resolved": "https://registry.npmjs.org/delayed-stream/-/delayed-stream-1.0.0.tgz",
+      "integrity": "sha512-ZySD7Nf91aLB0RxL4KGrKHBXl7Eds1DAmEdcoVawXnLD7SDhpNgtuII2aAkg7a7QS41jxPSZ17p4VdGnMHk3MQ==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=0.4.0"
+      }
+    },
+    "node_modules/follow-redirects": {
+      "version": "1.15.9",
+      "resolved": "https://registry.npmjs.org/follow-redirects/-/follow-redirects-1.15.9.tgz",
+      "integrity": "sha512-gew4GsXizNgdoRyqmyfMHyAmXsZDk6mHkSxZFCzW9gwlbtOW44CDtYavM+y+72qD/Vq2l550kMF52DT8fOLJqQ==",
+      "funding": [
+        {
+          "type": "individual",
+          "url": "https://github.com/sponsors/RubenVerborgh"
+        }
+      ],
+      "license": "MIT",
+      "engines": {
+        "node": ">=4.0"
+      },
+      "peerDependenciesMeta": {
+        "debug": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/form-data": {
+      "version": "4.0.1",
+      "resolved": "https://registry.npmjs.org/form-data/-/form-data-4.0.1.tgz",
+      "integrity": "sha512-tzN8e4TX8+kkxGPK8D5u0FNmjPUjw3lwC9lSLxxoB/+GtsJG91CO8bSWy73APlgAZzZbXEYZJuxjkHH2w+Ezhw==",
+      "license": "MIT",
+      "dependencies": {
+        "asynckit": "^0.4.0",
+        "combined-stream": "^1.0.8",
+        "mime-types": "^2.1.12"
+      },
+      "engines": {
+        "node": ">= 6"
+      }
+    },
+    "node_modules/mime-db": {
+      "version": "1.52.0",
+      "resolved": "https://registry.npmjs.org/mime-db/-/mime-db-1.52.0.tgz",
+      "integrity": "sha512-sPU4uV7dYlvtWJxwwxHD0PuihVNiE7TyAbQ5SWxDCB9mUYvOgroQOwYQQOKPJ8CIbE+1ETVlOoK1UC2nU3gYvg==",
+      "license": "MIT",
+      "engines": {
+        "node": ">= 0.6"
+      }
+    },
+    "node_modules/mime-types": {
+      "version": "2.1.35",
+      "resolved": "https://registry.npmjs.org/mime-types/-/mime-types-2.1.35.tgz",
+      "integrity": "sha512-ZDY+bPm5zTTF+YpCrAU9nK0UgICYPT0QtT1NZWFv4s++TNkcgVaT0g6+4R2uI4MjQjzysHB1zxuWL50hzaeXiw==",
+      "license": "MIT",
+      "dependencies": {
+        "mime-db": "1.52.0"
+      },
+      "engines": {
+        "node": ">= 0.6"
+      }
+    },
+    "node_modules/proxy-from-env": {
+      "version": "1.1.0",
+      "resolved": "https://registry.npmjs.org/proxy-from-env/-/proxy-from-env-1.1.0.tgz",
+      "integrity": "sha512-D+zkORCbA9f1tdWRK0RaCR3GPv50cMxcrz4X8k5LTSUD1Dkw47mKJEZQNunItRTkWwgtaUSo1RVFRIG9ZXiFYg==",
+      "license": "MIT"
+    }
+  }
+}
diff --git a/tortoise/package.json b/tortoise/package.json
new file mode 100644
index 0000000..5100893
--- /dev/null
+++ b/tortoise/package.json
@@ -0,0 +1,5 @@
+{
+  "dependencies": {
+    "axios": "^1.7.7"
+  }
+}
diff --git a/tortoise/requirements.txt b/tortoise/requirements.txt
new file mode 100644
index 0000000..8015fc1
Binary files /dev/null and b/tortoise/requirements.txt differ
diff --git a/tortoise/scripts/tortoise_tts.py b/tortoise/scripts/tortoise_tts.py
new file mode 100644
index 0000000..932a780
--- /dev/null
+++ b/tortoise/scripts/tortoise_tts.py
@@ -0,0 +1,266 @@
+#!/usr/bin/env python3
+
+import argparse
+import os
+import sys
+import tempfile
+import time
+
+import torch
+import torchaudio
+
+from tortoise.api import MODELS_DIR, TextToSpeech
+from tortoise.utils.audio import get_voices, load_voices, load_audio
+from tortoise.utils.text import split_and_recombine_text
+
+parser = argparse.ArgumentParser(
+    description='TorToiSe is a text-to-speech program that is capable of synthesizing speech '
+                'in multiple voices with realistic prosody and intonation.')
+
+parser.add_argument(
+    'text', type=str, nargs='*',
+    help='Text to speak. If omitted, text is read from stdin.')
+parser.add_argument(
+    '-v, --voice', type=str, default='random', metavar='VOICE', dest='voice',
+    help='Selects the voice to use for generation. Use the & character to join two voices together. '
+         'Use a comma to perform inference on multiple voices. Set to "all" to use all available voices. '
+         'Note that multiple voices require the --output-dir option to be set.')
+parser.add_argument(
+    '-V, --voices-dir', metavar='VOICES_DIR', type=str, dest='voices_dir',
+    help='Path to directory containing extra voices to be loaded. Use a comma to specify multiple directories.')
+parser.add_argument(
+    '-p, --preset', type=str, default='fast', choices=['ultra_fast', 'fast', 'standard', 'high_quality'], dest='preset',
+    help='Which voice quality preset to use.')
+parser.add_argument(
+    '-q, --quiet', default=False, action='store_true', dest='quiet',
+    help='Suppress all output.')
+
+output_group = parser.add_mutually_exclusive_group(required=True)
+output_group.add_argument(
+    '-l, --list-voices', default=False, action='store_true', dest='list_voices',
+    help='List available voices and exit.')
+output_group.add_argument(
+    '-P, --play', action='store_true', dest='play',
+    help='Play the audio (requires pydub).')
+output_group.add_argument(
+    '-o, --output', type=str, metavar='OUTPUT', dest='output',
+    help='Save the audio to a file.')
+output_group.add_argument(
+    '-O, --output-dir', type=str, metavar='OUTPUT_DIR', dest='output_dir',
+    help='Save the audio to a directory as individual segments.')
+
+multi_output_group = parser.add_argument_group('multi-output options (requires --output-dir)')
+multi_output_group.add_argument(
+    '--candidates', type=int, default=1,
+    help='How many output candidates to produce per-voice. Note that only the first candidate is used in the combined output.')
+multi_output_group.add_argument(
+    '--regenerate', type=str, default=None,
+    help='Comma-separated list of clip numbers to re-generate.')
+multi_output_group.add_argument(
+    '--skip-existing', action='store_true',
+    help='Set to skip re-generating existing clips.')
+
+advanced_group = parser.add_argument_group('advanced options')
+advanced_group.add_argument(
+    '--produce-debug-state', default=False, action='store_true',
+    help='Whether or not to produce debug_states in current directory, which can aid in reproducing problems.')
+advanced_group.add_argument(
+    '--seed', type=int, default=None,
+    help='Random seed which can be used to reproduce results.')
+advanced_group.add_argument(
+    '--models-dir', type=str, default=MODELS_DIR,
+    help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to '
+         '~/.cache/tortoise/.models, so this should only be specified if you have custom checkpoints.')
+advanced_group.add_argument(
+    '--text-split', type=str, default=None,
+    help='How big chunks to split the text into, in the format <desired_length>,<max_length>.')
+advanced_group.add_argument(
+    '--disable-redaction', default=False, action='store_true',
+    help='Normally text enclosed in brackets are automatically redacted from the spoken output '
+         '(but are still rendered by the model), this can be used for prompt engineering. '
+         'Set this to disable this behavior.')
+advanced_group.add_argument(
+    '--device', type=str, default=None,
+    help='Device to use for inference.')
+advanced_group.add_argument(
+    '--batch-size', type=int, default=None,
+    help='Batch size to use for inference. If omitted, the batch size is set based on available GPU memory.')
+
+tuning_group = parser.add_argument_group('tuning options (overrides preset settings)')
+tuning_group.add_argument(
+    '--num-autoregressive-samples', type=int, default=None,
+    help='Number of samples taken from the autoregressive model, all of which are filtered using CLVP. '
+         'As TorToiSe is a probabilistic model, more samples means a higher probability of creating something "great".')
+tuning_group.add_argument(
+    '--temperature', type=float, default=None,
+    help='The softmax temperature of the autoregressive model.')
+tuning_group.add_argument(
+    '--length-penalty', type=float, default=None,
+    help='A length penalty applied to the autoregressive decoder. Higher settings causes the model to produce more terse outputs.')
+tuning_group.add_argument(
+    '--repetition-penalty', type=float, default=None,
+    help='A penalty that prevents the autoregressive decoder from repeating itself during decoding. '
+         'Can be used to reduce the incidence of long silences or "uhhhhhhs", etc.')
+tuning_group.add_argument(
+    '--top-p', type=float, default=None,
+    help='P value used in nucleus sampling. 0 to 1. Lower values mean the decoder produces more "likely" (aka boring) outputs.')
+tuning_group.add_argument(
+    '--max-mel-tokens', type=int, default=None,
+    help='Restricts the output length. 1 to 600. Each unit is 1/20 of a second.')
+tuning_group.add_argument(
+    '--cvvp-amount', type=float, default=None,
+    help='How much the CVVP model should influence the output.'
+    'Increasing this can in some cases reduce the likelihood of multiple speakers.')
+tuning_group.add_argument(
+    '--diffusion-iterations', type=int, default=None,
+    help='Number of diffusion steps to perform.  More steps means the network has more chances to iteratively'
+         'refine the output, which should theoretically mean a higher quality output. '
+         'Generally a value above 250 is not noticeably better, however.')
+tuning_group.add_argument(
+    '--cond-free', type=bool, default=None,
+    help='Whether or not to perform conditioning-free diffusion. Conditioning-free diffusion performs two forward passes for '
+         'each diffusion step: one with the outputs of the autoregressive model and one with no conditioning priors. The output '
+         'of the two is blended according to the cond_free_k value below. Conditioning-free diffusion is the real deal, and '
+         'dramatically improves realism.')
+tuning_group.add_argument(
+    '--cond-free-k', type=float, default=None,
+    help='Knob that determines how to balance the conditioning free signal with the conditioning-present signal. [0,inf]. '
+         'As cond_free_k increases, the output becomes dominated by the conditioning-free signal. '
+         'Formula is: output=cond_present_output*(cond_free_k+1)-cond_absenct_output*cond_free_k')
+tuning_group.add_argument(
+    '--diffusion-temperature', type=float, default=None,
+    help='Controls the variance of the noise fed into the diffusion model. [0,1]. Values at 0 '
+         'are the "mean" prediction of the diffusion network and will sound bland and smeared. ')
+
+usage_examples = f'''
+Examples:
+
+Read text using random voice and place it in a file:
+
+    {parser.prog} -o hello.wav "Hello, how are you?"
+
+Read text from stdin and play it using the tom voice:
+
+    echo "Say it like you mean it!" | {parser.prog} -P -v tom
+
+Read a text file using multiple voices and save the audio clips to a directory:
+
+    {parser.prog} -O /tmp/tts-results -v tom,emma <textfile.txt
+'''
+
+try:
+    args = parser.parse_args()
+except SystemExit as e:
+    if e.code == 0:
+        print(usage_examples)
+    sys.exit(e.code)
+
+extra_voice_dirs = args.voices_dir.split(',') if args.voices_dir else []
+all_voices = sorted(get_voices(extra_voice_dirs))
+
+if args.list_voices:
+    for v in all_voices:
+        print(v)
+    sys.exit(0)
+
+selected_voices = all_voices if args.voice == 'all' else args.voice.split(',')
+selected_voices = [v.split('&') if '&' in v else [v] for v in selected_voices]
+for voices in selected_voices:
+    for v in voices:
+        if v != 'random' and v not in all_voices:
+            parser.error(f'voice {v} not available, use --list-voices to see available voices.')
+
+if len(args.text) == 0:
+    text = ''
+    for line in sys.stdin:
+        text += line
+else:
+    text = ' '.join(args.text)
+text = text.strip()
+if args.text_split:
+    desired_length, max_length = [int(x) for x in args.text_split.split(',')]
+    if desired_length > max_length:
+        parser.error(f'--text-split: desired_length ({desired_length}) must be <= max_length ({max_length})')
+    texts = split_and_recombine_text(text, desired_length, max_length)
+else:
+    texts = split_and_recombine_text(text)
+if len(texts) == 0:
+    parser.error('no text provided')
+
+if args.output_dir:
+    os.makedirs(args.output_dir, exist_ok=True)
+else:
+    if len(selected_voices) > 1:
+        parser.error('cannot have multiple voices without --output-dir"')
+    if args.candidates > 1:
+        parser.error('cannot have multiple candidates without --output-dir"')
+
+# error out early if pydub isn't installed
+if args.play:
+    try:
+        import pydub
+        import pydub.playback
+    except ImportError:
+        parser.error('--play requires pydub to be installed, which can be done with "pip install pydub"')
+
+seed = int(time.time()) if args.seed is None else args.seed
+if not args.quiet:
+    print('Loading tts...')
+tts = TextToSpeech(models_dir=args.models_dir, enable_redaction=not args.disable_redaction,
+                   device=args.device, autoregressive_batch_size=args.batch_size)
+gen_settings = {
+    'use_deterministic_seed': seed,
+    'verbose': not args.quiet,
+    'k': args.candidates,
+    'preset': args.preset,
+}
+tuning_options = [
+    'num_autoregressive_samples', 'temperature', 'length_penalty', 'repetition_penalty', 'top_p',
+    'max_mel_tokens', 'cvvp_amount', 'diffusion_iterations', 'cond_free', 'cond_free_k', 'diffusion_temperature']
+for option in tuning_options:
+    if getattr(args, option) is not None:
+        gen_settings[option] = getattr(args, option)
+total_clips = len(texts) * len(selected_voices)
+regenerate_clips = [int(x) for x in args.regenerate.split(',')] if args.regenerate else None
+for voice_idx, voice in enumerate(selected_voices):
+    audio_parts = []
+    voice_samples, conditioning_latents = load_voices(voice, extra_voice_dirs)
+    for text_idx, text in enumerate(texts):
+        clip_name = f'{"-".join(voice)}_{text_idx:02d}'
+        if args.output_dir:
+            first_clip = os.path.join(args.output_dir, f'{clip_name}_00.wav')
+            if (args.skip_existing or (regenerate_clips and text_idx not in regenerate_clips)) and os.path.exists(first_clip):
+                audio_parts.append(load_audio(first_clip, 24000))
+                if not args.quiet:
+                    print(f'Skipping {clip_name}')
+                continue
+        if not args.quiet:
+            print(f'Rendering {clip_name} ({(voice_idx * len(texts) + text_idx + 1)} of {total_clips})...')
+            print('  ' + text)
+        gen = tts.tts_with_preset(
+            text, voice_samples=voice_samples, conditioning_latents=conditioning_latents, **gen_settings)
+        gen = gen if args.candidates > 1 else [gen]
+        for candidate_idx, audio in enumerate(gen):
+            audio = audio.squeeze(0).cpu()
+            if candidate_idx == 0:
+                audio_parts.append(audio)
+            if args.output_dir:
+                filename = f'{clip_name}_{candidate_idx:02d}.wav'
+                torchaudio.save(os.path.join(args.output_dir, filename), audio, 24000)
+
+    audio = torch.cat(audio_parts, dim=-1)
+    if args.output_dir:
+        filename = f'{"-".join(voice)}_combined.wav'
+        torchaudio.save(os.path.join(args.output_dir, filename), audio, 24000)
+    elif args.output:
+        filename = args.output if args.output else os.tmp
+        torchaudio.save(args.output, audio, 24000)
+    elif args.play:
+        f = tempfile.NamedTemporaryFile(suffix='.wav', delete=True)
+        torchaudio.save(f.name, audio, 24000)
+        pydub.playback.play(pydub.AudioSegment.from_wav(f.name))
+
+    if args.produce_debug_state:
+        os.makedirs('debug_states', exist_ok=True)
+        dbg_state = (seed, texts, voice_samples, conditioning_latents, args)
+        torch.save(dbg_state, os.path.join('debug_states', f'debug_{"-".join(voice)}.pth'))
diff --git a/tortoise/server.py b/tortoise/server.py
new file mode 100644
index 0000000..0dd1da9
--- /dev/null
+++ b/tortoise/server.py
@@ -0,0 +1,84 @@
+import asyncio
+import websockets
+import spacy
+from tortoise.api_fast import TextToSpeech
+from tortoise.utils.audio import load_voices
+
+tts = TextToSpeech()
+nlp = spacy.load("en_core_web_sm")
+
+
+def generate_audio_stream(text, tts, voice_samples):
+    print(f"Generating audio stream...: {text}")
+    voice_samples, conditioning_latents = load_voices([voice_samples])
+    stream = tts.tts_stream(
+        text,
+        voice_samples=voice_samples,
+        conditioning_latents=conditioning_latents,
+        verbose=True,
+        stream_chunk_size=40  # Adjust chunk size as needed
+    )
+    for audio_chunk in stream:
+        yield audio_chunk
+
+
+def split_text(text, max_length=200):
+    doc = nlp(text)
+    chunks = []
+    chunk = []
+    length = 0
+
+    for sent in doc.sents:
+        sent_length = len(sent.text)
+        if length + sent_length > max_length:
+            chunks.append(' '.join(chunk))
+            chunk = []
+            length = 0
+        chunk.append(sent.text)
+        length += sent_length + 1
+
+    if chunk:
+        chunks.append(' '.join(chunk))
+
+    return chunks
+
+
+async def handle_client(websocket, path):
+    try:
+        async for message in websocket:
+            if '|' in message:
+                character_name, text = message.split('|', 1)
+            else:
+                character_name = "deniro"  # 기본 캐릭터 이름 설정
+                text = message
+
+            text_chunks = split_text(text, max_length=200)
+            print(text_chunks)
+
+            with open("server_output.wav", 'wb') as f:  # 서버에서 음성 저장
+                for chunk in text_chunks:
+                    audio_stream = generate_audio_stream(chunk, tts, character_name)
+
+                    for audio_chunk in audio_stream:
+                        audio_data = audio_chunk.cpu().numpy().flatten()
+                        f.write(audio_data.tobytes())  # 서버에서 받은 음성을 저장
+
+            # 서버에서 생성된 파일을 클라이언트로 전송
+            with open("server_output.wav", 'rb') as audio_file:
+                await websocket.send(audio_file.read())
+
+            await websocket.send("END_OF_AUDIO")
+
+    finally:
+        print("Client disconnected.")
+
+
+async def start_server():
+    server = await websockets.serve(handle_client, "0.0.0.0", 5000)
+    print("Server listening on port 90")
+    await server.wait_closed()
+
+
+if __name__ == "__main__":
+    asyncio.run(start_server())
+
diff --git a/tortoise/setup.cfg b/tortoise/setup.cfg
new file mode 100644
index 0000000..08aedd7
--- /dev/null
+++ b/tortoise/setup.cfg
@@ -0,0 +1,2 @@
+[metadata]
+description_file = README.md
diff --git a/tortoise/setup.py b/tortoise/setup.py
new file mode 100644
index 0000000..29943bf
--- /dev/null
+++ b/tortoise/setup.py
@@ -0,0 +1,40 @@
+import setuptools
+
+with open("README.md", "r", encoding="utf-8") as fh:
+    long_description = fh.read()
+
+setuptools.setup(
+    name="tortoise-tts",
+    packages=setuptools.find_packages(),
+    version="3.0.0",
+    author="James Betker",
+    author_email="james@adamant.ai",
+    description="A high quality multi-voice text-to-speech library",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/neonbjb/tortoise-tts",
+    project_urls={},
+    scripts=[
+        'scripts/tortoise_tts.py',
+    ],
+    include_package_data=True,
+    install_requires=[
+        'tqdm',
+        'rotary_embedding_torch',
+        'inflect',
+        'progressbar',
+        'einops',
+        'unidecode',
+        'scipy',
+        'librosa',
+        'transformers==4.31.0',
+        'tokenizers',
+        # 'deepspeed==0.8.3',
+    ],
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "License :: OSI Approved :: Apache Software License",
+        "Operating System :: OS Independent",
+    ],
+    python_requires=">=3.6",
+)
diff --git a/tortoise/socket_client.py b/tortoise/socket_client.py
new file mode 100644
index 0000000..5192f64
--- /dev/null
+++ b/tortoise/socket_client.py
@@ -0,0 +1,65 @@
+import asyncio
+import websockets
+import argparse
+
+async def receive_and_save_audio(websocket, output_file):
+    buffer = b''
+    total_chunks_received = 0
+
+    try:
+        while True:
+            chunk = await websocket.recv()
+            total_chunks_received += 1
+
+            # Check if the received data marks the end of the audio stream
+            if isinstance(chunk, str) and chunk == "END_OF_AUDIO":
+                print("End of audio signal received.")
+                break
+
+            # Accumulate the buffer
+            buffer += chunk
+
+        # Write the raw byte data to a file
+        with open(output_file, 'wb') as file:
+            file.write(buffer)
+        
+        print(f"Audio saved to {output_file}")
+        print(f"Total chunks received: {total_chunks_received}")
+        print(f"Total audio length (bytes): {len(buffer)}")
+
+    except Exception as e:
+        print(f"Error receiving audio: {e}")
+
+async def send_text_to_server(character_name, text, output_file, server_ip='localhost', server_port=5000):
+    uri = f"ws://{server_ip}:{server_port}"
+    async with websockets.connect(uri) as websocket:
+        # Format the data to be sent to the server
+        data = f"{character_name}|{text}"
+        print(f"Sending text to server: {data}")
+        await websocket.send(data)
+
+        # Confirm sending was successful
+        print("Text sent successfully. Awaiting audio stream...")
+        
+        await receive_and_save_audio(websocket, output_file)
+
+async def main(server_ip='localhost', server_port=5000):
+    character_name = "deniro"
+
+    while True:
+        # Prompt the user for text input
+        text = input("Enter text to convert to speech (or 'quit' to exit): ")
+
+        if text.lower() == 'quit':
+            print("Exiting the client...")
+            break
+
+        # Generate a unique output file name
+        output_file = "output.raw"
+
+        # Send the text to the server
+        await send_text_to_server(character_name, text, output_file, server_ip, server_port)
+
+if __name__ == "__main__":
+    # Run the main function
+    asyncio.run(main())
diff --git a/tortoise/socket_client_raw.py b/tortoise/socket_client_raw.py
new file mode 100644
index 0000000..5192f64
--- /dev/null
+++ b/tortoise/socket_client_raw.py
@@ -0,0 +1,65 @@
+import asyncio
+import websockets
+import argparse
+
+async def receive_and_save_audio(websocket, output_file):
+    buffer = b''
+    total_chunks_received = 0
+
+    try:
+        while True:
+            chunk = await websocket.recv()
+            total_chunks_received += 1
+
+            # Check if the received data marks the end of the audio stream
+            if isinstance(chunk, str) and chunk == "END_OF_AUDIO":
+                print("End of audio signal received.")
+                break
+
+            # Accumulate the buffer
+            buffer += chunk
+
+        # Write the raw byte data to a file
+        with open(output_file, 'wb') as file:
+            file.write(buffer)
+        
+        print(f"Audio saved to {output_file}")
+        print(f"Total chunks received: {total_chunks_received}")
+        print(f"Total audio length (bytes): {len(buffer)}")
+
+    except Exception as e:
+        print(f"Error receiving audio: {e}")
+
+async def send_text_to_server(character_name, text, output_file, server_ip='localhost', server_port=5000):
+    uri = f"ws://{server_ip}:{server_port}"
+    async with websockets.connect(uri) as websocket:
+        # Format the data to be sent to the server
+        data = f"{character_name}|{text}"
+        print(f"Sending text to server: {data}")
+        await websocket.send(data)
+
+        # Confirm sending was successful
+        print("Text sent successfully. Awaiting audio stream...")
+        
+        await receive_and_save_audio(websocket, output_file)
+
+async def main(server_ip='localhost', server_port=5000):
+    character_name = "deniro"
+
+    while True:
+        # Prompt the user for text input
+        text = input("Enter text to convert to speech (or 'quit' to exit): ")
+
+        if text.lower() == 'quit':
+            print("Exiting the client...")
+            break
+
+        # Generate a unique output file name
+        output_file = "output.raw"
+
+        # Send the text to the server
+        await send_text_to_server(character_name, text, output_file, server_ip, server_port)
+
+if __name__ == "__main__":
+    # Run the main function
+    asyncio.run(main())
diff --git a/tortoise/socket_client_wav.py b/tortoise/socket_client_wav.py
new file mode 100644
index 0000000..a616b40
--- /dev/null
+++ b/tortoise/socket_client_wav.py
@@ -0,0 +1,68 @@
+import asyncio
+import websockets
+import soundfile as sf
+import numpy as np
+import os
+
+async def receive_and_save_audio(websocket, output_file):
+    buffer = b''
+    total_chunks_received = 0
+
+    try:
+        while True:
+            chunk = await websocket.recv()
+            total_chunks_received += 1
+
+            # Check if the received data marks the end of the audio stream
+            if isinstance(chunk, str) and chunk == "END_OF_AUDIO":
+                print("End of audio signal received.")
+                break
+
+            # Accumulate the buffer
+            buffer += chunk
+
+        # Convert the buffer to a numpy array
+        audio_data = np.frombuffer(buffer, dtype=np.float32)
+
+        # Write the numpy array to a .wav file
+        sf.write(output_file, audio_data, 24000, format='WAV')
+        print(f"Audio saved to {output_file}")
+        print(f"Total chunks received: {total_chunks_received}")
+        print(f"Total audio length (samples): {len(audio_data)}")
+
+    except Exception as e:
+        print(f"Error receiving audio: {e}")
+
+async def send_text_to_server(character_name, text, output_file, server_ip='localhost', server_port=5000):
+    uri = f"ws://{server_ip}:{server_port}"
+    async with websockets.connect(uri) as websocket:
+        # Format the data to be sent to the server
+        data = f"{character_name}|{text}"
+        print(f"Sending text to server: {data}")
+        await websocket.send(data)
+
+        # Confirm sending was successful
+        print("Text sent successfully. Awaiting audio stream...")
+        
+        await receive_and_save_audio(websocket, output_file)
+
+async def main(server_ip='localhost', server_port=5000):
+    character_name = "deniro"
+
+    while True:
+        # Prompt the user for text input
+        text = input("Enter text to convert to speech (or 'quit' to exit): ")
+
+        if text.lower() == 'quit':
+            print("Exiting the client...")
+            break
+
+        # Generate a unique output file name
+        output_file = f"output_{character_name}_{len(text)}.wav"
+
+        # Send the text to the server
+        await send_text_to_server(character_name, text, output_file, server_ip, server_port)
+
+if __name__ == "__main__":
+    # Run the main function
+    asyncio.run(main())
diff --git a/tortoise/socket_server.py b/tortoise/socket_server.py
new file mode 100644
index 0000000..15dc300
--- /dev/null
+++ b/tortoise/socket_server.py
@@ -0,0 +1,91 @@
+import asyncio
+import websockets
+import spacy
+import json
+from tortoise.api_fast import TextToSpeech
+from tortoise.utils.audio import load_voices
+
+tts = TextToSpeech()
+nlp = spacy.load("en_core_web_sm")
+
+
+def generate_audio_stream(text, tts, voice_samples):
+    print(f"Generating audio stream...: {text}")
+    voice_samples, conditioning_latents = load_voices([voice_samples])
+    stream = tts.tts_stream(
+        text,
+        voice_samples=voice_samples,
+        conditioning_latents=conditioning_latents,
+        verbose=True,
+        stream_chunk_size=40  # Adjust chunk size as needed
+    )
+    for audio_chunk in stream:
+        yield audio_chunk
+
+
+def split_text(text, max_length=200):
+    doc = nlp(text)
+    chunks = []
+    chunk = []
+    length = 0
+
+    for sent in doc.sents:
+        sent_length = len(sent.text)
+        if length + sent_length > max_length:
+            chunks.append(' '.join(chunk))
+            chunk = []
+            length = 0
+        chunk.append(sent.text)
+        length += sent_length + 1
+
+    if chunk:
+        chunks.append(' '.join(chunk))
+
+    return chunks
+
+
+async def handle_client(websocket, path):
+    try:
+        async for data in websocket:
+            try:
+                data = json.loads(data)
+                sender_id = data.get('senderId')
+                message = data.get('message')
+                
+                if sender_id is not None:
+                    print(f"연결된 사용자: {sender_id}")
+                    print(f"메시지: {message}")
+                
+                if '|' in message:
+                    character_name, text = message.split('|', 1)
+                else:
+                    # user_id를 character_name으로 사용
+                    character_name = str(sender_id)
+                    text = message
+
+                text_chunks = split_text(text, max_length=200)
+                #print(f"사용자 {sender_id}의 텍스트 청크: {text_chunks}")
+                for chunk in text_chunks:
+                    audio_stream = generate_audio_stream(chunk, tts, character_name)
+
+                    for audio_chunk in audio_stream:
+                        audio_data = audio_chunk.cpu().numpy().flatten()
+                        await websocket.send(audio_data.tobytes())
+
+                await websocket.send("END_OF_AUDIO")
+            except json.JSONDecodeError:
+                print("잘못된 JSON 형식입니다.")
+            except KeyError as e:
+                print(f"필수 키가 누락되었습니다: {e}")
+    finally:
+        print(f"사용자 {sender_id} 연결 해제.")
+
+
+async def start_server():
+    server = await websockets.serve(handle_client, "0.0.0.0", 5000)
+    print("Server listening on port 5000")
+    await server.wait_closed()
+
+
+if __name__ == "__main__":
+    asyncio.run(start_server())
diff --git a/tortoise/socket_server1.py b/tortoise/socket_server1.py
new file mode 100644
index 0000000..719ad80
--- /dev/null
+++ b/tortoise/socket_server1.py
@@ -0,0 +1,65 @@
+import asyncio
+import websockets
+import argparse
+
+async def receive_and_save_audio(websocket, output_file):
+    buffer = b''
+    total_chunks_received = 0
+
+    try:
+        while True:
+            chunk = await websocket.recv()
+            total_chunks_received += 1
+
+            # Check if the received data marks the end of the audio stream
+            if isinstance(chunk, str) and chunk == "END_OF_AUDIO":
+                print("End of audio signal received.")
+                break
+
+            # Accumulate the buffer
+            buffer += chunk
+
+        # Write the raw byte data to a file
+        with open(output_file, 'wb') as file:
+            file.write(buffer)
+        
+        print(f"Audio saved to {output_file}")
+        print(f"Total chunks received: {total_chunks_received}")
+        print(f"Total audio length (bytes): {len(buffer)}")
+
+    except Exception as e:
+        print(f"Error receiving audio: {e}")
+
+async def send_text_to_server(character_name, text, output_file, server_ip='localhost', server_port=5000):
+    uri = f"ws://{server_ip}:{server_port}"
+    async with websockets.connect(uri) as websocket:
+        # Format the data to be sent to the server
+        data = f"{character_name}|{text}"
+        print(f"Sending text to server: {data}")
+        await websocket.send(data)
+
+        # Confirm sending was successful
+        print("Text sent successfully. Awaiting audio stream...")
+        
+        await receive_and_save_audio(websocket, output_file)
+
+async def main(server_ip='localhost', server_port=5000):
+    character_name = "deniro"
+
+    while True:
+        # Prompt the user for text input
+        text = input("Enter text to convert to speech (or 'quit' to exit): ")
+
+        if text.lower() == 'quit':
+            print("Exiting the client...")
+            break
+
+        # Generate a unique output file name
+        output_file = f"output_{character_name}_{len(text)}.raw"
+
+        # Send the text to the server
+        await send_text_to_server(character_name, text, output_file, server_ip, server_port)
+
+if __name__ == "__main__":
+    # Run the main function
+    asyncio.run(main())
diff --git a/tortoise/socket_server_json.py b/tortoise/socket_server_json.py
new file mode 100644
index 0000000..e9a39f0
--- /dev/null
+++ b/tortoise/socket_server_json.py
@@ -0,0 +1,112 @@
+import asyncio
+import websockets
+import spacy
+import json
+import os
+import uuid
+import numpy as np
+from scipy.io import wavfile
+from tortoise.api_fast import TextToSpeech
+from tortoise.utils.audio import load_voices
+
+tts = TextToSpeech()
+nlp = spacy.load("en_core_web_sm")
+
+
+def generate_audio_stream(text, tts, voice_samples):
+    print(f"Generating audio stream...: {text}")
+    voice_samples, conditioning_latents = load_voices([voice_samples])
+    stream = tts.tts_stream(
+        text,
+        voice_samples=voice_samples,
+        conditioning_latents=conditioning_latents,
+        verbose=True,
+        stream_chunk_size=40  # Adjust chunk size as needed
+    )
+    for audio_chunk in stream:
+        yield audio_chunk
+
+
+def split_text(text, max_length=200):
+    doc = nlp(text)
+    chunks = []
+    chunk = []
+    length = 0
+
+    for sent in doc.sents:
+        sent_length = len(sent.text)
+        if length + sent_length > max_length:
+            chunks.append(' '.join(chunk))
+            chunk = []
+            length = 0
+        chunk.append(sent.text)
+        length += sent_length + 1
+
+    if chunk:
+        chunks.append(' '.join(chunk))
+
+    return chunks
+
+
+async def handle_client(websocket, path):
+    try:
+        async for data in websocket:
+            try:
+                data = json.loads(data)
+                
+                sender_id = data.get('senderId')
+                message = data.get('message')
+                
+                if sender_id is not None:
+                    print(f"연결된 사용자: {sender_id}")
+                    print(f"메시지: {message}")
+                
+                if '|' in message:
+                    character_name, text = message.split('|', 1)
+                else:
+                    # user_id를 character_name으로 사용
+                    # if weight folder created, change to senderId
+                    character_name = "deniro"# str(sender_id)
+                    text = message
+
+                text_chunks = split_text(text, max_length=200)
+                print(f"사용자 {sender_id}의 텍스트 청크: {text_chunks}")
+                audio_chunks = []
+                file_path = "audio_files"
+
+                for chunk in text_chunks:
+                    audio_stream = generate_audio_stream(chunk, tts, character_name)
+                    for audio_chunk in audio_stream:
+                        audio_data = audio_chunk
+                    #    audio_data = audio_chunk.cpu().numpy().flatten()
+                    #    audio_chunks.append(audio_data)
+                    #    print("len of audio_data : ",len(audio_data))
+                    #    await websocket.send(audio_data.tobytes())
+                    
+                # 모든 오디오 청크를 하나의 파일로 저장
+                if audio_chunks:
+                    combined_audio = np.concatenate(audio_chunks)
+                    await websocket.send(combined_audio.tobytes())
+                    file_name = f"{character_name}.wav"
+                    file_path = os.path.join("audio_files", file_name)
+                    os.makedirs("audio_files", exist_ok=True)
+                    wavfile.write(file_path, 24000, combined_audio)
+
+                await websocket.send("END_OF_AUDIO")
+            except json.JSONDecodeError:
+                print("잘못된 JSON 형식입니다.")
+            except KeyError as e:
+                print(f"필수 키가 누락되었습니다: {e}")
+    finally:
+        print(f"사용자 {sender_id} 연결 해제.")
+
+
+async def start_server():
+    server = await websockets.serve(handle_client, "0.0.0.0", 5000)
+    print("서버가 5000번 포트에서 리스닝 중입니다.")
+    await server.wait_closed()
+
+
+if __name__ == "__main__":
+    asyncio.run(start_server())
+
diff --git a/tortoise/socket_server_raw.py b/tortoise/socket_server_raw.py
new file mode 100644
index 0000000..bb026f6
--- /dev/null
+++ b/tortoise/socket_server_raw.py
@@ -0,0 +1,88 @@
+import asyncio
+import websockets
+import spacy
+from tortoise.api_fast import TextToSpeech
+from tortoise.utils.audio import load_voices
+
+tts = TextToSpeech()
+nlp = spacy.load("en_core_web_sm")
+
+
+def generate_audio_stream(text, tts, voice_samples):
+    print(f"Generating audio stream...: {text}")
+    voice_samples, conditioning_latents = load_voices([voice_samples])
+    stream = tts.tts_stream(
+        text,
+        voice_samples=voice_samples,
+        conditioning_latents=conditioning_latents,
+        verbose=True,
+        stream_chunk_size=40  # Adjust chunk size as needed
+    )
+    for audio_chunk in stream:
+        yield audio_chunk
+
+
+def split_text(text, max_length=200):
+    doc = nlp(text)
+    chunks = []
+    chunk = []
+    length = 0
+
+    for sent in doc.sents:
+        sent_length = len(sent.text)
+        if length + sent_length > max_length:
+            chunks.append(' '.join(chunk))
+            chunk = []
+            length = 0
+        chunk.append(sent.text)
+        length += sent_length + 1
+
+    if chunk:
+        chunks.append(' '.join(chunk))
+
+    return chunks
+
+import torch
+async def handle_client(websocket, path):
+    try:
+        async for message in websocket:
+            if '|' in message:
+                character_name, text = message.split('|', 1)
+            else:
+                # 기본값으로 처리
+                character_name = "deniro"  # 기본 캐릭터 이름 설정
+                text = message
+
+            text_chunks = split_text(text, max_length=200)
+            print(text_chunks)
+            for chunk in text_chunks:
+                audio_stream = generate_audio_stream(chunk, tts, character_name)
+
+                for audio_chunk in audio_stream:
+                    # raw 데이터로 전송 (numpy 변환 제거)
+                    if isinstance(audio_chunk, torch.Tensor):
+                        # torch.Tensor인 경우 CPU로 이동한 후 numpy 변환하지 않고 그대로 전송
+                        raw_data = audio_chunk.cpu()
+                    elif isinstance(audio_chunk, np.ndarray):
+                        raw_data = audio_chunk
+                    else:
+                        # 그 외의 경우 적절한 형식으로 처리
+                        raise TypeError("Unsupported audio chunk type")
+
+                    await websocket.send(raw_data.tobytes())
+
+            await websocket.send("END_OF_AUDIO")
+
+    finally:
+        print("Client disconnected.")
+
+
+
+async def start_server():
+    server = await websockets.serve(handle_client, "0.0.0.0", 5000)
+    print("Server listening on port 5000")
+    await server.wait_closed()
+
+
+if __name__ == "__main__":
+    asyncio.run(start_server())
diff --git a/tortoise/tortoise/__init__.py b/tortoise/tortoise/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tortoise/tortoise/api.py b/tortoise/tortoise/api.py
new file mode 100644
index 0000000..8ab37eb
--- /dev/null
+++ b/tortoise/tortoise/api.py
@@ -0,0 +1,609 @@
+import os
+import random
+import uuid
+from time import time
+from urllib import request
+
+import torch
+import torch.nn.functional as F
+import progressbar
+import torchaudio
+
+from tortoise.models.classifier import AudioMiniEncoderWithClassifierHead
+from tortoise.models.diffusion_decoder import DiffusionTts
+from tortoise.models.autoregressive import UnifiedVoice
+from tqdm import tqdm
+from tortoise.models.arch_util import TorchMelSpectrogram
+from tortoise.models.clvp import CLVP
+from tortoise.models.cvvp import CVVP
+from tortoise.models.random_latent_generator import RandomLatentConverter
+from tortoise.models.vocoder import UnivNetGenerator
+from tortoise.utils.audio import wav_to_univnet_mel, denormalize_tacotron_mel, TacotronSTFT
+from tortoise.utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule
+from tortoise.utils.tokenizer import VoiceBpeTokenizer
+from tortoise.utils.wav2vec_alignment import Wav2VecAlignment
+from contextlib import contextmanager
+from huggingface_hub import hf_hub_download
+pbar = None
+
+DEFAULT_MODELS_DIR = os.path.join(os.path.expanduser('~'), '.cache', 'tortoise', 'models')
+MODELS_DIR = os.environ.get('TORTOISE_MODELS_DIR', DEFAULT_MODELS_DIR)
+MODELS = {
+    'autoregressive.pth': 'https://huggingface.co/doammii/tortoise-tts-v2/resolve/main/3420_gpt.pth',
+    'classifier.pth': 'https://huggingface.co/doammii/tortoise-tts-v2/resolve/main/3420_gpt.pth',
+    'clvp2.pth': 'https://huggingface.co/doammii/tortoise-tts-v2/resolve/main/3420_gpt.pth',
+    'cvvp.pth': 'https://huggingface.co/doammii/tortoise-tts-v2/resolve/main/3420_gpt.pth',
+    'diffusion_decoder.pth': 'https://huggingface.co/doammii/tortoise-tts-v2/resolve/main/3420_gpt.pth',
+    'vocoder.pth': 'https://huggingface.co/doammii/tortoise-tts-v2/resolve/main/3420_gpt.pth',
+    'rlg_auto.pth': 'https://huggingface.co/doammii/tortoise-tts-v2/resolve/main/3420_gpt.pth',
+    'rlg_diffuser.pth': 'https://huggingface.co/doammii/tortoise-tts-v2/resolve/main/3420_gpt.pth',
+}
+
+def get_model_path(model_name, models_dir=MODELS_DIR):
+    """
+    Get path to given model, download it if it doesn't exist.
+    """
+    if model_name not in MODELS:
+        raise ValueError(f'Model {model_name} not found in available models.')
+    model_path = hf_hub_download(repo_id="Manmay/tortoise-tts", filename=model_name, cache_dir=models_dir)
+    return model_path
+
+
+def pad_or_truncate(t, length):
+    """
+    Utility function for forcing <t> to have the specified sequence length, whether by clipping it or padding it with 0s.
+    """
+    if t.shape[-1] == length:
+        return t
+    elif t.shape[-1] < length:
+        return F.pad(t, (0, length-t.shape[-1]))
+    else:
+        return t[..., :length]
+
+
+def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusion_steps=200, cond_free=True, cond_free_k=1):
+    """
+    Helper function to load a GaussianDiffusion instance configured for use as a vocoder.
+    """
+    return SpacedDiffusion(use_timesteps=space_timesteps(trained_diffusion_steps, [desired_diffusion_steps]), model_mean_type='epsilon',
+                           model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', trained_diffusion_steps),
+                           conditioning_free=cond_free, conditioning_free_k=cond_free_k)
+
+
+def format_conditioning(clip, cond_length=132300, device="cuda" if not torch.backends.mps.is_available() else 'mps'):
+    """
+    Converts the given conditioning signal to a MEL spectrogram and clips it as expected by the models.
+    """
+    gap = clip.shape[-1] - cond_length
+    if gap < 0:
+        clip = F.pad(clip, pad=(0, abs(gap)))
+    elif gap > 0:
+        rand_start = random.randint(0, gap)
+        clip = clip[:, rand_start:rand_start + cond_length]
+    mel_clip = TorchMelSpectrogram()(clip.unsqueeze(0)).squeeze(0)
+    return mel_clip.unsqueeze(0).to(device)
+
+
+def fix_autoregressive_output(codes, stop_token, complain=True):
+    """
+    This function performs some padding on coded audio that fixes a mismatch issue between what the diffusion model was
+    trained on and what the autoregressive code generator creates (which has no padding or end).
+    This is highly specific to the DVAE being used, so this particular coding will not necessarily work if used with
+    a different DVAE. This can be inferred by feeding a audio clip padded with lots of zeros on the end through the DVAE
+    and copying out the last few codes.
+
+    Failing to do this padding will produce speech with a harsh end that sounds like "BLAH" or similar.
+    """
+    # Strip off the autoregressive stop token and add padding.
+    stop_token_indices = (codes == stop_token).nonzero()
+    if len(stop_token_indices) == 0:
+        if complain:
+            print("No stop tokens found in one of the generated voice clips. This typically means the spoken audio is "
+                  "too long. In some cases, the output will still be good, though. Listen to it and if it is missing words, "
+                  "try breaking up your input text.")
+        return codes
+    else:
+        codes[stop_token_indices] = 83
+    stm = stop_token_indices.min().item()
+    codes[stm:] = 83
+    if stm - 3 < codes.shape[0]:
+        codes[-3] = 45
+        codes[-2] = 45
+        codes[-1] = 248
+
+    return codes
+
+
+def do_spectrogram_diffusion(diffusion_model, diffuser, latents, conditioning_latents, temperature=1, verbose=True):
+    """
+    Uses the specified diffusion model to convert discrete codes into a spectrogram.
+    """
+    with torch.no_grad():
+        output_seq_len = latents.shape[1] * 4 * 24000 // 22050  # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal.
+        output_shape = (latents.shape[0], 100, output_seq_len)
+        precomputed_embeddings = diffusion_model.timestep_independent(latents, conditioning_latents, output_seq_len, False)
+
+        noise = torch.randn(output_shape, device=latents.device) * temperature
+        mel = diffuser.p_sample_loop(diffusion_model, output_shape, noise=noise,
+                                      model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings},
+                                     progress=verbose)
+        return denormalize_tacotron_mel(mel)[:,:,:output_seq_len]
+
+
+def classify_audio_clip(clip):
+    """
+    Returns whether or not Tortoises' classifier thinks the given clip came from Tortoise.
+    :param clip: torch tensor containing audio waveform data (get it from load_audio)
+    :return: True if the clip was classified as coming from Tortoise and false if it was classified as real.
+    """
+    classifier = AudioMiniEncoderWithClassifierHead(2, spec_dim=1, embedding_dim=512, depth=5, downsample_factor=4,
+                                                    resnet_blocks=2, attn_blocks=4, num_attn_heads=4, base_channels=32,
+                                                    dropout=0, kernel_size=5, distribute_zero_label=False)
+    classifier.load_state_dict(torch.load(get_model_path('classifier.pth'), map_location=torch.device('cpu')))
+    clip = clip.cpu().unsqueeze(0)
+    results = F.softmax(classifier(clip), dim=-1)
+    return results[0][0]
+
+
+def pick_best_batch_size_for_gpu():
+    """
+    Tries to pick a batch size that will fit in your GPU. These sizes aren't guaranteed to work, but they should give
+    you a good shot.
+    """
+    if torch.cuda.is_available():
+        _, available = torch.cuda.mem_get_info()
+        availableGb = available / (1024 ** 3)
+        if availableGb > 14:
+            return 16
+        elif availableGb > 10:
+            return 8
+        elif availableGb > 7:
+            return 4
+    if torch.backends.mps.is_available():
+        import psutil
+        available = psutil.virtual_memory().total
+        availableGb = available / (1024 ** 3)
+        if availableGb > 14:
+            return 16
+        elif availableGb > 10:
+            return 8
+        elif availableGb > 7:
+            return 4
+    return 1
+
+class TextToSpeech:
+    """
+    Main entry point into Tortoise.
+    """
+
+    def __init__(self, autoregressive_batch_size=None, models_dir=MODELS_DIR, 
+                 enable_redaction=True, kv_cache=False, use_deepspeed=False, half=False, device=None,
+                 tokenizer_vocab_file=None, tokenizer_basic=False):
+
+        """
+        Constructor
+        :param autoregressive_batch_size: Specifies how many samples to generate per batch. Lower this if you are seeing
+                                          GPU OOM errors. Larger numbers generates slightly faster.
+        :param models_dir: Where model weights are stored. This should only be specified if you are providing your own
+                           models, otherwise use the defaults.
+        :param enable_redaction: When true, text enclosed in brackets are automatically redacted from the spoken output
+                                 (but are still rendered by the model). This can be used for prompt engineering.
+                                 Default is true.
+        :param device: Device to use when running the model. If omitted, the device will be automatically chosen.
+        """
+        self.models_dir = models_dir
+        self.autoregressive_batch_size = pick_best_batch_size_for_gpu() if autoregressive_batch_size is None else autoregressive_batch_size
+        self.enable_redaction = enable_redaction
+        if device is None:
+            self.device = torch.device('cuda' if torch.cuda.is_available() else'cpu')
+        else:
+            self.device = torch.device(device)
+            
+        if torch.backends.mps.is_available():
+            self.device = torch.device('mps')
+        if self.enable_redaction:
+            self.aligner = Wav2VecAlignment()
+
+        self.tokenizer = VoiceBpeTokenizer(
+            vocab_file=tokenizer_vocab_file,
+            use_basic_cleaners=tokenizer_basic,
+        )
+        self.half = half
+        if os.path.exists(f'{models_dir}/autoregressive.ptt'):
+            # Assume this is a traced directory.
+            self.autoregressive = torch.jit.load(f'{models_dir}/autoregressive.ptt')
+            self.diffusion = torch.jit.load(f'{models_dir}/diffusion_decoder.ptt')
+        else:
+            self.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
+                                          model_dim=1024,
+                                          heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
+                                          train_solo_embeddings=False).cpu().eval()
+            self.autoregressive.load_state_dict(torch.load(get_model_path('autoregressive.pth', models_dir)), strict=False)
+            self.autoregressive.post_init_gpt2_config(use_deepspeed=use_deepspeed, kv_cache=kv_cache, half=self.half)
+            
+            self.diffusion = DiffusionTts(model_channels=1024, num_layers=10, in_channels=100, out_channels=200,
+                                          in_latent_channels=1024, in_tokens=8193, dropout=0, use_fp16=False, num_heads=16,
+                                          layer_drop=0, unconditioned_percentage=0).cpu().eval()
+            self.diffusion.load_state_dict(torch.load(get_model_path('diffusion_decoder.pth', models_dir)))
+
+        self.clvp = CLVP(dim_text=768, dim_speech=768, dim_latent=768, num_text_tokens=256, text_enc_depth=20,
+                         text_seq_len=350, text_heads=12,
+                         num_speech_tokens=8192, speech_enc_depth=20, speech_heads=12, speech_seq_len=430,
+                         use_xformers=True).cpu().eval()
+        self.clvp.load_state_dict(torch.load(get_model_path('clvp2.pth', models_dir)))
+        self.cvvp = None # CVVP model is only loaded if used.
+
+        self.vocoder = UnivNetGenerator().cpu()
+        self.vocoder.load_state_dict(torch.load(get_model_path('vocoder.pth', models_dir), map_location=torch.device('cpu'))['model_g'])
+        self.vocoder.eval(inference=True)
+        
+        self.stft = None # TacotronSTFT is only loaded if used.
+
+        # Random latent generators (RLGs) are loaded lazily.
+        self.rlg_auto = None
+        self.rlg_diffusion = None
+    @contextmanager
+    def temporary_cuda(self, model):
+        m = model.to(self.device)
+        yield m
+        m = model.cpu()
+
+    
+    def load_cvvp(self):
+        """Load CVVP model."""
+        self.cvvp = CVVP(model_dim=512, transformer_heads=8, dropout=0, mel_codes=8192, conditioning_enc_depth=8, cond_mask_percentage=0,
+                         speech_enc_depth=8, speech_mask_percentage=0, latent_multiplier=1).cpu().eval()
+        self.cvvp.load_state_dict(torch.load(get_model_path('cvvp.pth', self.models_dir)))
+
+    def get_conditioning_latents(self, voice_samples, return_mels=False):
+        """
+        Transforms one or more voice_samples into a tuple (autoregressive_conditioning_latent, diffusion_conditioning_latent).
+        These are expressive learned latents that encode aspects of the provided clips like voice, intonation, and acoustic
+        properties.
+        :param voice_samples: List of 2 or more ~10 second reference clips, which should be torch tensors containing 22.05kHz waveform data.
+        """
+        with torch.no_grad():
+            voice_samples = [v.to(self.device) for v in voice_samples]
+
+            auto_conds = []
+            if not isinstance(voice_samples, list):
+                voice_samples = [voice_samples]
+            for vs in voice_samples:
+                auto_conds.append(format_conditioning(vs, device=self.device))
+            auto_conds = torch.stack(auto_conds, dim=1)
+            self.autoregressive = self.autoregressive.to(self.device)
+            auto_latent = self.autoregressive.get_conditioning(auto_conds)
+            self.autoregressive = self.autoregressive.cpu()
+
+            if self.stft is None:
+                # Initialize STFT
+                self.stft = TacotronSTFT(1024, 256, 1024, 100, 24000, 0, 12000).to(self.device)
+
+            diffusion_conds = []
+            for sample in voice_samples:
+                # The diffuser operates at a sample rate of 24000 (except for the latent inputs)
+                sample = torchaudio.functional.resample(sample, 22050, 24000)
+                sample = pad_or_truncate(sample, 102400)
+                cond_mel = wav_to_univnet_mel(sample.to(self.device), do_normalization=False,
+                                              device=self.device, stft=self.stft)
+                diffusion_conds.append(cond_mel)
+            diffusion_conds = torch.stack(diffusion_conds, dim=1)
+
+            self.diffusion = self.diffusion.to(self.device)
+            diffusion_latent = self.diffusion.get_conditioning(diffusion_conds)
+            self.diffusion = self.diffusion.cpu()
+
+        if return_mels:
+            return auto_latent, diffusion_latent, auto_conds, diffusion_conds
+        else:
+            return auto_latent, diffusion_latent
+
+    def get_random_conditioning_latents(self):
+        # Lazy-load the RLG models.
+        if self.rlg_auto is None:
+            self.rlg_auto = RandomLatentConverter(1024).eval()
+            self.rlg_auto.load_state_dict(torch.load(get_model_path('rlg_auto.pth', self.models_dir), map_location=torch.device('cpu')))
+            self.rlg_diffusion = RandomLatentConverter(2048).eval()
+            self.rlg_diffusion.load_state_dict(torch.load(get_model_path('rlg_diffuser.pth', self.models_dir), map_location=torch.device('cpu')))
+        with torch.no_grad():
+            return self.rlg_auto(torch.tensor([0.0])), self.rlg_diffusion(torch.tensor([0.0]))
+
+    def tts_with_preset(self, text, preset='fast', **kwargs):
+        """
+        Calls TTS with one of a set of preset generation parameters. Options:
+            'ultra_fast': Produces speech at a speed which belies the name of this repo. (Not really, but it's definitely fastest).
+            'fast': Decent quality speech at a decent inference rate. A good choice for mass inference.
+            'standard': Very good quality. This is generally about as good as you are going to get.
+            'high_quality': Use if you want the absolute best. This is not really worth the compute, though.
+        """
+        # Use generally found best tuning knobs for generation.
+        settings = {'temperature': .8, 'length_penalty': 1.0, 'repetition_penalty': 2.0,
+                    'top_p': .8,
+                    'cond_free_k': 2.0, 'diffusion_temperature': 1.0}
+        # Presets are defined here.
+        presets = {
+            'ultra_fast': {'num_autoregressive_samples': 16, 'diffusion_iterations': 30, 'cond_free': False},
+            'fast': {'num_autoregressive_samples': 96, 'diffusion_iterations': 80},
+            'standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 200},
+            'high_quality': {'num_autoregressive_samples': 256, 'diffusion_iterations': 400},
+        }
+        settings.update(presets[preset])
+        settings.update(kwargs) # allow overriding of preset settings with kwargs
+        return self.tts(text, **settings)
+
+    def tts(self, text, voice_samples=None, conditioning_latents=None, k=1, verbose=True, use_deterministic_seed=None,
+            return_deterministic_state=False,
+            # autoregressive generation parameters follow
+            num_autoregressive_samples=512, temperature=.8, length_penalty=1, repetition_penalty=2.0, top_p=.8, max_mel_tokens=500,
+            # CVVP parameters follow
+            cvvp_amount=.0,
+            # diffusion generation parameters follow
+            diffusion_iterations=100, cond_free=True, cond_free_k=2, diffusion_temperature=1.0,
+            **hf_generate_kwargs):
+        """
+        Produces an audio clip of the given text being spoken with the given reference voice.
+        :param text: Text to be spoken.
+        :param voice_samples: List of 2 or more ~10 second reference clips which should be torch tensors containing 22.05kHz waveform data.
+        :param conditioning_latents: A tuple of (autoregressive_conditioning_latent, diffusion_conditioning_latent), which
+                                     can be provided in lieu of voice_samples. This is ignored unless voice_samples=None.
+                                     Conditioning latents can be retrieved via get_conditioning_latents().
+        :param k: The number of returned clips. The most likely (as determined by Tortoises' CLVP model) clips are returned.
+        :param verbose: Whether or not to print log messages indicating the progress of creating a clip. Default=true.
+        ~~AUTOREGRESSIVE KNOBS~~
+        :param num_autoregressive_samples: Number of samples taken from the autoregressive model, all of which are filtered using CLVP.
+               As Tortoise is a probabilistic model, more samples means a higher probability of creating something "great".
+        :param temperature: The softmax temperature of the autoregressive model.
+        :param length_penalty: A length penalty applied to the autoregressive decoder. Higher settings causes the model to produce more terse outputs.
+        :param repetition_penalty: A penalty that prevents the autoregressive decoder from repeating itself during decoding. Can be used to reduce the incidence
+                                   of long silences or "uhhhhhhs", etc.
+        :param top_p: P value used in nucleus sampling. (0,1]. Lower values mean the decoder produces more "likely" (aka boring) outputs.
+        :param max_mel_tokens: Restricts the output length. (0,600] integer. Each unit is 1/20 of a second.
+        :param typical_sampling: Turns typical sampling on or off. This sampling mode is discussed in this paper: https://arxiv.org/abs/2202.00666
+                                 I was interested in the premise, but the results were not as good as I was hoping. This is off by default, but
+                                 could use some tuning.
+        :param typical_mass: The typical_mass parameter from the typical_sampling algorithm.
+        ~~CLVP-CVVP KNOBS~~
+        :param cvvp_amount: Controls the influence of the CVVP model in selecting the best output from the autoregressive model.
+                            [0,1]. Values closer to 1 mean the CVVP model is more important, 0 disables the CVVP model.
+        ~~DIFFUSION KNOBS~~
+        :param diffusion_iterations: Number of diffusion steps to perform. [0,4000]. More steps means the network has more chances to iteratively refine
+                                     the output, which should theoretically mean a higher quality output. Generally a value above 250 is not noticeably better,
+                                     however.
+        :param cond_free: Whether or not to perform conditioning-free diffusion. Conditioning-free diffusion performs two forward passes for
+                          each diffusion step: one with the outputs of the autoregressive model and one with no conditioning priors. The output
+                          of the two is blended according to the cond_free_k value below. Conditioning-free diffusion is the real deal, and
+                          dramatically improves realism.
+        :param cond_free_k: Knob that determines how to balance the conditioning free signal with the conditioning-present signal. [0,inf].
+                            As cond_free_k increases, the output becomes dominated by the conditioning-free signal.
+                            Formula is: output=cond_present_output*(cond_free_k+1)-cond_absenct_output*cond_free_k
+        :param diffusion_temperature: Controls the variance of the noise fed into the diffusion model. [0,1]. Values at 0
+                                      are the "mean" prediction of the diffusion network and will sound bland and smeared.
+        ~~OTHER STUFF~~
+        :param hf_generate_kwargs: The huggingface Transformers generate API is used for the autoregressive transformer.
+                                   Extra keyword args fed to this function get forwarded directly to that API. Documentation
+                                   here: https://huggingface.co/docs/transformers/internal/generation_utils
+        :return: Generated audio clip(s) as a torch tensor. Shape 1,S if k=1 else, (k,1,S) where S is the sample length.
+                 Sample rate is 24kHz.
+        """
+        deterministic_seed = self.deterministic_state(seed=use_deterministic_seed)
+
+        text_tokens = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).to(self.device)
+        text_tokens = F.pad(text_tokens, (0, 1))  # This may not be necessary.
+        assert text_tokens.shape[-1] < 400, 'Too much text provided. Break the text up into separate segments and re-try inference.'
+        auto_conds = None
+        if voice_samples is not None:
+            auto_conditioning, diffusion_conditioning, auto_conds, _ = self.get_conditioning_latents(voice_samples, return_mels=True)
+        elif conditioning_latents is not None:
+            auto_conditioning, diffusion_conditioning = conditioning_latents
+        else:
+            auto_conditioning, diffusion_conditioning = self.get_random_conditioning_latents()
+        auto_conditioning = auto_conditioning.to(self.device)
+        diffusion_conditioning = diffusion_conditioning.to(self.device)
+
+        diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=diffusion_iterations, cond_free=cond_free, cond_free_k=cond_free_k)
+
+        with torch.no_grad():
+            samples = []
+            num_batches = num_autoregressive_samples // self.autoregressive_batch_size
+            stop_mel_token = self.autoregressive.stop_mel_token
+            calm_token = 83  # This is the token for coding silence, which is fixed in place with "fix_autoregressive_output"
+            if verbose:
+                print("Generating autoregressive samples..")
+            if not torch.backends.mps.is_available():
+                with self.temporary_cuda(self.autoregressive
+                ) as autoregressive, torch.autocast(device_type="cuda", dtype=torch.float16, enabled=self.half):
+                    for b in tqdm(range(num_batches), disable=not verbose):
+                        codes = autoregressive.inference_speech(auto_conditioning, text_tokens,
+                                                                    do_sample=True,
+                                                                    top_p=top_p,
+                                                                    temperature=temperature,
+                                                                    num_return_sequences=self.autoregressive_batch_size,
+                                                                    length_penalty=length_penalty,
+                                                                    repetition_penalty=repetition_penalty,
+                                                                    max_generate_length=max_mel_tokens,
+                                                                    **hf_generate_kwargs)
+                        padding_needed = max_mel_tokens - codes.shape[1]
+                        codes = F.pad(codes, (0, padding_needed), value=stop_mel_token)
+                        samples.append(codes)
+            else:
+                with self.temporary_cuda(self.autoregressive) as autoregressive:
+                    for b in tqdm(range(num_batches), disable=not verbose):
+                        codes = autoregressive.inference_speech(auto_conditioning, text_tokens,
+                                                                    do_sample=True,
+                                                                    top_p=top_p,
+                                                                    temperature=temperature,
+                                                                    num_return_sequences=self.autoregressive_batch_size,
+                                                                    length_penalty=length_penalty,
+                                                                    repetition_penalty=repetition_penalty,
+                                                                    max_generate_length=max_mel_tokens,
+                                                                    **hf_generate_kwargs)
+                        padding_needed = max_mel_tokens - codes.shape[1]
+                        codes = F.pad(codes, (0, padding_needed), value=stop_mel_token)
+                        samples.append(codes)
+
+            clip_results = []
+            
+            if not torch.backends.mps.is_available():
+                with self.temporary_cuda(self.clvp) as clvp, torch.autocast(
+                    device_type="cuda" if not torch.backends.mps.is_available() else 'mps', dtype=torch.float16, enabled=self.half
+                ):
+                    if cvvp_amount > 0:
+                        if self.cvvp is None:
+                            self.load_cvvp()
+                        self.cvvp = self.cvvp.to(self.device)
+                    if verbose:
+                        if self.cvvp is None:
+                            print("Computing best candidates using CLVP")
+                        else:
+                            print(f"Computing best candidates using CLVP {((1-cvvp_amount) * 100):2.0f}% and CVVP {(cvvp_amount * 100):2.0f}%")
+                    for batch in tqdm(samples, disable=not verbose):
+                        for i in range(batch.shape[0]):
+                            batch[i] = fix_autoregressive_output(batch[i], stop_mel_token)
+                        if cvvp_amount != 1:
+                            clvp_out = clvp(text_tokens.repeat(batch.shape[0], 1), batch, return_loss=False)
+                        if auto_conds is not None and cvvp_amount > 0:
+                            cvvp_accumulator = 0
+                            for cl in range(auto_conds.shape[1]):
+                                cvvp_accumulator = cvvp_accumulator + self.cvvp(auto_conds[:, cl].repeat(batch.shape[0], 1, 1), batch, return_loss=False)
+                            cvvp = cvvp_accumulator / auto_conds.shape[1]
+                            if cvvp_amount == 1:
+                                clip_results.append(cvvp)
+                            else:
+                                clip_results.append(cvvp * cvvp_amount + clvp_out * (1-cvvp_amount))
+                        else:
+                            clip_results.append(clvp_out)
+                    clip_results = torch.cat(clip_results, dim=0)
+                    samples = torch.cat(samples, dim=0)
+                    best_results = samples[torch.topk(clip_results, k=k).indices]
+            else:
+                with self.temporary_cuda(self.clvp) as clvp:
+                    if cvvp_amount > 0:
+                        if self.cvvp is None:
+                            self.load_cvvp()
+                        self.cvvp = self.cvvp.to(self.device)
+                    if verbose:
+                        if self.cvvp is None:
+                            print("Computing best candidates using CLVP")
+                        else:
+                            print(f"Computing best candidates using CLVP {((1-cvvp_amount) * 100):2.0f}% and CVVP {(cvvp_amount * 100):2.0f}%")
+                    for batch in tqdm(samples, disable=not verbose):
+                        for i in range(batch.shape[0]):
+                            batch[i] = fix_autoregressive_output(batch[i], stop_mel_token)
+                        if cvvp_amount != 1:
+                            clvp_out = clvp(text_tokens.repeat(batch.shape[0], 1), batch, return_loss=False)
+                        if auto_conds is not None and cvvp_amount > 0:
+                            cvvp_accumulator = 0
+                            for cl in range(auto_conds.shape[1]):
+                                cvvp_accumulator = cvvp_accumulator + self.cvvp(auto_conds[:, cl].repeat(batch.shape[0], 1, 1), batch, return_loss=False)
+                            cvvp = cvvp_accumulator / auto_conds.shape[1]
+                            if cvvp_amount == 1:
+                                clip_results.append(cvvp)
+                            else:
+                                clip_results.append(cvvp * cvvp_amount + clvp_out * (1-cvvp_amount))
+                        else:
+                            clip_results.append(clvp_out)
+                    clip_results = torch.cat(clip_results, dim=0)
+                    samples = torch.cat(samples, dim=0)
+                    best_results = samples[torch.topk(clip_results, k=k).indices]
+            if self.cvvp is not None:
+                self.cvvp = self.cvvp.cpu()
+            del samples
+
+            # The diffusion model actually wants the last hidden layer from the autoregressive model as conditioning
+            # inputs. Re-produce those for the top results. This could be made more efficient by storing all of these
+            # results, but will increase memory usage.
+            if not torch.backends.mps.is_available():
+                with self.temporary_cuda(
+                    self.autoregressive
+                ) as autoregressive, torch.autocast(
+                    device_type="cuda" if not torch.backends.mps.is_available() else 'mps', dtype=torch.float16, enabled=self.half
+                ):
+                    best_latents = autoregressive(auto_conditioning.repeat(k, 1), text_tokens.repeat(k, 1),
+                                                    torch.tensor([text_tokens.shape[-1]], device=text_tokens.device), best_results,
+                                                    torch.tensor([best_results.shape[-1]*self.autoregressive.mel_length_compression], device=text_tokens.device),
+                                                    return_latent=True, clip_inputs=False)
+                    del auto_conditioning
+            else:
+                with self.temporary_cuda(
+                    self.autoregressive
+                ) as autoregressive:
+                    best_latents = autoregressive(auto_conditioning.repeat(k, 1), text_tokens.repeat(k, 1),
+                                                    torch.tensor([text_tokens.shape[-1]], device=text_tokens.device), best_results,
+                                                    torch.tensor([best_results.shape[-1]*self.autoregressive.mel_length_compression], device=text_tokens.device),
+                                                    return_latent=True, clip_inputs=False)
+                    del auto_conditioning
+
+            if verbose:
+                print("Transforming autoregressive outputs into audio..")
+            wav_candidates = []
+            if not torch.backends.mps.is_available():
+                with self.temporary_cuda(self.diffusion) as diffusion, self.temporary_cuda(
+                    self.vocoder
+                ) as vocoder:
+                    for b in range(best_results.shape[0]):
+                        codes = best_results[b].unsqueeze(0)
+                        latents = best_latents[b].unsqueeze(0)
+
+                        # Find the first occurrence of the "calm" token and trim the codes to that.
+                        ctokens = 0
+                        for k in range(codes.shape[-1]):
+                            if codes[0, k] == calm_token:
+                                ctokens += 1
+                            else:
+                                ctokens = 0
+                            if ctokens > 8:  # 8 tokens gives the diffusion model some "breathing room" to terminate speech.
+                                latents = latents[:, :k]
+                                break
+                        mel = do_spectrogram_diffusion(diffusion, diffuser, latents, diffusion_conditioning, temperature=diffusion_temperature, 
+                                                    verbose=verbose)
+                        wav = vocoder.inference(mel)
+                        wav_candidates.append(wav.cpu())
+            else:
+                diffusion, vocoder = self.diffusion, self.vocoder
+                diffusion_conditioning = diffusion_conditioning.cpu()
+                for b in range(best_results.shape[0]):
+                    codes = best_results[b].unsqueeze(0).cpu()
+                    latents = best_latents[b].unsqueeze(0).cpu()
+
+                    # Find the first occurrence of the "calm" token and trim the codes to that.
+                    ctokens = 0
+                    for k in range(codes.shape[-1]):
+                        if codes[0, k] == calm_token:
+                            ctokens += 1
+                        else:
+                            ctokens = 0
+                        if ctokens > 8:  # 8 tokens gives the diffusion model some "breathing room" to terminate speech.
+                            latents = latents[:, :k]
+                            break
+                    mel = do_spectrogram_diffusion(diffusion, diffuser, latents, diffusion_conditioning, temperature=diffusion_temperature, 
+                                                verbose=verbose)
+                    wav = vocoder.inference(mel)
+                    wav_candidates.append(wav.cpu())
+
+            def potentially_redact(clip, text):
+                if self.enable_redaction:
+                    return self.aligner.redact(clip.squeeze(1), text).unsqueeze(1)
+                return clip
+            wav_candidates = [potentially_redact(wav_candidate, text) for wav_candidate in wav_candidates]
+
+            if len(wav_candidates) > 1:
+                res = wav_candidates
+            else:
+                res = wav_candidates[0]
+
+            if return_deterministic_state:
+                return res, (deterministic_seed, text, voice_samples, conditioning_latents)
+            else:
+                return res
+    def deterministic_state(self, seed=None):
+        """
+        Sets the random seeds that tortoise uses to the current time() and returns that seed so results can be
+        reproduced.
+        """
+        seed = int(time()) if seed is None else seed
+        torch.manual_seed(seed)
+        random.seed(seed)
+        # Can't currently set this because of CUBLAS. TODO: potentially enable it if necessary.
+        # torch.use_deterministic_algorithms(True)
+
+        return seed
diff --git a/tortoise/tortoise/api_fast.py b/tortoise/tortoise/api_fast.py
new file mode 100644
index 0000000..8cfe4b8
--- /dev/null
+++ b/tortoise/tortoise/api_fast.py
@@ -0,0 +1,515 @@
+import os
+import random
+import uuid
+from time import time
+from urllib import request
+
+import torch
+import torch.nn.functional as F
+import progressbar
+import torchaudio
+import numpy as np
+from tortoise.models.classifier import AudioMiniEncoderWithClassifierHead
+from tortoise.models.diffusion_decoder import DiffusionTts
+from tortoise.models.autoregressive import UnifiedVoice
+from tqdm import tqdm
+from tortoise.models.arch_util import TorchMelSpectrogram
+from tortoise.models.clvp import CLVP
+from tortoise.models.cvvp import CVVP
+from tortoise.models.hifigan_decoder import HifiganGenerator
+from tortoise.models.random_latent_generator import RandomLatentConverter
+from tortoise.models.vocoder import UnivNetGenerator
+from tortoise.utils.audio import wav_to_univnet_mel, denormalize_tacotron_mel
+from tortoise.utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule
+from tortoise.utils.tokenizer import VoiceBpeTokenizer
+from tortoise.utils.wav2vec_alignment import Wav2VecAlignment
+from contextlib import contextmanager
+from tortoise.models.stream_generator import init_stream_support
+from huggingface_hub import hf_hub_download
+pbar = None
+init_stream_support()
+DEFAULT_MODELS_DIR = os.path.join(os.path.expanduser('~'), '.cache', 'tortoise', 'models')
+MODELS_DIR = os.environ.get('TORTOISE_MODELS_DIR', DEFAULT_MODELS_DIR)
+
+MODELS = {
+    'autoregressive.pth': 'https://huggingface.co/doammii/tortoise-tts-v2/resolve/main/3420_gpt.pth',
+    'classifier.pth': 'https://huggingface.co/doammii/tortoise-tts-v2/resolve/main/3420_gpt.pth',
+    'rlg_auto.pth': 'https://huggingface.co/doammii/tortoise-tts-v2/resolve/main/3420_gpt.pthh',
+    'hifidecoder.pth': 'https://huggingface.co/doammii/tortoise-tts-v2/resolve/main/3420_gpt.pth',
+}
+
+def get_model_path(model_name, models_dir=MODELS_DIR):
+    """
+    Get path to given model, download it if it doesn't exist.
+    """
+    if model_name not in MODELS:
+        raise ValueError(f'Model {model_name} not found in available models.')
+    model_path = hf_hub_download(repo_id="Manmay/tortoise-tts", filename=model_name, cache_dir=models_dir)
+    return model_path
+
+
+def pad_or_truncate(t, length):
+    """
+    Utility function for forcing <t> to have the specified sequence length, whether by clipping it or padding it with 0s.
+    """
+    if t.shape[-1] == length:
+        return t
+    elif t.shape[-1] < length:
+        return F.pad(t, (0, length-t.shape[-1]))
+    else:
+        return t[..., :length]
+
+
+def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusion_steps=200, cond_free=True, cond_free_k=1):
+    """
+    Helper function to load a GaussianDiffusion instance configured for use as a vocoder.
+    """
+    return SpacedDiffusion(use_timesteps=space_timesteps(trained_diffusion_steps, [desired_diffusion_steps]), model_mean_type='epsilon',
+                           model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', trained_diffusion_steps),
+                           conditioning_free=cond_free, conditioning_free_k=cond_free_k)
+
+
+def format_conditioning(clip, cond_length=132300, device="cuda" if not torch.backends.mps.is_available() else 'mps'):
+    """
+    Converts the given conditioning signal to a MEL spectrogram and clips it as expected by the models.
+    """
+    gap = clip.shape[-1] - cond_length
+    if gap < 0:
+        clip = F.pad(clip, pad=(0, abs(gap)))
+    elif gap > 0:
+        rand_start = random.randint(0, gap)
+        clip = clip[:, rand_start:rand_start + cond_length]
+    mel_clip = TorchMelSpectrogram()(clip.unsqueeze(0)).squeeze(0)
+    return mel_clip.unsqueeze(0).to(device)
+
+
+def fix_autoregressive_output(codes, stop_token, complain=True):
+    """
+    This function performs some padding on coded audio that fixes a mismatch issue between what the diffusion model was
+    trained on and what the autoregressive code generator creates (which has no padding or end).
+    This is highly specific to the DVAE being used, so this particular coding will not necessarily work if used with
+    a different DVAE. This can be inferred by feeding a audio clip padded with lots of zeros on the end through the DVAE
+    and copying out the last few codes.
+
+    Failing to do this padding will produce speech with a harsh end that sounds like "BLAH" or similar.
+    """
+    # Strip off the autoregressive stop token and add padding.
+    stop_token_indices = (codes == stop_token).nonzero()
+    if len(stop_token_indices) == 0:
+        if complain:
+            print("No stop tokens found in one of the generated voice clips. This typically means the spoken audio is "
+                  "too long. In some cases, the output will still be good, though. Listen to it and if it is missing words, "
+                  "try breaking up your input text.")
+        return codes
+    else:
+        codes[stop_token_indices] = 83
+    stm = stop_token_indices.min().item()
+    codes[stm:] = 83
+    if stm - 3 < codes.shape[0]:
+        codes[-3] = 45
+        codes[-2] = 45
+        codes[-1] = 248
+
+    return codes
+
+
+def do_spectrogram_diffusion(diffusion_model, diffuser, latents, conditioning_latents, temperature=1, verbose=True):
+    """
+    Uses the specified diffusion model to convert discrete codes into a spectrogram.
+    """
+    with torch.no_grad():
+        output_seq_len = latents.shape[1] * 4 * 24000 // 22050  # This diffusion model converts from 22kHz spectrogram codes to a 24kHz spectrogram signal.
+        output_shape = (latents.shape[0], 100, output_seq_len)
+        precomputed_embeddings = diffusion_model.timestep_independent(latents, conditioning_latents, output_seq_len, False)
+
+        noise = torch.randn(output_shape, device=latents.device) * temperature
+        mel = diffuser.p_sample_loop(diffusion_model, output_shape, noise=noise,
+                                      model_kwargs={'precomputed_aligned_embeddings': precomputed_embeddings},
+                                     progress=verbose)
+        return denormalize_tacotron_mel(mel)[:,:,:output_seq_len]
+
+
+def classify_audio_clip(clip):
+    """
+    Returns whether or not Tortoises' classifier thinks the given clip came from Tortoise.
+    :param clip: torch tensor containing audio waveform data (get it from load_audio)
+    :return: True if the clip was classified as coming from Tortoise and false if it was classified as real.
+    """
+    classifier = AudioMiniEncoderWithClassifierHead(2, spec_dim=1, embedding_dim=512, depth=5, downsample_factor=4,
+                                                    resnet_blocks=2, attn_blocks=4, num_attn_heads=4, base_channels=32,
+                                                    dropout=0, kernel_size=5, distribute_zero_label=False)
+    classifier.load_state_dict(torch.load(get_model_path('classifier.pth'), map_location=torch.device('cpu')))
+    clip = clip.cpu().unsqueeze(0)
+    results = F.softmax(classifier(clip), dim=-1)
+    return results[0][0]
+
+
+def pick_best_batch_size_for_gpu():
+    """
+    Tries to pick a batch size that will fit in your GPU. These sizes aren't guaranteed to work, but they should give
+    you a good shot.
+    """
+    if torch.cuda.is_available():
+        _, available = torch.cuda.mem_get_info()
+        availableGb = available / (1024 ** 3)
+        if availableGb > 14:
+            return 16
+        elif availableGb > 10:
+            return 8
+        elif availableGb > 7:
+            return 4
+    if torch.backends.mps.is_available():
+        import psutil
+        available = psutil.virtual_memory().total
+        availableGb = available / (1024 ** 3)
+        if availableGb > 14:
+            return 16
+        elif availableGb > 10:
+            return 8
+        elif availableGb > 7:
+            return 4
+    return 1
+
+class TextToSpeech:
+    """
+    Main entry point into Tortoise.
+    """
+
+    def __init__(self, autoregressive_batch_size=None, models_dir=MODELS_DIR, 
+                 enable_redaction=True, kv_cache=False, use_deepspeed=False, half=False, device=None,
+                 tokenizer_vocab_file=None, tokenizer_basic=False):
+
+        """
+        Constructor
+        :param autoregressive_batch_size: Specifies how many samples to generate per batch. Lower this if you are seeing
+                                          GPU OOM errors. Larger numbers generates slightly faster.
+        :param models_dir: Where model weights are stored. This should only be specified if you are providing your own
+                           models, otherwise use the defaults.
+        :param enable_redaction: When true, text enclosed in brackets are automatically redacted from the spoken output
+                                 (but are still rendered by the model). This can be used for prompt engineering.
+                                 Default is true.
+        :param device: Device to use when running the model. If omitted, the device will be automatically chosen.
+        """
+        self.models_dir = models_dir
+        self.autoregressive_batch_size = pick_best_batch_size_for_gpu() if autoregressive_batch_size is None else autoregressive_batch_size
+        self.enable_redaction = enable_redaction
+        if device is None:
+            self.device = torch.device('cuda' if torch.cuda.is_available() else'cpu')
+        else:
+            self.device = torch.device(device)
+            
+        if torch.backends.mps.is_available():
+            self.device = torch.device('mps')
+        if self.enable_redaction:
+            self.aligner = Wav2VecAlignment()
+
+        self.tokenizer = VoiceBpeTokenizer(
+            vocab_file=tokenizer_vocab_file,
+            use_basic_cleaners=tokenizer_basic,
+        )
+        self.half = half
+        if os.path.exists(f'{models_dir}/autoregressive.ptt'):
+            # Assume this is a traced directory.
+            self.autoregressive = torch.jit.load(f'{models_dir}/autoregressive.ptt')
+        else:
+            self.autoregressive = UnifiedVoice(max_mel_tokens=604, max_text_tokens=402, max_conditioning_inputs=2, layers=30,
+                                          model_dim=1024,
+                                          heads=16, number_text_tokens=255, start_text_token=255, checkpointing=False,
+                                          train_solo_embeddings=False).to(self.device).eval()
+            self.autoregressive.load_state_dict(torch.load(get_model_path('autoregressive.pth', models_dir)), strict=False)
+            self.autoregressive.post_init_gpt2_config(use_deepspeed=use_deepspeed, kv_cache=kv_cache, half=self.half)
+
+        self.hifi_decoder = HifiganGenerator(in_channels=1024, out_channels = 1, resblock_type = "1",
+        resblock_dilation_sizes = [[1, 3, 5], [1, 3, 5], [1, 3, 5]], resblock_kernel_sizes = [3, 7, 11],
+        upsample_kernel_sizes = [16, 16, 4, 4], upsample_initial_channel = 512, upsample_factors = [8, 8, 2, 2],
+        cond_channels=1024).to(self.device).eval()
+        hifi_model = torch.load(get_model_path('hifidecoder.pth'))
+        self.hifi_decoder.load_state_dict(hifi_model, strict=False)
+        # Random latent generators (RLGs) are loaded lazily.
+        self.rlg_auto = None
+    def get_conditioning_latents(self, voice_samples, return_mels=False):
+        """
+        Transforms one or more voice_samples into a tuple (autoregressive_conditioning_latent, diffusion_conditioning_latent).
+        These are expressive learned latents that encode aspects of the provided clips like voice, intonation, and acoustic
+        properties.
+        :param voice_samples: List of 2 or more ~10 second reference clips, which should be torch tensors containing 22.05kHz waveform data.
+        """
+        with torch.no_grad():
+            voice_samples = [v.to(self.device) for v in voice_samples]
+
+            auto_conds = []
+            if not isinstance(voice_samples, list):
+                voice_samples = [voice_samples]
+            for vs in voice_samples:
+                auto_conds.append(format_conditioning(vs, device=self.device))
+            auto_conds = torch.stack(auto_conds, dim=1)
+            auto_latent = self.autoregressive.get_conditioning(auto_conds)
+
+        if return_mels:
+            return auto_latent
+        else:
+            return auto_latent
+
+    def get_random_conditioning_latents(self):
+        # Lazy-load the RLG models.
+        if self.rlg_auto is None:
+            self.rlg_auto = RandomLatentConverter(1024).eval()
+            self.rlg_auto.load_state_dict(torch.load(get_model_path('rlg_auto.pth', self.models_dir), map_location=torch.device('cpu')))
+        with torch.no_grad():
+            return self.rlg_auto(torch.tensor([0.0]))
+
+    def tts_with_preset(self, text, preset='fast', **kwargs):
+        """
+        Calls TTS with one of a set of preset generation parameters. Options:
+            'ultra_fast': Produces speech at a speed which belies the name of this repo. (Not really, but it's definitely fastest).
+            'fast': Decent quality speech at a decent inference rate. A good choice for mass inference.
+            'standard': Very good quality. This is generally about as good as you are going to get.
+            'high_quality': Use if you want the absolute best. This is not really worth the compute, though.
+        """
+        # Use generally found best tuning knobs for generation.
+        settings = {'temperature': .8, 'length_penalty': 1.0, 'repetition_penalty': 2.0,
+                    'top_p': .8,
+                    'cond_free_k': 2.0, 'diffusion_temperature': 1.0}
+        # Presets are defined here.
+        presets = {
+            'ultra_fast': {'num_autoregressive_samples': 1, 'diffusion_iterations': 10},
+            'fast': {'num_autoregressive_samples': 32, 'diffusion_iterations': 50},
+            'standard': {'num_autoregressive_samples': 256, 'diffusion_iterations': 200},
+            'high_quality': {'num_autoregressive_samples': 256, 'diffusion_iterations': 400},
+        }
+        settings.update(presets[preset])
+        settings.update(kwargs) # allow overriding of preset settings with kwargs
+        for audio_frame in self.tts(text, **settings):
+            yield audio_frame
+    # taken from here https://github.com/coqui-ai/TTS/blob/b4c552a112fd4c5f3477f439882eb43c2e2ce85f/TTS/tts/models/xtts.py#L600
+    def handle_chunks(self, wav_gen, wav_gen_prev, wav_overlap, overlap_len):
+        """Handle chunk formatting in streaming mode"""
+        wav_chunk = wav_gen[:-overlap_len]
+        if wav_gen_prev is not None:
+            wav_chunk = wav_gen[(wav_gen_prev.shape[0] - overlap_len) : -overlap_len]
+        if wav_overlap is not None:
+            # cross fade the overlap section
+            if overlap_len > len(wav_chunk):
+                # wav_chunk is smaller than overlap_len, pass on last wav_gen
+                if wav_gen_prev is not None:
+                    wav_chunk = wav_gen[(wav_gen_prev.shape[0] - overlap_len):]
+                else:
+                    # not expecting will hit here as problem happens on last chunk
+                    wav_chunk = wav_gen[-overlap_len:]
+                return wav_chunk, wav_gen, None
+            else:
+                crossfade_wav = wav_chunk[:overlap_len]
+                crossfade_wav = crossfade_wav * torch.linspace(0.0, 1.0, overlap_len).to(crossfade_wav.device)
+                wav_chunk[:overlap_len] = wav_overlap * torch.linspace(1.0, 0.0, overlap_len).to(wav_overlap.device)
+                wav_chunk[:overlap_len] += crossfade_wav
+
+        wav_overlap = wav_gen[-overlap_len:]
+        wav_gen_prev = wav_gen
+        return wav_chunk, wav_gen_prev, wav_overlap
+
+
+    def tts_stream(self, text, voice_samples=None, conditioning_latents=None, k=1, verbose=True, use_deterministic_seed=None,
+            return_deterministic_state=False, overlap_wav_len=1024, stream_chunk_size=40,
+            # autoregressive generation parameters follow
+            num_autoregressive_samples=512, temperature=.8, length_penalty=1, repetition_penalty=2.0, top_p=.8, max_mel_tokens=500,
+            # CVVP parameters follow
+            cvvp_amount=.0,
+            # diffusion generation parameters follow
+            diffusion_iterations=100, cond_free=True, cond_free_k=2, diffusion_temperature=1.0,
+            **hf_generate_kwargs):
+        """
+        Produces an audio clip of the given text being spoken with the given reference voice.
+        :param text: Text to be spoken.
+        :param voice_samples: List of 2 or more ~10 second reference clips which should be torch tensors containing 22.05kHz waveform data.
+        :param conditioning_latents: A tuple of (autoregressive_conditioning_latent, diffusion_conditioning_latent), which
+                                     can be provided in lieu of voice_samples. This is ignored unless voice_samples=None.
+                                     Conditioning latents can be retrieved via get_conditioning_latents().
+        :param k: The number of returned clips. The most likely (as determined by Tortoises' CLVP model) clips are returned.
+        :param verbose: Whether or not to print log messages indicating the progress of creating a clip. Default=true.
+        ~~AUTOREGRESSIVE KNOBS~~
+        :param num_autoregressive_samples: Number of samples taken from the autoregressive model, all of which are filtered using CLVP.
+               As Tortoise is a probabilistic model, more samples means a higher probability of creating something "great".
+        :param temperature: The softmax temperature of the autoregressive model.
+        :param length_penalty: A length penalty applied to the autoregressive decoder. Higher settings causes the model to produce more terse outputs.
+        :param repetition_penalty: A penalty that prevents the autoregressive decoder from repeating itself during decoding. Can be used to reduce the incidence
+                                   of long silences or "uhhhhhhs", etc.
+        :param top_p: P value used in nucleus sampling. (0,1]. Lower values mean the decoder produces more "likely" (aka boring) outputs.
+        :param max_mel_tokens: Restricts the output length. (0,600] integer. Each unit is 1/20 of a second.
+        ~~DIFFUSION KNOBS~~
+        :param diffusion_iterations: Number of diffusion steps to perform. [0,4000]. More steps means the network has more chances to iteratively refine
+                                     the output, which should theoretically mean a higher quality output. Generally a value above 250 is not noticeably better,
+                                     however.
+        :param cond_free: Whether or not to perform conditioning-free diffusion. Conditioning-free diffusion performs two forward passes for
+                          each diffusion step: one with the outputs of the autoregressive model and one with no conditioning priors. The output
+                          of the two is blended according to the cond_free_k value below. Conditioning-free diffusion is the real deal, and
+                          dramatically improves realism.
+        :param cond_free_k: Knob that determines how to balance the conditioning free signal with the conditioning-present signal. [0,inf].
+                            As cond_free_k increases, the output becomes dominated by the conditioning-free signal.
+                            Formula is: output=cond_present_output*(cond_free_k+1)-cond_absenct_output*cond_free_k
+        :param diffusion_temperature: Controls the variance of the noise fed into the diffusion model. [0,1]. Values at 0
+                                      are the "mean" prediction of the diffusion network and will sound bland and smeared.
+        ~~OTHER STUFF~~
+        :param hf_generate_kwargs: The huggingface Transformers generate API is used for the autoregressive transformer.
+                                   Extra keyword args fed to this function get forwarded directly to that API. Documentation
+                                   here: https://huggingface.co/docs/transformers/internal/generation_utils
+        :return: Generated audio clip(s) as a torch tensor. Shape 1,S if k=1 else, (k,1,S) where S is the sample length.
+                 Sample rate is 24kHz.
+        """
+        deterministic_seed = self.deterministic_state(seed=use_deterministic_seed)
+
+        text_tokens = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).to(self.device)
+        text_tokens = F.pad(text_tokens, (0, 1))  # This may not be necessary.
+        assert text_tokens.shape[-1] < 400, 'Too much text provided. Break the text up into separate segments and re-try inference.'
+        if voice_samples is not None:
+            auto_conditioning = self.get_conditioning_latents(voice_samples, return_mels=False)
+        else:
+            auto_conditioning  = self.get_random_conditioning_latents()
+        auto_conditioning = auto_conditioning.to(self.device)
+
+        with torch.no_grad():
+            calm_token = 83  # This is the token for coding silence, which is fixed in place with "fix_autoregressive_output"
+            if verbose:
+                print("Generating autoregressive samples..")
+            with torch.autocast(
+                    device_type="cuda" , dtype=torch.float16, enabled=self.half
+                ):
+                fake_inputs = self.autoregressive.compute_embeddings(
+                    auto_conditioning,
+                    text_tokens,
+                )
+                gpt_generator = self.autoregressive.get_generator(
+                    fake_inputs=fake_inputs,
+                    top_k=50,
+                    top_p=top_p,
+                    temperature=temperature,
+                    do_sample=True,
+                    num_beams=1,
+                    num_return_sequences=1,
+                    length_penalty=float(length_penalty),
+                    repetition_penalty=float(repetition_penalty),
+                    output_attentions=False,
+                    output_hidden_states=True,
+                    **hf_generate_kwargs,
+                )
+            all_latents = []
+            codes_ = []
+            wav_gen_prev = None
+            wav_overlap = None
+            is_end = False
+            first_buffer = 60
+            while not is_end:
+                try:
+                    with torch.autocast(
+                        device_type="cuda", dtype=torch.float16, enabled=self.half
+                    ):
+                        codes, latent = next(gpt_generator)
+                        all_latents += [latent]
+                        codes_ += [codes]
+                except StopIteration:
+                    is_end = True
+
+                if is_end or (stream_chunk_size > 0 and len(codes_) >= max(stream_chunk_size, first_buffer)):
+                    first_buffer = 0
+                    gpt_latents = torch.cat(all_latents, dim=0)[None, :]
+                    wav_gen = self.hifi_decoder.inference(gpt_latents.to(self.device), auto_conditioning)
+                    wav_gen = wav_gen.squeeze()
+                    wav_chunk, wav_gen_prev, wav_overlap = self.handle_chunks(
+                        wav_gen.squeeze(), wav_gen_prev, wav_overlap, overlap_wav_len
+                    )
+                    codes_ = []
+                    yield wav_chunk
+    def tts(self, text, voice_samples=None, k=1, verbose=True, use_deterministic_seed=None,
+            # autoregressive generation parameters follow
+            num_autoregressive_samples=512, temperature=.8, length_penalty=1, repetition_penalty=2.0, 
+            top_p=.8, max_mel_tokens=500,
+            # CVVP parameters follow
+            cvvp_amount=.0,
+            **hf_generate_kwargs):
+        """
+        Produces an audio clip of the given text being spoken with the given reference voice.
+        :param text: Text to be spoken.
+        :param voice_samples: List of 2 or more ~10 second reference clips which should be torch tensors containing 22.05kHz waveform data.
+        :param conditioning_latents: A tuple of (autoregressive_conditioning_latent, diffusion_conditioning_latent), which
+                                     can be provided in lieu of voice_samples. This is ignored unless voice_samples=None.
+                                     Conditioning latents can be retrieved via get_conditioning_latents().
+        :param k: The number of returned clips. The most likely (as determined by Tortoises' CLVP model) clips are returned.
+        :param verbose: Whether or not to print log messages indicating the progress of creating a clip. Default=true.
+        ~~AUTOREGRESSIVE KNOBS~~
+        :param num_autoregressive_samples: Number of samples taken from the autoregressive model, all of which are filtered using CLVP.
+               As Tortoise is a probabilistic model, more samples means a higher probability of creating something "great".
+        :param temperature: The softmax temperature of the autoregressive model.
+        :param length_penalty: A length penalty applied to the autoregressive decoder. Higher settings causes the model to produce more terse outputs.
+        :param repetition_penalty: A penalty that prevents the autoregressive decoder from repeating itself during decoding. Can be used to reduce the incidence
+                                   of long silences or "uhhhhhhs", etc.
+        :param top_p: P value used in nucleus sampling. (0,1]. Lower values mean the decoder produces more "likely" (aka boring) outputs.
+        :param max_mel_tokens: Restricts the output length. (0,600] integer. Each unit is 1/20 of a second.
+        ~~DIFFUSION KNOBS~~
+        :param diffusion_iterations: Number of diffusion steps to perform. [0,4000]. More steps means the network has more chances to iteratively refine
+                                     the output, which should theoretically mean a higher quality output. Generally a value above 250 is not noticeably better,
+                                     however.
+        :param cond_free: Whether or not to perform conditioning-free diffusion. Conditioning-free diffusion performs two forward passes for
+                          each diffusion step: one with the outputs of the autoregressive model and one with no conditioning priors. The output
+                          of the two is blended according to the cond_free_k value below. Conditioning-free diffusion is the real deal, and
+                          dramatically improves realism.
+        :param cond_free_k: Knob that determines how to balance the conditioning free signal with the conditioning-present signal. [0,inf].
+                            As cond_free_k increases, the output becomes dominated by the conditioning-free signal.
+                            Formula is: output=cond_present_output*(cond_free_k+1)-cond_absenct_output*cond_free_k
+        :param diffusion_temperature: Controls the variance of the noise fed into the diffusion model. [0,1]. Values at 0
+                                      are the "mean" prediction of the diffusion network and will sound bland and smeared.
+        ~~OTHER STUFF~~
+        :param hf_generate_kwargs: The huggingface Transformers generate API is used for the autoregressive transformer.
+                                   Extra keyword args fed to this function get forwarded directly to that API. Documentation
+                                   here: https://huggingface.co/docs/transformers/internal/generation_utils
+        :return: Generated audio clip(s) as a torch tensor. Shape 1,S if k=1 else, (k,1,S) where S is the sample length.
+                 Sample rate is 24kHz.
+        """
+        deterministic_seed = self.deterministic_state(seed=use_deterministic_seed)
+
+        text_tokens = torch.IntTensor(self.tokenizer.encode(text)).unsqueeze(0).to(self.device)
+        text_tokens = F.pad(text_tokens, (0, 1))  # This may not be necessary.
+        assert text_tokens.shape[-1] < 400, 'Too much text provided. Break the text up into separate segments and re-try inference.'
+        if voice_samples is not None:
+            auto_conditioning = self.get_conditioning_latents(voice_samples, return_mels=False)
+        else:
+            auto_conditioning  = self.get_random_conditioning_latents()
+        auto_conditioning = auto_conditioning.to(self.device)
+
+        with torch.no_grad():
+            calm_token = 83  # This is the token for coding silence, which is fixed in place with "fix_autoregressive_output"
+            if verbose:
+                print("Generating autoregressive samples..")
+            with torch.autocast(
+                    device_type="cuda" , dtype=torch.float16, enabled=self.half
+                ):
+                codes = self.autoregressive.inference_speech(auto_conditioning, text_tokens,
+                                                            top_k=50,
+                                                            top_p=top_p,
+                                                            temperature=temperature,
+                                                            do_sample=True,
+                                                            num_beams=1,
+                                                            num_return_sequences=1,
+                                                            length_penalty=float(length_penalty),
+                                                            repetition_penalty=float(repetition_penalty),
+                                                            output_attentions=False,
+                                                            output_hidden_states=True,
+                                                            **hf_generate_kwargs)
+                gpt_latents = self.autoregressive(auto_conditioning.repeat(k, 1), text_tokens.repeat(k, 1),
+                                torch.tensor([text_tokens.shape[-1]], device=text_tokens.device), codes,
+                                torch.tensor([codes.shape[-1]*self.autoregressive.mel_length_compression], device=text_tokens.device),
+                                return_latent=True, clip_inputs=False)
+            if verbose:
+                print("generating audio..")
+            wav_gen = self.hifi_decoder.inference(gpt_latents.to(self.device), auto_conditioning)
+            return wav_gen
+    def deterministic_state(self, seed=None):
+        """
+        Sets the random seeds that tortoise uses to the current time() and returns that seed so results can be
+        reproduced.
+        """
+        seed = int(time()) if seed is None else seed
+        torch.manual_seed(seed)
+        random.seed(seed)
+        # Can't currently set this because of CUBLAS. TODO: potentially enable it if necessary.
+        # torch.use_deterministic_algorithms(True)
+
+        return seed
diff --git a/tortoise/tortoise/data/got.txt b/tortoise/tortoise/data/got.txt
new file mode 100644
index 0000000..a7180b9
--- /dev/null
+++ b/tortoise/tortoise/data/got.txt
@@ -0,0 +1,276 @@
+Chapter One
+
+
+Bran
+
+
+The morning had dawned clear and cold, with a crispness that hinted at the end of summer. They set forth at daybreak to see a man beheaded, twenty in all, and Bran rode among them, nervous with excitement. This was the first time he had been deemed old enough to go with his lord father and his brothers to see the king's justice done. It was the ninth year of summer, and the seventh of Bran's life.
+
+
+The man had been taken outside a small holdfast in the hills. Robb thought he was a wildling, his sword sworn to Mance Rayder, the King-beyond-the-Wall. It made Bran's skin prickle to think of it. He remembered the hearth tales Old Nan told them. The wildlings were cruel men, she said, slavers and slayers and thieves. They consorted with giants and ghouls, stole girl children in the dead of night, and drank blood from polished horns. And their women lay with the Others in the Long Night to sire terrible half-human children.
+
+
+But the man they found bound hand and foot to the holdfast wall awaiting the king's justice was old and scrawny, not much taller than Robb. He had lost both ears and a finger to frostbite, and he dressed all in black, the same as a brother of the Night's Watch, except that his furs were ragged and greasy.
+
+
+The breath of man and horse mingled, steaming, in the cold morning air as his lord father had the man cut down from the wall and dragged before them. Robb and Jon sat tall and still on their horses, with Bran between them on his pony, trying to seem older than seven, trying to pretend that he'd seen all this before. A faint wind blew through the holdfast gate. Over their heads flapped the banner of the Starks of Winterfell: a grey direwolf racing across an ice-white field.
+
+Bran's father sat solemnly on his horse, long brown hair stirring in the wind. His closely trimmed beard was shot with white, making him look older than his thirty-five years. He had a grim cast to his grey eyes this day, and he seemed not at all the man who would sit before the fire in the evening and talk softly of the age of heroes and the children of the forest. He had taken off Father's face, Bran thought, and donned the face of Lord Stark of Winterfell.
+
+
+There were questions asked and answers given there in the chill of morning, but afterward Bran could not recall much of what had been said. Finally his lord father gave a command, and two of his guardsmen dragged the ragged man to the ironwood stump in the center of the square. They forced his head down onto the hard black wood. Lord Eddard Stark dismounted and his ward Theon Greyjoy brought forth the sword. "Ice," that sword was called. It was as wide across as a man's hand, and taller even than Robb. The blade was Valyrian steel, spell-forged and dark as smoke. Nothing held an edge like Valyrian steel.
+
+
+His father peeled off his gloves and handed them to Jory Cassel, the captain of his household guard. He took hold of Ice with both hands and said, "In the name of Robert of the House Baratheon, the First of his Name, King of the Andals and the Rhoynar and the First Men, Lord of the Seven Kingdoms and Protector of the Realm, by the word of Eddard of the House Stark, Lord of Winterfell and Warden of the North, I do sentence you to die." He lifted the greatsword high above his head.
+
+
+Bran's bastard brother Jon Snow moved closer. "Keep the pony well in hand," he whispered. "And don't look away. Father will know if you do."
+
+
+Bran kept his pony well in hand, and did not look away.
+
+
+His father took off the man's head with a single sure stroke. Blood sprayed out across the snow, as red as surnmerwine. One of the horses reared and had to be restrained to keep from bolting. Bran could not take his eyes off the blood. The snows around the stump drank it eagerly, reddening as he watched.
+
+The head bounced off a thick root and rolled. It came up near Greyjoy's feet. Theon was a lean, dark youth of nineteen who found everything amusing. He laughed, put his boot on the head, and kicked it away.
+
+
+"Ass," Jon muttered, low enough so Greyjoy did not hear. He put a hand on Bran's shoulder, and Bran looked over at his bastard brother. "You did well," Jon told him solemnly. Jon was fourteen, an old hand at justice.
+
+
+It seemed colder on the long ride back to Winterfell, though the wind had died by then and the sun was higher in the sky. Bran rode with his brothers, well ahead of the main party, his pony struggling hard to keep up with their horses.
+
+
+"The deserter died bravely," Robb said. He was big and broad and growing every day, with his mother's coloring, the fair skin, red-brown hair, and blue eyes of the Tullys of Riverrun. "He had courage, at the least."
+
+
+"No," Jon Snow said quietly. "It was not courage. This one was dead of fear. You could see it in his eyes, Stark." Jon's eyes were a grey so dark they seemed almost black, but there was little they did not see. He was of an age with Robb, but they did not look alike. Jon was slender where Robb was muscular, dark where Robb was fair, graceful and quick where his half brother was strong and fast.
+
+
+Robb was not impressed. "The Others take his eyes," he swore. "He died well. Race you to the bridge?"
+
+
+"Done," Jon said, kicking his horse forward. Robb cursed and followed, and they galloped off down the trail, Robb laughing and hooting, Jon silent and intent. The hooves of their horses kicked up showers of snow as they went.
+
+Bran did not try to follow. His pony could not keep up. He had seen the ragged man's eyes, and he was thinking of them now. After a while, the sound of Robb's laughter receded, and the woods grew silent again.
+
+
+So deep in thought was he that he never heard the rest of the party until his father moved up to ride beside him. "Are you well, Bran?" he asked, not unkindly.
+
+
+"Yes, Father," Bran told him. He looked up. Wrapped in his furs and leathers, mounted on his great warhorse, his lord father loomed over him like a giant. "Robb says the man died bravely, but Jon says he was afraid."
+
+
+"What do you think?" his father asked.
+
+
+Bran thought about it. "Can a man still be brave if he's afraid?"
+
+
+"That is the only time a man can be brave," his father told him. "Do you understand why I did it?"
+
+
+"He was a wildling," Bran said. "They carry off women and sell them to the Others."
+
+
+His lord father smiled. "Old Nan has been telling you stories again. In truth, the man was an oathbreaker, a deserter from the Night's Watch. No man is more dangerous. The deserter knows his life is forfeit if he is taken, so he will not flinch from any crime, no matter how vile. But you mistake me. The question was not why the man had to die, but why I must do it."
+
+
+Bran had no answer for that. "King Robert has a headsman," he said, uncertainly.
+
+
+"He does," his father admitted. "As did the Targaryen kings before him. Yet our way is the older way. The blood of the First Men still flows in the veins of the Starks, and we hold to the belief that the man who passes the sentence should swing the sword. If you would take a man's life, you owe it to him to look into his eyes and hear his final words. And if you cannot bear to do that, then perhaps the man does not deserve to die.
+
+
+"One day, Bran, you will be Robb's bannerman, holding a keep of your own for your brother and your king, and justice will fall to you. When that day comes, you must take no pleasure in the task, but neither must you look away. A ruler who hides behind paid executioners soon forgets what death is."
+
+
+That was when Jon reappeared on the crest of the hill before them. He waved and shouted down at them. "Father, Bran, come quickly, see what Robb has found!" Then he was gone again.
+
+
+Jory rode up beside them. "Trouble, my lord?"
+
+
+"Beyond a doubt," his lord father said. "Come, let us see what mischief my sons have rooted out now." He sent his horse into a trot. Jory and Bran and the rest came after.
+
+
+They found Robb on the riverbank north of the bridge, with Jon still mounted beside him. The late summer snows had been heavy this moonturn. Robb stood knee-deep in white, his hood pulled back so the sun shone in his hair. He was cradling something in his arm, while the boys talked in hushed, excited voices.
+
+
+The riders picked their way carefully through the drifts, groping for solid footing on the hidden, uneven ground . Jory Cassel and Theon Greyjoy were the first to reach the boys. Greyjoy was laughing and joking as he rode. Bran heard the breath go out of him. "Gods!" he exclaimed, struggling to keep control of his horse as he reached for his sword.
+
+
+Jory's sword was already out. "Robb, get away from it!" he called as his horse reared under him.
+
+
+Robb grinned and looked up from the bundle in his arms. "She can't hurt you," he said. "She's dead, Jory."
+
+
+Bran was afire with curiosity by then. He would have spurred the pony faster, but his father made them dismount beside the bridge and approach on foot. Bran jumped off and ran.
+
+
+By then Jon, Jory, and Theon Greyjoy had all dismounted as well. "What in the seven hells is it?" Greyjoy was saying.
+
+
+"A wolf," Robb told him.
+
+
+"A freak," Greyjoy said. "Look at the size of it."
+
+
+Bran's heart was thumping in his chest as he pushed through a waist-high drift to his brothers' side.
+
+
+Half-buried in bloodstained snow, a huge dark shape slumped in death. Ice had formed in its shaggy grey fur, and the faint smell of corruption clung to it like a woman's perfume. Bran glimpsed blind eyes crawling with maggots, a wide mouth full of yellowed teeth. But it was the size of it that made him gasp. It was bigger than his pony, twice the size of the largest hound in his father's kennel.
+
+
+"It's no freak," Jon said calmly. "That's a direwolf. They grow larger than the other kind."
+
+
+Theon Greyjoy said, "There's not been a direwolf sighted south of the Wall in two hundred years."
+
+
+"I see one now," Jon replied.
+
+
+Bran tore his eyes away from the monster. That was when he noticed the bundle in Robb's arms. He gave a cry of delight and moved closer. The pup was a tiny ball of grey-black fur, its eyes still closed. It nuzzled blindly against Robb's chest as he cradled it, searching for milk among his leathers, making a sad little whimpery sound. Bran reached out hesitantly. "Go on," Robb told him. "You can touch him."
+
+
+Bran gave the pup a quick nervous stroke, then turned as Jon said, "Here you go." His half brother put a second pup into his arms. "There are five of them." Bran sat down in the snow and hugged the wolf pup to his face. Its fur was soft and warm against his cheek.
+
+
+"Direwolves loose in the realm, after so many years," muttered Hullen, the master of horse. "I like it not."
+
+
+"It is a sign," Jory said.
+
+
+Father frowned. "This is only a dead animal, Jory," he said. Yet he seemed troubled. Snow crunched under his boots as he moved around the body. "Do we know what killed her?"
+
+
+"There's something in the throat," Robb told him, proud to have found the answer before his father even asked. "There, just under the jaw."
+
+
+His father knelt and groped under the beast's head with his hand. He gave a yank and held it up for all to see. A foot of shattered antler, tines snapped off, all wet with blood.
+
+
+A sudden silence descended over the party. The men looked at the antler uneasily, and no one dared to speak. Even Bran could sense their fear, though he did not understand.
+
+
+His father tossed the antler to the side and cleansed his hands in the snow. "I'm surprised she lived long enough to whelp," he said. His voice broke the spell.
+
+
+"Maybe she didn't," Jory said. "I've heard tales . . . maybe the bitch was already dead when the pups came."
+
+
+"Born with the dead," another man put in. "Worse luck."
+
+
+"No matter," said Hullen. "They be dead soon enough too."
+
+
+Bran gave a wordless cry of dismay.
+
+
+"The sooner the better," Theon Greyjoy agreed. He drew his sword. "Give the beast here, Bran."
+
+
+The little thing squirmed against him, as if it heard and understood. "No!" Bran cried out fiercely. "It's mine."
+
+
+"Put away your sword, Greyjoy," Robb said. For a moment he sounded as commanding as their father, like the lord he would someday be. "We will keep these pups."
+
+
+"You cannot do that, boy," said Harwin, who was Hullen's son.
+
+
+"It be a mercy to kill them," Hullen said.
+
+
+Bran looked to his lord father for rescue, but got only a frown, a furrowed brow. "Hullen speaks truly, son. Better a swift death than a hard one from cold and starvation."
+
+
+"No!" He could feel tears welling in his eyes, and he looked away. He did not want to cry in front of his father.
+
+
+Robb resisted stubbornly. "Ser Rodrik's red bitch whelped again last week," he said. "It was a small litter, only two live pups. She'll have milk enough."
+
+
+"She'll rip them apart when they try to nurse."
+
+
+"Lord Stark," Jon said. It was strange to hear him call Father that, so formal. Bran looked at him with desperate hope. "There are five pups," he told Father. "Three male, two female."
+
+
+"What of it, Jon?"
+
+
+"You have five trueborn children," Jon said. "Three sons, two daughters. The direwolf is the sigil of your House. Your children were meant to have these pups, my lord."
+
+
+Bran saw his father's face change, saw the other men exchange glances. He loved Jon with all his heart at that moment. Even at seven, Bran understood what his brother had done. The count had come right only because Jon had omitted himself. He had included the girls, included even Rickon, the baby, but not the bastard who bore the surname Snow, the name that custom decreed be given to all those in the north unlucky enough to be born with no name of their own.
+
+
+Their father understood as well. "You want no pup for yourself, Jon?" he asked softly.
+
+
+"The direwolf graces the banners of House Stark," Jon pointed out. "I am no Stark, Father."
+
+
+Their lord father regarded Jon thoughtfully. Robb rushed into the silence he left. "I will nurse him myself, Father," he promised. "I will soak a towel with warm milk, and give him suck from that."
+
+
+"Me too!" Bran echoed.
+
+
+The lord weighed his sons long and carefully with his eyes. "Easy to say, and harder to do. I will not have you wasting the servants' time with this. If you want these pups, you will feed them yourselves. Is that understood?"
+
+
+Bran nodded eagerly. The pup squirmed in his grasp, licked at his face with a warm tongue.
+
+
+"You must train them as well," their father said. "You must train them. The kennelmaster will have nothing to do with these monsters, I promise you that. And the gods help you if you neglect them, or brutalize them, or train them badly. These are not dogs to beg for treats and slink off at a kick. A direwolf will rip a man's arm off his shoulder as easily as a dog will kill a rat. Are you sure you want this?"
+
+"Yes, Father," Bran said.
+
+
+"Yes," Robb agreed.
+
+
+"The pups may die anyway, despite all you do."
+
+
+"They won't die," Robb said. "We won't let them die."
+
+
+"Keep them, then. Jory, Desmond, gather up the other pups. It's time we were back to Winterfell."
+
+
+It was not until they were mounted and on their way that Bran allowed himself to taste the sweet air of victory. By then, his pup was snuggled inside his leathers, warm against him, safe for the long ride home. Bran was wondering what to name him.
+
+
+Halfway across the bridge, Jon pulled up suddenly.
+
+
+"What is it, Jon?" their lord father asked.
+
+
+"Can't you hear it?"
+
+
+Bran could hear the wind in the trees, the clatter of their hooves on the ironwood planks, the whimpering of his hungry pup, but Jon was listening to something else.
+
+
+"There," Jon said. He swung his horse around and galloped back across the bridge. They watched him dismount where the direwolf lay dead in the snow, watched him kneel. A moment later he was riding back to them, smiling.
+
+
+"He must have crawled away from the others," Jon said.
+
+
+"Or been driven away," their father said, looking at the sixth pup. His fur was white, where the rest of the litter was grey. His eyes were as red as the blood of the ragged man who had died that morning. Bran thought it curious that this pup alone would have opened his eyes while the others were still blind.
+
+
+"An albino," Theon Greyjoy said with wry amusement. "This one will die even faster than the others."
+
+
+Jon Snow gave his father's ward a long, chilling look. "I think not, Greyjoy," he said. "This one belongs to me."
\ No newline at end of file
diff --git a/tortoise/tortoise/data/layman.txt b/tortoise/tortoise/data/layman.txt
new file mode 100644
index 0000000..e69de29
diff --git a/tortoise/tortoise/data/mel_norms.pth b/tortoise/tortoise/data/mel_norms.pth
new file mode 100644
index 0000000..d8c7321
Binary files /dev/null and b/tortoise/tortoise/data/mel_norms.pth differ
diff --git a/tortoise/tortoise/data/riding_hood.txt b/tortoise/tortoise/data/riding_hood.txt
new file mode 100644
index 0000000..2987bef
--- /dev/null
+++ b/tortoise/tortoise/data/riding_hood.txt
@@ -0,0 +1,54 @@
+Once upon a time there lived in a certain village a little country girl, the prettiest creature who was ever seen. Her mother was excessively fond of her; and her grandmother doted on her still more. This good woman had a little red riding hood made for her. It suited the girl so extremely well that everybody called her Little Red Riding Hood.
+One day her mother, having made some cakes, said to her, "Go, my dear, and see how your grandmother is doing, for I hear she has been very ill. Take her a cake, and this little pot of butter."
+
+Little Red Riding Hood set out immediately to go to her grandmother, who lived in another village.
+
+As she was going through the wood, she met with a wolf, who had a very great mind to eat her up, but he dared not, because of some woodcutters working nearby in the forest. He asked her where she was going. The poor child, who did not know that it was dangerous to stay and talk to a wolf, said to him, "I am going to see my grandmother and carry her a cake and a little pot of butter from my mother."
+
+"Does she live far off?" said the wolf
+
+"Oh I say," answered Little Red Riding Hood; "it is beyond that mill you see there, at the first house in the village."
+
+"Well," said the wolf, "and I'll go and see her too. I'll go this way and go you that, and we shall see who will be there first."
+
+The wolf ran as fast as he could, taking the shortest path, and the little girl took a roundabout way, entertaining herself by gathering nuts, running after butterflies, and gathering bouquets of little flowers. It was not long before the wolf arrived at the old woman's house. He knocked at the door: tap, tap.
+
+"Who's there?"
+
+"Your grandchild, Little Red Riding Hood," replied the wolf, counterfeiting her voice; "who has brought you a cake and a little pot of butter sent you by mother."
+
+The good grandmother, who was in bed, because she was somewhat ill, cried out, "Pull the bobbin, and the latch will go up."
+
+The wolf pulled the bobbin, and the door opened, and then he immediately fell upon the good woman and ate her up in a moment, for it been more than three days since he had eaten. He then shut the door and got into the grandmother's bed, expecting Little Red Riding Hood, who came some time afterwards and knocked at the door: tap, tap.
+
+"Who's there?"
+
+Little Red Riding Hood, hearing the big voice of the wolf, was at first afraid; but believing her grandmother had a cold and was hoarse, answered, "It is your grandchild Little Red Riding Hood, who has brought you a cake and a little pot of butter mother sends you."
+
+The wolf cried out to her, softening his voice as much as he could, "Pull the bobbin, and the latch will go up."
+
+Little Red Riding Hood pulled the bobbin, and the door opened.
+
+The wolf, seeing her come in, said to her, hiding himself under the bedclothes, "Put the cake and the little pot of butter upon the stool, and come get into bed with me."
+
+Little Red Riding Hood took off her clothes and got into bed. She was greatly amazed to see how her grandmother looked in her nightclothes, and said to her, "Grandmother, what big arms you have!"
+
+"All the better to hug you with, my dear."
+
+"Grandmother, what big legs you have!"
+
+"All the better to run with, my child."
+
+"Grandmother, what big ears you have!"
+
+"All the better to hear with, my child."
+
+"Grandmother, what big eyes you have!"
+
+"All the better to see with, my child."
+
+"Grandmother, what big teeth you have got!"
+
+"All the better to eat you up with."
+
+And, saying these words, this wicked wolf fell upon Little Red Riding Hood, and ate her all up.
\ No newline at end of file
diff --git a/tortoise/tortoise/data/seal_copypasta.txt b/tortoise/tortoise/data/seal_copypasta.txt
new file mode 100644
index 0000000..ce59a38
--- /dev/null
+++ b/tortoise/tortoise/data/seal_copypasta.txt
@@ -0,0 +1 @@
+What the fuck did you just fucking say about me, you little bitch? I'll have you know I graduated top of my class in the Navy Seals, and I've been involved in numerous secret raids on Al kayda, and I have over 300 confirmed kills. I am trained in gorilla warfare and I'm the top sniper in the entire U S armed forces. You are nothing to me but just another target. I will wipe you the fuck out with precision the likes of which has never been seen before on this Earth, mark my fucking words. You think you can get away with saying that shit to me over the Internet? Think again, fucker. As we speak I am contacting my secret network of spies across the U S A and your IP is being traced right now so you better prepare for the storm, maggot. The storm that wipes out the pathetic little thing you call your life. You're fucking dead, kid. I can be anywhere, anytime, and I can kill you in over seven hundred ways, and that's just with my bare hands. Not only am I extensively trained in unarmed combat, but I have access to the entire arsenal of the United States Marine Corps and I will use it to its full extent to wipe your miserable ass off the face of the continent, you little shit. If only you could have known what unholy retribution your little "clever" comment was about to bring down upon you, maybe you would have held your fucking tongue. But you couldn't, you didn't, and now you're paying the price, you goddamn idiot. I will shit fury all over you and you will drown in it. You're fucking dead, kiddo.
\ No newline at end of file
diff --git a/tortoise/tortoise/data/tokenizer.json b/tortoise/tortoise/data/tokenizer.json
new file mode 100644
index 0000000..a128f27
--- /dev/null
+++ b/tortoise/tortoise/data/tokenizer.json
@@ -0,0 +1 @@
+{"version":"1.0","truncation":null,"padding":null,"added_tokens":[{"id":0,"special":true,"content":"[STOP]","single_word":false,"lstrip":false,"rstrip":false,"normalized":false},{"id":1,"special":true,"content":"[UNK]","single_word":false,"lstrip":false,"rstrip":false,"normalized":false},{"id":2,"special":true,"content":"[SPACE]","single_word":false,"lstrip":false,"rstrip":false,"normalized":false}],"normalizer":null,"pre_tokenizer":{"type":"Whitespace"},"post_processor":null,"decoder":null,"model":{"type":"BPE","dropout":null,"unk_token":"[UNK]","continuing_subword_prefix":null,"end_of_word_suffix":null,"fuse_unk":false,"vocab":{"[STOP]":0,"[UNK]":1,"[SPACE]":2,"!":3,"'":4,"(":5,")":6,",":7,"-":8,".":9,"/":10,":":11,";":12,"?":13,"a":14,"b":15,"c":16,"d":17,"e":18,"f":19,"g":20,"h":21,"i":22,"j":23,"k":24,"l":25,"m":26,"n":27,"o":28,"p":29,"q":30,"r":31,"s":32,"t":33,"u":34,"v":35,"w":36,"x":37,"y":38,"z":39,"th":40,"in":41,"the":42,"an":43,"er":44,"ou":45,"re":46,"on":47,"at":48,"ed":49,"en":50,"to":51,"ing":52,"and":53,"is":54,"as":55,"al":56,"or":57,"of":58,"ar":59,"it":60,"es":61,"he":62,"st":63,"le":64,"om":65,"se":66,"be":67,"ad":68,"ow":69,"ly":70,"ch":71,"wh":72,"that":73,"you":74,"li":75,"ve":76,"ac":77,"ti":78,"ld":79,"me":80,"was":81,"gh":82,"id":83,"ll":84,"wi":85,"ent":86,"for":87,"ay":88,"ro":89,"ver":90,"ic":91,"her":92,"ke":93,"his":94,"no":95,"ut":96,"un":97,"ir":98,"lo":99,"we":100,"ri":101,"ha":102,"with":103,"ght":104,"out":105,"im":106,"ion":107,"all":108,"ab":109,"one":110,"ne":111,"ge":112,"ould":113,"ter":114,"mo":115,"had":116,"ce":117,"she":118,"go":119,"sh":120,"ur":121,"am":122,"so":123,"pe":124,"my":125,"de":126,"are":127,"but":128,"ome":129,"fr":130,"ther":131,"fe":132,"su":133,"do":134,"con":135,"te":136,"ain":137,"ere":138,"po":139,"if":140,"they":141,"us":142,"ag":143,"tr":144,"now":145,"oun":146,"this":147,"have":148,"not":149,"sa":150,"il":151,"up":152,"thing":153,"from":154,"ap":155,"him":156,"ack":157,"ation":158,"ant":159,"our":160,"op":161,"like":162,"ust":163,"ess":164,"bo":165,"ok":166,"ul":167,"ind":168,"ex":169,"com":170,"some":171,"there":172,"ers":173,"co":174,"res":175,"man":176,"ard":177,"pl":178,"wor":179,"way":180,"tion":181,"fo":182,"ca":183,"were":184,"by":185,"ate":186,"pro":187,"ted":188,"ound":189,"own":190,"would":191,"ts":192,"what":193,"qu":194,"ally":195,"ight":196,"ck":197,"gr":198,"when":199,"ven":200,"can":201,"ough":202,"ine":203,"end":204,"per":205,"ous":206,"od":207,"ide":208,"know":209,"ty":210,"very":211,"si":212,"ak":213,"who":214,"about":215,"ill":216,"them":217,"est":218,"red":219,"ye":220,"could":221,"ong":222,"your":223,"their":224,"em":225,"just":226,"other":227,"into":228,"any":229,"whi":230,"um":231,"tw":232,"ast":233,"der":234,"did":235,"ie":236,"been":237,"ace":238,"ink":239,"ity":240,"back":241,"ting":242,"br":243,"more":244,"ake":245,"pp":246,"then":247,"sp":248,"el":249,"use":250,"bl":251,"said":252,"over":253,"get":254},"merges":["t h","i n","th e","a n","e r","o u","r e","o n","a t","e d","e n","t o","in g","an d","i s","a s","a l","o r","o f","a r","i t","e s","h e","s t","l e","o m","s e","b e","a d","o w","l y","c h","w h","th at","y ou","l i","v e","a c","t i","l d","m e","w as","g h","i d","l l","w i","en t","f or","a y","r o","v er","i c","h er","k e","h is","n o","u t","u n","i r","l o","w e","r i","h a","wi th","gh t","ou t","i m","i on","al l","a b","on e","n e","g e","ou ld","t er","m o","h ad","c e","s he","g o","s h","u r","a m","s o","p e","m y","d e","a re","b ut","om e","f r","the r","f e","s u","d o","c on","t e","a in","er e","p o","i f","the y","u s","a g","t r","n ow","ou n","th is","ha ve","no t","s a","i l","u p","th ing","fr om","a p","h im","ac k","at ion","an t","ou r","o p","li ke","u st","es s","b o","o k","u l","in d","e x","c om","s ome","the re","er s","c o","re s","m an","ar d","p l","w or","w ay","ti on","f o","c a","w ere","b y","at e","p ro","t ed","oun d","ow n","w ould","t s","wh at","q u","al ly","i ght","c k","g r","wh en","v en","c an","ou gh","in e","en d","p er","ou s","o d","id e","k now","t y","ver y","s i","a k","wh o","ab out","i ll","the m","es t","re d","y e","c ould","on g","you r","the ir","e m","j ust","o ther","in to","an y","wh i","u m","t w","as t","d er","d id","i e","be en","ac e","in k","it y","b ack","t ing","b r","mo re","a ke","p p","the n","s p","e l","u se","b l","sa id","o ver","ge t"]}}
\ No newline at end of file
diff --git a/tortoise/tortoise/do_tts.py b/tortoise/tortoise/do_tts.py
new file mode 100644
index 0000000..c6e2b17
--- /dev/null
+++ b/tortoise/tortoise/do_tts.py
@@ -0,0 +1,52 @@
+import argparse
+import os
+
+import torch
+import torchaudio
+
+from api import TextToSpeech, MODELS_DIR
+from utils.audio import load_voices
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--text', type=str, help='Text to speak.', default="The expressiveness of autoregressive transformers is literally nuts! I absolutely adore them.")
+    parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) '
+                                                 'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='random')
+    parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='fast')
+    parser.add_argument('--use_deepspeed', type=str, help='Use deepspeed for speed bump.', default=False)
+    parser.add_argument('--kv_cache', type=bool, help='If you disable this please wait for a long a time to get the output', default=True)
+    parser.add_argument('--half', type=bool, help="float16(half) precision inference if True it's faster and take less vram and ram", default=True)
+    parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/')
+    parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this'
+                                                      'should only be specified if you have custom checkpoints.', default=MODELS_DIR)
+    parser.add_argument('--candidates', type=int, help='How many output candidates to produce per-voice.', default=3)
+    parser.add_argument('--seed', type=int, help='Random seed which can be used to reproduce results.', default=None)
+    parser.add_argument('--produce_debug_state', type=bool, help='Whether or not to produce debug_state.pth, which can aid in reproducing problems. Defaults to true.', default=True)
+    parser.add_argument('--cvvp_amount', type=float, help='How much the CVVP model should influence the output.'
+                                                          'Increasing this can in some cases reduce the likelihood of multiple speakers. Defaults to 0 (disabled)', default=.0)
+    args = parser.parse_args()
+    if torch.backends.mps.is_available():
+        args.use_deepspeed = False
+    os.makedirs(args.output_path, exist_ok=True)
+    tts = TextToSpeech(models_dir=args.model_dir, use_deepspeed=args.use_deepspeed, kv_cache=args.kv_cache, half=args.half)
+
+    selected_voices = args.voice.split(',')
+    for k, selected_voice in enumerate(selected_voices):
+        if '&' in selected_voice:
+            voice_sel = selected_voice.split('&')
+        else:
+            voice_sel = [selected_voice]
+        voice_samples, conditioning_latents = load_voices(voice_sel)
+
+        gen, dbg_state = tts.tts_with_preset(args.text, k=args.candidates, voice_samples=voice_samples, conditioning_latents=conditioning_latents,
+                                  preset=args.preset, use_deterministic_seed=args.seed, return_deterministic_state=True, cvvp_amount=args.cvvp_amount)
+        if isinstance(gen, list):
+            for j, g in enumerate(gen):
+                torchaudio.save(os.path.join(args.output_path, f'{selected_voice}_{k}_{j}.wav'), g.squeeze(0).cpu(), 24000)
+        else:
+            torchaudio.save(os.path.join(args.output_path, f'{selected_voice}_{k}.wav'), gen.squeeze(0).cpu(), 24000)
+
+        if args.produce_debug_state:
+            os.makedirs('debug_states', exist_ok=True)
+            torch.save(dbg_state, f'debug_states/do_tts_debug_{selected_voice}.pth')
+
diff --git a/tortoise/tortoise/eval.py b/tortoise/tortoise/eval.py
new file mode 100644
index 0000000..312b162
--- /dev/null
+++ b/tortoise/tortoise/eval.py
@@ -0,0 +1,27 @@
+import argparse
+import os
+
+import torchaudio
+
+from api import TextToSpeech
+from tortoise.utils.audio import load_audio
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--eval_path', type=str, help='Path to TSV test file', default="D:\\tmp\\tortoise-tts-eval\\test.tsv")
+    parser.add_argument('--output_path', type=str, help='Where to put results', default="D:\\tmp\\tortoise-tts-eval\\baseline")
+    parser.add_argument('--preset', type=str, help='Rendering preset.', default="standard")
+    args = parser.parse_args()
+    os.makedirs(args.output_path, exist_ok=True)
+
+    tts = TextToSpeech()
+
+    with open(args.eval_path, 'r', encoding='utf-8') as f:
+        lines = f.readlines()
+
+    for line in lines:
+        text, real = line.strip().split('\t')
+        conds = [load_audio(real, 22050)]
+        gen = tts.tts_with_preset(text, voice_samples=conds, conditioning_latents=None, preset=args.preset)
+        torchaudio.save(os.path.join(args.output_path, os.path.basename(real)), gen.squeeze(0).cpu(), 24000)
+
diff --git a/tortoise/tortoise/get_conditioning_latents.py b/tortoise/tortoise/get_conditioning_latents.py
new file mode 100644
index 0000000..aa7e9b7
--- /dev/null
+++ b/tortoise/tortoise/get_conditioning_latents.py
@@ -0,0 +1,30 @@
+import argparse
+import os
+import torch
+
+from api import TextToSpeech
+from tortoise.utils.audio import load_audio, get_voices
+
+"""
+Dumps the conditioning latents for the specified voice to disk. These are expressive latents which can be used for
+other ML models, or can be augmented manually and fed back into Tortoise to affect vocal qualities.
+"""
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--voice', type=str, help='Selects the voice to convert to conditioning latents', default='pat2')
+    parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='../results/conditioning_latents')
+    args = parser.parse_args()
+    os.makedirs(args.output_path, exist_ok=True)
+
+    tts = TextToSpeech()
+    voices = get_voices()
+    selected_voices = args.voice.split(',')
+    for voice in selected_voices:
+        cond_paths = voices[voice]
+        conds = []
+        for cond_path in cond_paths:
+            c = load_audio(cond_path, 22050)
+            conds.append(c)
+        conditioning_latents = tts.get_conditioning_latents(conds)
+        torch.save(conditioning_latents, os.path.join(args.output_path, f'{voice}.pth'))
+
diff --git a/tortoise/tortoise/is_this_from_tortoise.py b/tortoise/tortoise/is_this_from_tortoise.py
new file mode 100644
index 0000000..289844f
--- /dev/null
+++ b/tortoise/tortoise/is_this_from_tortoise.py
@@ -0,0 +1,14 @@
+import argparse
+
+from api import classify_audio_clip
+from tortoise.utils.audio import load_audio
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--clip', type=str, help='Path to an audio clip to classify.', default="../examples/favorite_riding_hood.mp3")
+    args = parser.parse_args()
+
+    clip = load_audio(args.clip, 24000)
+    clip = clip[:, :220000]
+    prob = classify_audio_clip(clip)
+    print(f"This classifier thinks there is a {prob*100}% chance that this clip was generated from Tortoise.")
\ No newline at end of file
diff --git a/tortoise/tortoise/models/__init__.py b/tortoise/tortoise/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tortoise/tortoise/models/arch_util.py b/tortoise/tortoise/models/arch_util.py
new file mode 100644
index 0000000..f678a02
--- /dev/null
+++ b/tortoise/tortoise/models/arch_util.py
@@ -0,0 +1,373 @@
+import os
+import functools
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchaudio
+from tortoise.models.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
+
+
+def zero_module(module):
+    """
+    Zero out the parameters of a module and return it.
+    """
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+class GroupNorm32(nn.GroupNorm):
+    def forward(self, x):
+        return super().forward(x.float()).type(x.dtype)
+
+
+def normalization(channels):
+    """
+    Make a standard normalization layer.
+
+    :param channels: number of input channels.
+    :return: an nn.Module for normalization.
+    """
+    groups = 32
+    if channels <= 16:
+        groups = 8
+    elif channels <= 64:
+        groups = 16
+    while channels % groups != 0:
+        groups = int(groups / 2)
+    assert groups > 2
+    return GroupNorm32(groups, channels)
+
+
+class QKVAttentionLegacy(nn.Module):
+    """
+    A module which performs QKV attention. Matches legacy QKVAttention + input/output heads shaping
+    """
+
+    def __init__(self, n_heads):
+        super().__init__()
+        self.n_heads = n_heads
+
+    def forward(self, qkv, mask=None, rel_pos=None):
+        """
+        Apply QKV attention.
+
+        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
+        :return: an [N x (H * C) x T] tensor after attention.
+        """
+        bs, width, length = qkv.shape
+        assert width % (3 * self.n_heads) == 0
+        ch = width // (3 * self.n_heads)
+        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+        scale = 1 / math.sqrt(math.sqrt(ch))
+        weight = torch.einsum(
+            "bct,bcs->bts", q * scale, k * scale
+        )  # More stable with f16 than dividing afterwards
+        if rel_pos is not None:
+            weight = rel_pos(weight.reshape(bs, self.n_heads, weight.shape[-2], weight.shape[-1])).reshape(bs * self.n_heads, weight.shape[-2], weight.shape[-1])
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
+        if mask is not None:
+            # The proper way to do this is to mask before the softmax using -inf, but that doesn't work properly on CPUs.
+            mask = mask.repeat(self.n_heads, 1).unsqueeze(1)
+            weight = weight * mask
+        a = torch.einsum("bts,bcs->bct", weight, v)
+
+        return a.reshape(bs, -1, length)
+
+
+class AttentionBlock(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other.
+
+    Originally ported from here, but adapted to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    """
+
+    def __init__(
+        self,
+        channels,
+        num_heads=1,
+        num_head_channels=-1,
+        do_checkpoint=True,
+        relative_pos_embeddings=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.do_checkpoint = do_checkpoint
+        if num_head_channels == -1:
+            self.num_heads = num_heads
+        else:
+            assert (
+                channels % num_head_channels == 0
+            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+            self.num_heads = channels // num_head_channels
+        self.norm = normalization(channels)
+        self.qkv = nn.Conv1d(channels, channels * 3, 1)
+        # split heads before split qkv
+        self.attention = QKVAttentionLegacy(self.num_heads)
+
+        self.proj_out = zero_module(nn.Conv1d(channels, channels, 1))
+        if relative_pos_embeddings:
+            self.relative_pos_embeddings = RelativePositionBias(scale=(channels // self.num_heads) ** .5, causal=False, heads=num_heads, num_buckets=32, max_distance=64)
+        else:
+            self.relative_pos_embeddings = None
+
+    def forward(self, x, mask=None):
+        b, c, *spatial = x.shape
+        x = x.reshape(b, c, -1)
+        qkv = self.qkv(self.norm(x))
+        h = self.attention(qkv, mask, self.relative_pos_embeddings)
+        h = self.proj_out(h)
+        return (x + h).reshape(b, c, *spatial)
+
+
+class Upsample(nn.Module):
+    """
+    An upsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    """
+
+    def __init__(self, channels, use_conv, out_channels=None, factor=4):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.factor = factor
+        if use_conv:
+            ksize = 5
+            pad = 2
+            self.conv = nn.Conv1d(self.channels, self.out_channels, ksize, padding=pad)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        x = F.interpolate(x, scale_factor=self.factor, mode="nearest")
+        if self.use_conv:
+            x = self.conv(x)
+        return x
+
+
+class Downsample(nn.Module):
+    """
+    A downsampling layer with an optional convolution.
+
+    :param channels: channels in the inputs and outputs.
+    :param use_conv: a bool determining if a convolution is applied.
+    """
+
+    def __init__(self, channels, use_conv, out_channels=None, factor=4, ksize=5, pad=2):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+
+        stride = factor
+        if use_conv:
+            self.op = nn.Conv1d(
+                self.channels, self.out_channels, ksize, stride=stride, padding=pad
+            )
+        else:
+            assert self.channels == self.out_channels
+            self.op = nn.AvgPool1d(kernel_size=stride, stride=stride)
+
+    def forward(self, x):
+        assert x.shape[1] == self.channels
+        return self.op(x)
+
+
+class ResBlock(nn.Module):
+    def __init__(
+            self,
+            channels,
+            dropout,
+            out_channels=None,
+            use_conv=False,
+            use_scale_shift_norm=False,
+            up=False,
+            down=False,
+            kernel_size=3,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_scale_shift_norm = use_scale_shift_norm
+        padding = 1 if kernel_size == 3 else 2
+
+        self.in_layers = nn.Sequential(
+            normalization(channels),
+            nn.SiLU(),
+            nn.Conv1d(channels, self.out_channels, kernel_size, padding=padding),
+        )
+
+        self.updown = up or down
+
+        if up:
+            self.h_upd = Upsample(channels, False)
+            self.x_upd = Upsample(channels, False)
+        elif down:
+            self.h_upd = Downsample(channels, False)
+            self.x_upd = Downsample(channels, False)
+        else:
+            self.h_upd = self.x_upd = nn.Identity()
+
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels),
+            nn.SiLU(),
+            nn.Dropout(p=dropout),
+            zero_module(
+                nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding)
+            ),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        elif use_conv:
+            self.skip_connection = nn.Conv1d(
+                channels, self.out_channels, kernel_size, padding=padding
+            )
+        else:
+            self.skip_connection = nn.Conv1d(channels, self.out_channels, 1)
+
+    def forward(self, x):
+        if self.updown:
+            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+            h = in_rest(x)
+            h = self.h_upd(h)
+            x = self.x_upd(x)
+            h = in_conv(h)
+        else:
+            h = self.in_layers(x)
+        h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class AudioMiniEncoder(nn.Module):
+    def __init__(self,
+                 spec_dim,
+                 embedding_dim,
+                 base_channels=128,
+                 depth=2,
+                 resnet_blocks=2,
+                 attn_blocks=4,
+                 num_attn_heads=4,
+                 dropout=0,
+                 downsample_factor=2,
+                 kernel_size=3):
+        super().__init__()
+        self.init = nn.Sequential(
+            nn.Conv1d(spec_dim, base_channels, 3, padding=1)
+        )
+        ch = base_channels
+        res = []
+        for l in range(depth):
+            for r in range(resnet_blocks):
+                res.append(ResBlock(ch, dropout, kernel_size=kernel_size))
+            res.append(Downsample(ch, use_conv=True, out_channels=ch*2, factor=downsample_factor))
+            ch *= 2
+        self.res = nn.Sequential(*res)
+        self.final = nn.Sequential(
+            normalization(ch),
+            nn.SiLU(),
+            nn.Conv1d(ch, embedding_dim, 1)
+        )
+        attn = []
+        for a in range(attn_blocks):
+            attn.append(AttentionBlock(embedding_dim, num_attn_heads,))
+        self.attn = nn.Sequential(*attn)
+        self.dim = embedding_dim
+
+    def forward(self, x):
+        h = self.init(x)
+        h = self.res(h)
+        h = self.final(h)
+        h = self.attn(h)
+        return h[:, :, 0]
+
+
+DEFAULT_MEL_NORM_FILE = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../data/mel_norms.pth')
+
+
+class TorchMelSpectrogram(nn.Module):
+    def __init__(self, filter_length=1024, hop_length=256, win_length=1024, n_mel_channels=80, mel_fmin=0, mel_fmax=8000,
+                 sampling_rate=22050, normalize=False, mel_norm_file=DEFAULT_MEL_NORM_FILE):
+        super().__init__()
+        # These are the default tacotron values for the MEL spectrogram.
+        self.filter_length = filter_length
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.n_mel_channels = n_mel_channels
+        self.mel_fmin = mel_fmin
+        self.mel_fmax = mel_fmax
+        self.sampling_rate = sampling_rate
+        self.mel_stft = torchaudio.transforms.MelSpectrogram(n_fft=self.filter_length, hop_length=self.hop_length,
+                                                             win_length=self.win_length, power=2, normalized=normalize,
+                                                             sample_rate=self.sampling_rate, f_min=self.mel_fmin,
+                                                             f_max=self.mel_fmax, n_mels=self.n_mel_channels,
+                                                             norm="slaney")
+        self.mel_norm_file = mel_norm_file
+        if self.mel_norm_file is not None:
+            self.mel_norms = torch.load(self.mel_norm_file)
+        else:
+            self.mel_norms = None
+
+    def forward(self, inp):
+        if len(inp.shape) == 3:  # Automatically squeeze out the channels dimension if it is present (assuming mono-audio)
+            inp = inp.squeeze(1)
+        assert len(inp.shape) == 2
+        if torch.backends.mps.is_available():
+            inp = inp.to('cpu')
+        self.mel_stft = self.mel_stft.to(inp.device)
+        mel = self.mel_stft(inp)
+        # Perform dynamic range compression
+        mel = torch.log(torch.clamp(mel, min=1e-5))
+        if self.mel_norms is not None:
+            self.mel_norms = self.mel_norms.to(mel.device)
+            mel = mel / self.mel_norms.unsqueeze(0).unsqueeze(-1)
+        return mel
+
+
+class CheckpointedLayer(nn.Module):
+    """
+    Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
+    checkpoint for all other args.
+    """
+    def __init__(self, wrap):
+        super().__init__()
+        self.wrap = wrap
+
+    def forward(self, x, *args, **kwargs):
+        for k, v in kwargs.items():
+            assert not (isinstance(v, torch.Tensor) and v.requires_grad)  # This would screw up checkpointing.
+        partial = functools.partial(self.wrap, **kwargs)
+        return partial(x, *args)
+
+
+class CheckpointedXTransformerEncoder(nn.Module):
+    """
+    Wraps a ContinuousTransformerWrapper and applies CheckpointedLayer to each layer and permutes from channels-mid
+    to channels-last that XTransformer expects.
+    """
+    def __init__(self, needs_permute=True, exit_permute=True, checkpoint=True, **xtransformer_kwargs):
+        super().__init__()
+        self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs)
+        self.needs_permute = needs_permute
+        self.exit_permute = exit_permute
+
+        if not checkpoint:
+            return
+        for i in range(len(self.transformer.attn_layers.layers)):
+            n, b, r = self.transformer.attn_layers.layers[i]
+            self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
+
+    def forward(self, x, **kwargs):
+        if self.needs_permute:
+            x = x.permute(0,2,1)
+        h = self.transformer(x, **kwargs)
+        if self.exit_permute:
+            h = h.permute(0,2,1)
+        return h
\ No newline at end of file
diff --git a/tortoise/tortoise/models/autoregressive.py b/tortoise/tortoise/models/autoregressive.py
new file mode 100644
index 0000000..fcd1a94
--- /dev/null
+++ b/tortoise/tortoise/models/autoregressive.py
@@ -0,0 +1,582 @@
+import functools
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers import GPT2Config, GPT2PreTrainedModel, LogitsProcessorList
+from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
+from transformers.utils.model_parallel_utils import get_device_map, assert_device_map
+from tortoise.models.arch_util import AttentionBlock
+from tortoise.utils.typical_sampling import TypicalLogitsWarper
+
+
+def null_position_embeddings(range, dim):
+    return torch.zeros((range.shape[0], range.shape[1], dim), device=range.device)
+
+
+class ResBlock(nn.Module):
+    """
+    Basic residual convolutional block that uses GroupNorm.
+    """
+    def __init__(self, chan):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Conv1d(chan, chan, kernel_size=3, padding=1),
+            nn.GroupNorm(chan//8, chan),
+            nn.ReLU(),
+            nn.Conv1d(chan, chan, kernel_size=3, padding=1),
+            nn.GroupNorm(chan//8, chan)
+        )
+
+    def forward(self, x):
+        return F.relu(self.net(x) + x)
+
+
+class GPT2InferenceModel(GPT2PreTrainedModel):
+    def __init__(self, config, gpt, text_pos_emb, embeddings, norm, linear, kv_cache=False):
+        super().__init__(config)
+        self.transformer = gpt
+        self.text_pos_embedding = text_pos_emb
+        self.embeddings = embeddings
+        self.final_norm = norm
+        self.lm_head = nn.Sequential(norm, linear)
+        self.kv_cache = kv_cache
+        
+        # Model parallel
+        self.model_parallel = False
+        self.device_map = None
+        self.cached_mel_emb = None
+    def parallelize(self, device_map=None):
+        self.device_map = (
+            get_device_map(len(self.transformer.h), range(max(1, torch.cuda.device_count())))
+            if device_map is None
+            else device_map
+        )
+        assert_device_map(self.device_map, len(self.transformer.h))
+        self.transformer.parallelize(self.device_map)
+        self.lm_head = self.lm_head.to(self.transformer.first_device)
+        self.model_parallel = True
+
+    def deparallelize(self):
+        self.transformer.deparallelize()
+        self.transformer = self.transformer.to("cpu")
+        self.lm_head = self.lm_head.to("cpu")
+        self.model_parallel = False
+        torch.cuda.empty_cache()
+        if torch.backends.mps.is_available():
+            torch.mps.empty_cache()
+    
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+    
+    def store_mel_emb(self, mel_emb):
+        self.cached_mel_emb = mel_emb
+
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
+        token_type_ids = kwargs.get("token_type_ids", None)  # usually None
+        if not self.kv_cache:
+            past_key_values = None
+        # only last token for inputs_ids if past is defined in kwargs
+        if past_key_values:
+            input_ids = input_ids[:, -1].unsqueeze(-1)
+            if token_type_ids is not None:
+                token_type_ids = token_type_ids[:, -1].unsqueeze(-1)
+
+        attention_mask = kwargs.get("attention_mask", None)
+        position_ids = kwargs.get("position_ids", None)
+
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+        else:
+            position_ids = None
+        return {
+            "input_ids": input_ids,
+            "past_key_values": past_key_values,
+            "use_cache": kwargs.get("use_cache"),
+            "position_ids": position_ids,
+            "attention_mask": attention_mask,
+            "token_type_ids": token_type_ids,
+        }
+
+    def forward(
+        self,
+        input_ids=None,
+        past_key_values=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        labels=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        assert self.cached_mel_emb is not None
+        assert inputs_embeds is None  # Not supported by this inference model.
+        assert labels is None  # Training not supported by this inference model.
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # Create embedding
+        mel_len = self.cached_mel_emb.shape[1]
+        if input_ids.shape[1] != 1:
+            text_inputs = input_ids[:, mel_len:]
+            text_emb = self.embeddings(text_inputs)
+            text_emb = text_emb + self.text_pos_embedding(text_emb)
+            if self.cached_mel_emb.shape[0] != text_emb.shape[0]:
+                mel_emb = self.cached_mel_emb.repeat_interleave(
+                    text_emb.shape[0] // self.cached_mel_emb.shape[0], 0
+                )
+            else:  # this outcome only occurs once per loop in most cases
+                mel_emb = self.cached_mel_emb
+            emb = torch.cat([mel_emb, text_emb], dim=1)
+        else:
+            emb = self.embeddings(input_ids)
+            emb = emb + self.text_pos_embedding.get_fixed_embedding(
+                attention_mask.shape[1] - mel_len, attention_mask.device
+            )
+        transformer_outputs = self.transformer(
+            inputs_embeds=emb,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+
+        # Set device for model parallelism
+        if self.model_parallel:
+            if torch.backends.mps.is_available():
+                self.to(self.transformer.first_device)
+            else:
+                torch.cuda.set_device(self.transformer.first_device)
+            hidden_states = hidden_states.to(self.lm_head.weight.device)
+
+        lm_logits = self.lm_head(hidden_states)
+
+        if not return_dict:
+            return (lm_logits,) + transformer_outputs[1:]
+
+        return CausalLMOutputWithCrossAttentions(
+            loss=None,
+            logits=lm_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+            cross_attentions=transformer_outputs.cross_attentions,
+        )
+
+    @staticmethod
+    def _reorder_cache(past, beam_idx):
+        """
+        This function is used to re-order the :obj:`past_key_values` cache if
+        :meth:`~transformers.PreTrainedModel.beam_search` or :meth:`~transformers.PreTrainedModel.beam_sample` is
+        called. This is required to match :obj:`past_key_values` with the correct beam_idx at every generation step.
+        """
+        return tuple(
+            tuple(
+                past_state.index_select(0, beam_idx.to(past_state.device))
+                for past_state in layer_past
+            )
+            for layer_past in past
+        )
+
+
+class ConditioningEncoder(nn.Module):
+    def __init__(self,
+                 spec_dim,
+                 embedding_dim,
+                 attn_blocks=6,
+                 num_attn_heads=4,
+                 do_checkpointing=False,
+                 mean=False):
+        super().__init__()
+        attn = []
+        self.init = nn.Conv1d(spec_dim, embedding_dim, kernel_size=1)
+        for a in range(attn_blocks):
+            attn.append(AttentionBlock(embedding_dim, num_attn_heads))
+        self.attn = nn.Sequential(*attn)
+        self.dim = embedding_dim
+        self.do_checkpointing = do_checkpointing
+        self.mean = mean
+
+    def forward(self, x):
+        h = self.init(x)
+        h = self.attn(h)
+        if self.mean:
+            return h.mean(dim=2)
+        else:
+            return h[:, :, 0]
+
+
+class LearnedPositionEmbeddings(nn.Module):
+    def __init__(self, seq_len, model_dim, init=.02):
+        super().__init__()
+        self.emb = nn.Embedding(seq_len, model_dim)
+        # Initializing this way is standard for GPT-2
+        self.emb.weight.data.normal_(mean=0.0, std=init)
+
+    def forward(self, x):
+        sl = x.shape[1]
+        return self.emb(torch.arange(0, sl, device=x.device))
+
+    def get_fixed_embedding(self, ind, dev):
+        return self.emb(torch.tensor([ind], device=dev)).unsqueeze(0)
+
+
+def build_hf_gpt_transformer(layers, model_dim, heads, max_mel_seq_len, max_text_seq_len, checkpointing):
+    """
+    GPT-2 implemented by the HuggingFace library.
+    """
+    from transformers import GPT2Config, GPT2Model
+    gpt_config = GPT2Config(vocab_size=256,  # Unused.
+                             n_positions=max_mel_seq_len+max_text_seq_len,
+                             n_ctx=max_mel_seq_len+max_text_seq_len,
+                             n_embd=model_dim,
+                             n_layer=layers,
+                             n_head=heads,
+                             gradient_checkpointing=checkpointing,
+                             use_cache=not checkpointing)
+    gpt = GPT2Model(gpt_config)
+    # Override the built in positional embeddings
+    del gpt.wpe
+    gpt.wpe = functools.partial(null_position_embeddings, dim=model_dim)
+    # Built-in token embeddings are unused.
+    del gpt.wte
+    return gpt, LearnedPositionEmbeddings(max_mel_seq_len, model_dim), LearnedPositionEmbeddings(max_text_seq_len, model_dim),\
+           None, None
+
+
+class MelEncoder(nn.Module):
+    def __init__(self, channels, mel_channels=80, resblocks_per_reduction=2):
+        super().__init__()
+        self.channels = channels
+        self.encoder = nn.Sequential(nn.Conv1d(mel_channels, channels//4, kernel_size=3, padding=1),
+                                     nn.Sequential(*[ResBlock(channels//4) for _ in range(resblocks_per_reduction)]),
+                                     nn.Conv1d(channels//4, channels//2, kernel_size=3, stride=2, padding=1),
+                                     nn.GroupNorm(channels//16, channels//2),
+                                     nn.ReLU(),
+                                     nn.Sequential(*[ResBlock(channels//2) for _ in range(resblocks_per_reduction)]),
+                                     nn.Conv1d(channels//2, channels, kernel_size=3, stride=2, padding=1),
+                                     nn.GroupNorm(channels//8, channels),
+                                     nn.ReLU(),
+                                     nn.Sequential(*[ResBlock(channels) for _ in range(resblocks_per_reduction)]),
+                                     )
+        self.reduction = 4
+
+
+    def forward(self, x):
+        for e in self.encoder:
+            x = e(x)
+        return x.permute(0,2,1)
+
+
+class UnifiedVoice(nn.Module):
+    def __init__(self, layers=8, model_dim=512, heads=8, max_text_tokens=120, max_mel_tokens=250, max_conditioning_inputs=1,
+                 mel_length_compression=1024, number_text_tokens=256,
+                 start_text_token=None, number_mel_codes=8194, start_mel_token=8192,
+                 stop_mel_token=8193, train_solo_embeddings=False, use_mel_codes_as_input=True,
+                 checkpointing=True, types=1):
+        """
+        Args:
+            layers: Number of layers in transformer stack.
+            model_dim: Operating dimensions of the transformer
+            heads: Number of transformer heads. Must be divisible by model_dim. Recommend model_dim//64
+            max_text_tokens: Maximum number of text tokens that will be encountered by model.
+            max_mel_tokens: Maximum number of MEL tokens that will be encountered by model.
+            max_conditioning_inputs: Maximum number of conditioning inputs provided to the model. If (1), conditioning input can be of format (b,80,s), otherwise (b,n,80,s).
+            mel_length_compression: The factor between <number_input_samples> and <mel_tokens>. Used to compute MEL code padding given wav input length.
+            number_text_tokens:
+            start_text_token:
+            stop_text_token:
+            number_mel_codes:
+            start_mel_token:
+            stop_mel_token:
+            train_solo_embeddings:
+            use_mel_codes_as_input:
+            checkpointing:
+        """
+        super().__init__()
+
+        self.number_text_tokens = number_text_tokens
+        self.start_text_token = number_text_tokens * types if start_text_token is None else start_text_token
+        self.stop_text_token = 0
+        self.number_mel_codes = number_mel_codes
+        self.start_mel_token = start_mel_token
+        self.stop_mel_token = stop_mel_token
+        self.layers = layers
+        self.heads = heads
+        self.max_mel_tokens = max_mel_tokens
+        self.max_text_tokens = max_text_tokens
+        self.model_dim = model_dim
+        self.max_conditioning_inputs = max_conditioning_inputs
+        self.mel_length_compression = mel_length_compression
+        self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads)
+        self.text_embedding = nn.Embedding(self.number_text_tokens*types+1, model_dim)
+        if use_mel_codes_as_input:
+            self.mel_embedding = nn.Embedding(self.number_mel_codes, model_dim)
+        else:
+            self.mel_embedding = MelEncoder(model_dim, resblocks_per_reduction=1)
+        self.gpt, self.mel_pos_embedding, self.text_pos_embedding, self.mel_layer_pos_embedding, self.text_layer_pos_embedding = \
+            build_hf_gpt_transformer(layers, model_dim, heads, self.max_mel_tokens+2+self.max_conditioning_inputs, self.max_text_tokens+2, checkpointing)
+        if train_solo_embeddings:
+            self.mel_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * .02, requires_grad=True)
+            self.text_solo_embedding = nn.Parameter(torch.randn(1, 1, model_dim) * .02, requires_grad=True)
+        else:
+            self.mel_solo_embedding = 0
+            self.text_solo_embedding = 0
+
+        self.final_norm = nn.LayerNorm(model_dim)
+        self.text_head = nn.Linear(model_dim, self.number_text_tokens*types+1)
+        self.mel_head = nn.Linear(model_dim, self.number_mel_codes)
+
+        # Initialize the embeddings per the GPT-2 scheme
+        embeddings = [self.text_embedding]
+        if use_mel_codes_as_input:
+            embeddings.append(self.mel_embedding)
+        for module in embeddings:
+            module.weight.data.normal_(mean=0.0, std=.02)
+    def post_init_gpt2_config(self, use_deepspeed=False, kv_cache=False, half=False):
+        seq_length = self.max_mel_tokens + self.max_text_tokens + 2
+        gpt_config = GPT2Config(
+            vocab_size=self.max_mel_tokens,
+            n_positions=seq_length,
+            n_ctx=seq_length,
+            n_embd=self.model_dim,
+            n_layer=self.layers,
+            n_head=self.heads,
+            gradient_checkpointing=False,
+            use_cache=True,
+        )
+        self.inference_model = GPT2InferenceModel(
+            gpt_config,
+            self.gpt,
+            self.mel_pos_embedding,
+            self.mel_embedding,
+            self.final_norm,
+            self.mel_head,
+            kv_cache=kv_cache,
+        )
+        if use_deepspeed and half and torch.cuda.is_available():
+            import deepspeed
+            self.ds_engine = deepspeed.init_inference(model=self.inference_model,  
+                                                    mp_size=1,
+                                                    replace_with_kernel_inject=True,
+                                                    dtype=torch.float16)
+            self.inference_model = self.ds_engine.module.eval()
+        elif use_deepspeed and torch.cuda.is_available():
+            import deepspeed
+            self.ds_engine = deepspeed.init_inference(model=self.inference_model,  
+                                                    mp_size=1,
+                                                    replace_with_kernel_inject=True,
+                                                    dtype=torch.float32)
+            self.inference_model = self.ds_engine.module.eval()
+        else:
+            self.inference_model = self.inference_model.eval()
+
+        # self.inference_model = PrunedGPT2InferenceModel(gpt_config, self.gpt, self.mel_pos_embedding, self.mel_embedding, self.final_norm, self.mel_head)
+        self.gpt.wte = self.mel_embedding
+    def build_aligned_inputs_and_targets(self, input, start_token, stop_token):
+        inp = F.pad(input, (1,0), value=start_token)
+        tar = F.pad(input, (0,1), value=stop_token)
+        return inp, tar
+
+    def set_mel_padding(self, mel_input_tokens, wav_lengths):
+        """
+        Given mel tokens that are derived from a padded audio clip and the actual lengths of each batch element in
+        that audio clip, reformats the tokens with STOP_MEL_TOKEN in place of the zero padding. This is required
+        preformatting to create a working TTS model.
+        """
+        # Set padding areas within MEL (currently it is coded with the MEL code for <zero>).
+        mel_lengths = torch.div(wav_lengths, self.mel_length_compression, rounding_mode='trunc')
+        for b in range(len(mel_lengths)):
+            actual_end = mel_lengths[b] + 1  # Due to the convolutional nature of how these tokens are generated, it would be best if the model predicts a token past the actual last token.
+            if actual_end < mel_input_tokens.shape[-1]:
+                mel_input_tokens[b, actual_end:] = self.stop_mel_token
+        return mel_input_tokens
+
+    def get_logits(self, speech_conditioning_inputs, first_inputs, first_head, second_inputs=None, second_head=None, get_attns=False, return_latent=False):
+        if second_inputs is not None:
+            emb = torch.cat([speech_conditioning_inputs, first_inputs, second_inputs], dim=1)
+        else:
+            emb = torch.cat([speech_conditioning_inputs, first_inputs], dim=1)
+
+        gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns)
+        if get_attns:
+            return gpt_out.attentions
+
+        enc = gpt_out.last_hidden_state[:, 1:]  # The first logit is tied to the speech_conditioning_input
+        enc = self.final_norm(enc)
+
+        if return_latent:
+            return enc[:, speech_conditioning_inputs.shape[1]:speech_conditioning_inputs.shape[1]+first_inputs.shape[1]], enc[:, -second_inputs.shape[1]:]
+
+        first_logits = enc[:, :first_inputs.shape[1]]
+        first_logits = first_head(first_logits)
+        first_logits = first_logits.permute(0,2,1)
+        if second_inputs is not None:
+            second_logits = enc[:, -second_inputs.shape[1]:]
+            second_logits = second_head(second_logits)
+            second_logits = second_logits.permute(0,2,1)
+            return first_logits, second_logits
+        else:
+            return first_logits
+
+    def get_conditioning(self, speech_conditioning_input):
+        speech_conditioning_input = speech_conditioning_input.unsqueeze(1) if len(
+            speech_conditioning_input.shape) == 3 else speech_conditioning_input
+        conds = []
+        for j in range(speech_conditioning_input.shape[1]):
+            conds.append(self.conditioning_encoder(speech_conditioning_input[:, j]))
+        conds = torch.stack(conds, dim=1)
+        conds = conds.mean(dim=1)
+        return conds
+
+    def forward(self, speech_conditioning_latent, text_inputs, text_lengths, mel_codes, wav_lengths, types=None, text_first=True, raw_mels=None, return_attentions=False,
+                return_latent=False, clip_inputs=True):
+        """
+        Forward pass that uses both text and voice in either text conditioning mode or voice conditioning mode
+        (actuated by `text_first`).
+
+        speech_conditioning_input: MEL float tensor, (b,1024)
+        text_inputs: long tensor, (b,t)
+        text_lengths: long tensor, (b,)
+        mel_inputs:  long tensor, (b,m)
+        wav_lengths: long tensor, (b,)
+        raw_mels: MEL float tensor (b,80,s)
+
+        If return_attentions is specified, only logits are returned.
+        If return_latent is specified, loss & logits are not computed or returned. Only the predicted latents are returned.
+        If clip_inputs is True, the inputs will be clipped to the smallest input size across each input modality.
+        """
+        # Types are expressed by expanding the text embedding space.
+        if types is not None:
+            text_inputs = text_inputs * (1+types).unsqueeze(-1)
+
+        if clip_inputs:
+            # This model will receive micro-batches with a ton of padding for both the text and MELs. Ameliorate this by
+            # chopping the inputs by the maximum actual length.
+            max_text_len = text_lengths.max()
+            text_inputs = text_inputs[:, :max_text_len]
+            max_mel_len = wav_lengths.max() // self.mel_length_compression
+            mel_codes = mel_codes[:, :max_mel_len]
+            if raw_mels is not None:
+                raw_mels = raw_mels[:, :, :max_mel_len*4]
+        mel_codes = self.set_mel_padding(mel_codes, wav_lengths)
+        text_inputs = F.pad(text_inputs, (0,1), value=self.stop_text_token)
+        mel_codes = F.pad(mel_codes, (0,1), value=self.stop_mel_token)
+
+        conds = speech_conditioning_latent.unsqueeze(1)
+        text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
+        text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs)
+        mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token)
+        if raw_mels is not None:
+            mel_inp = F.pad(raw_mels, (0, 8))
+        else:
+            mel_inp = mel_codes
+        mel_emb = self.mel_embedding(mel_inp)
+        mel_emb = mel_emb + self.mel_pos_embedding(mel_codes)
+
+        if text_first:
+            text_logits, mel_logits = self.get_logits(conds, text_emb, self.text_head, mel_emb, self.mel_head, get_attns=return_attentions, return_latent=return_latent)
+            if return_latent:
+                return mel_logits[:, :-2]  # Despite the name, these are not logits. Strip off the two tokens added by this forward pass.
+        else:
+            mel_logits, text_logits = self.get_logits(conds, mel_emb, self.mel_head, text_emb, self.text_head, get_attns=return_attentions, return_latent=return_latent)
+            if return_latent:
+                return text_logits[:, :-2]  # Despite the name, these are not logits. Strip off the two tokens added by this forward pass.
+
+        if return_attentions:
+            return mel_logits
+        loss_text = F.cross_entropy(text_logits, text_targets.long())
+        loss_mel = F.cross_entropy(mel_logits, mel_targets.long())
+        return loss_text.mean(), loss_mel.mean(), mel_logits
+    def compute_embeddings(
+        self,
+        cond_latents,
+        text_inputs,
+    ):
+        text_inputs = F.pad(text_inputs, (0, 1), value=self.stop_text_token)
+        text_inputs = F.pad(text_inputs, (1, 0), value=self.start_text_token)
+        emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs)
+        conds = cond_latents.unsqueeze(1)
+        emb = torch.cat([conds, emb], dim=1)
+        self.inference_model.store_mel_emb(emb)
+        gpt_inputs = torch.full(
+            (
+                emb.shape[0],
+                emb.shape[1] + 1,  # +1 for the start_mel_token
+            ),
+            fill_value=1,
+            dtype=torch.long,
+            device=text_inputs.device,
+        )
+        gpt_inputs[:, -1] = self.start_mel_token
+        return gpt_inputs
+    def inference_speech(self, speech_conditioning_latent, text_inputs, input_tokens=None, num_return_sequences=1,
+                         max_generate_length=None, typical_sampling=False, typical_mass=.9, **hf_generate_kwargs):        
+
+        text_inputs = F.pad(text_inputs, (0, 1), value=self.stop_text_token)
+        text_inputs, _ = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token)
+        text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs)
+
+        conds = speech_conditioning_latent.unsqueeze(1)
+        emb = torch.cat([conds, text_emb], dim=1)
+        self.inference_model.store_mel_emb(emb)
+
+        fake_inputs = torch.full((emb.shape[0], conds.shape[1] + emb.shape[1],), fill_value=1, dtype=torch.long,
+                                 device=text_inputs.device)
+        fake_inputs[:, -1] = self.start_mel_token
+        trunc_index = fake_inputs.shape[1]
+        if input_tokens is None:
+            inputs = fake_inputs
+        else:
+            assert num_return_sequences % input_tokens.shape[0] == 0, "The number of return sequences must be divisible by the number of input sequences"
+            fake_inputs = fake_inputs.repeat(num_return_sequences, 1)
+            input_tokens = input_tokens.repeat(num_return_sequences // input_tokens.shape[0], 1)
+            inputs = torch.cat([fake_inputs, input_tokens], dim=1)
+
+        logits_processor = LogitsProcessorList([TypicalLogitsWarper(mass=typical_mass)]) if typical_sampling else LogitsProcessorList()
+        max_length = trunc_index + self.max_mel_tokens - 1  if max_generate_length is None else trunc_index + max_generate_length
+        gen = self.inference_model.generate(inputs, bos_token_id=self.start_mel_token, pad_token_id=self.stop_mel_token, eos_token_id=self.stop_mel_token,
+                                            max_length=max_length, logits_processor=logits_processor,
+                                            num_return_sequences=num_return_sequences, **hf_generate_kwargs)
+        return gen[:, trunc_index:]
+
+    def get_generator(self, fake_inputs, **hf_generate_kwargs):
+        return self.inference_model.generate_stream(
+            fake_inputs,
+            bos_token_id=self.start_mel_token,
+            pad_token_id=self.stop_mel_token,
+            eos_token_id=self.stop_mel_token,
+            max_length=500,
+            do_stream=True,
+            **hf_generate_kwargs,
+        )
+if __name__ == '__main__':
+    gpt = UnifiedVoice(model_dim=256, heads=4, train_solo_embeddings=True, use_mel_codes_as_input=True, max_conditioning_inputs=4)
+    l = gpt(torch.randn(2, 3, 80, 800),
+            torch.randint(high=120, size=(2,120)),
+            torch.tensor([32, 120]),
+            torch.randint(high=8192, size=(2,250)),
+            torch.tensor([250*256,195*256]))
+    gpt.text_forward(torch.randn(2,80,800), torch.randint(high=50, size=(2,80)), torch.tensor([32, 80]))
diff --git a/tortoise/tortoise/models/classifier.py b/tortoise/tortoise/models/classifier.py
new file mode 100644
index 0000000..f92d99e
--- /dev/null
+++ b/tortoise/tortoise/models/classifier.py
@@ -0,0 +1,148 @@
+import torch
+import torch.nn as nn
+
+from tortoise.models.arch_util import Upsample, Downsample, normalization, zero_module, AttentionBlock
+
+
+class ResBlock(nn.Module):
+    def __init__(
+        self,
+        channels,
+        dropout,
+        out_channels=None,
+        use_conv=False,
+        use_scale_shift_norm=False,
+        dims=2,
+        up=False,
+        down=False,
+        kernel_size=3,
+        do_checkpoint=True,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_scale_shift_norm = use_scale_shift_norm
+        self.do_checkpoint = do_checkpoint
+        padding = 1 if kernel_size == 3 else 2
+
+        self.in_layers = nn.Sequential(
+            normalization(channels),
+            nn.SiLU(),
+            nn.Conv1d(channels, self.out_channels, kernel_size, padding=padding),
+        )
+
+        self.updown = up or down
+
+        if up:
+            self.h_upd = Upsample(channels, False, dims)
+            self.x_upd = Upsample(channels, False, dims)
+        elif down:
+            self.h_upd = Downsample(channels, False, dims)
+            self.x_upd = Downsample(channels, False, dims)
+        else:
+            self.h_upd = self.x_upd = nn.Identity()
+
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels),
+            nn.SiLU(),
+            nn.Dropout(p=dropout),
+            zero_module(
+                nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding)
+            ),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        elif use_conv:
+            self.skip_connection = nn.Conv1d(
+                dims, channels, self.out_channels, kernel_size, padding=padding
+            )
+        else:
+            self.skip_connection = nn.Conv1d(dims, channels, self.out_channels, 1)
+
+    def forward(self, x):
+        if self.updown:
+            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+            h = in_rest(x)
+            h = self.h_upd(h)
+            x = self.x_upd(x)
+            h = in_conv(h)
+        else:
+            h = self.in_layers(x)
+        h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class AudioMiniEncoder(nn.Module):
+    def __init__(self,
+                 spec_dim,
+                 embedding_dim,
+                 base_channels=128,
+                 depth=2,
+                 resnet_blocks=2,
+                 attn_blocks=4,
+                 num_attn_heads=4,
+                 dropout=0,
+                 downsample_factor=2,
+                 kernel_size=3):
+        super().__init__()
+        self.init = nn.Sequential(
+            nn.Conv1d(spec_dim, base_channels, 3, padding=1)
+        )
+        ch = base_channels
+        res = []
+        self.layers = depth
+        for l in range(depth):
+            for r in range(resnet_blocks):
+                res.append(ResBlock(ch, dropout, do_checkpoint=False, kernel_size=kernel_size))
+            res.append(Downsample(ch, use_conv=True, out_channels=ch*2, factor=downsample_factor))
+            ch *= 2
+        self.res = nn.Sequential(*res)
+        self.final = nn.Sequential(
+            normalization(ch),
+            nn.SiLU(),
+            nn.Conv1d(ch, embedding_dim, 1)
+        )
+        attn = []
+        for a in range(attn_blocks):
+            attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=False))
+        self.attn = nn.Sequential(*attn)
+        self.dim = embedding_dim
+
+    def forward(self, x):
+        h = self.init(x)
+        h = self.res(h)
+        h = self.final(h)
+        for blk in self.attn:
+            h = blk(h)
+        return h[:, :, 0]
+
+
+class AudioMiniEncoderWithClassifierHead(nn.Module):
+    def __init__(self, classes, distribute_zero_label=True, **kwargs):
+        super().__init__()
+        self.enc = AudioMiniEncoder(**kwargs)
+        self.head = nn.Linear(self.enc.dim, classes)
+        self.num_classes = classes
+        self.distribute_zero_label = distribute_zero_label
+
+    def forward(self, x, labels=None):
+        h = self.enc(x)
+        logits = self.head(h)
+        if labels is None:
+            return logits
+        else:
+            if self.distribute_zero_label:
+                oh_labels = nn.functional.one_hot(labels, num_classes=self.num_classes)
+                zeros_indices = (labels == 0).unsqueeze(-1)
+                # Distribute 20% of the probability mass on all classes when zero is specified, to compensate for dataset noise.
+                zero_extra_mass = torch.full_like(oh_labels, dtype=torch.float, fill_value=.2/(self.num_classes-1))
+                zero_extra_mass[:, 0] = -.2
+                zero_extra_mass = zero_extra_mass * zeros_indices
+                oh_labels = oh_labels + zero_extra_mass
+            else:
+                oh_labels = labels
+            loss = nn.functional.cross_entropy(logits, oh_labels)
+            return loss
diff --git a/tortoise/tortoise/models/clvp.py b/tortoise/tortoise/models/clvp.py
new file mode 100644
index 0000000..00f5011
--- /dev/null
+++ b/tortoise/tortoise/models/clvp.py
@@ -0,0 +1,155 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import einsum
+
+from tortoise.models.arch_util import CheckpointedXTransformerEncoder
+from tortoise.models.transformer import Transformer
+from tortoise.models.xtransformers import Encoder
+
+
+def exists(val):
+    return val is not None
+
+
+def masked_mean(t, mask, dim = 1):
+    t = t.masked_fill(~mask[:, :, None], 0.)
+    return t.sum(dim = 1) / mask.sum(dim = 1)[..., None]
+
+class CLVP(nn.Module):
+    """
+    CLIP model retrofitted for performing contrastive evaluation between tokenized audio data and the corresponding
+    transcribed text.
+
+    Originally from https://github.com/lucidrains/DALLE-pytorch/blob/main/dalle_pytorch/dalle_pytorch.py
+    """
+
+    def __init__(
+            self,
+            *,
+            dim_text=512,
+            dim_speech=512,
+            dim_latent=512,
+            num_text_tokens=256,
+            text_enc_depth=6,
+            text_seq_len=120,
+            text_heads=8,
+            num_speech_tokens=8192,
+            speech_enc_depth=6,
+            speech_heads=8,
+            speech_seq_len=250,
+            text_mask_percentage=0,
+            voice_mask_percentage=0,
+            wav_token_compression=1024,
+            use_xformers=False,
+    ):
+        super().__init__()
+        self.text_emb = nn.Embedding(num_text_tokens, dim_text)
+        self.to_text_latent = nn.Linear(dim_text, dim_latent, bias=False)
+
+        self.speech_emb = nn.Embedding(num_speech_tokens, dim_speech)
+        self.to_speech_latent = nn.Linear(dim_speech, dim_latent, bias=False)
+
+        if use_xformers:
+            self.text_transformer = CheckpointedXTransformerEncoder(
+                needs_permute=False,
+                exit_permute=False,
+                max_seq_len=-1,
+                attn_layers=Encoder(
+                    dim=dim_text,
+                    depth=text_enc_depth,
+                    heads=text_heads,
+                    ff_dropout=.1,
+                    ff_mult=2,
+                    attn_dropout=.1,
+                    use_rmsnorm=True,
+                    ff_glu=True,
+                    rotary_pos_emb=True,
+                ))
+            self.speech_transformer = CheckpointedXTransformerEncoder(
+                needs_permute=False,
+                exit_permute=False,
+                max_seq_len=-1,
+                attn_layers=Encoder(
+                    dim=dim_speech,
+                    depth=speech_enc_depth,
+                    heads=speech_heads,
+                    ff_dropout=.1,
+                    ff_mult=2,
+                    attn_dropout=.1,
+                    use_rmsnorm=True,
+                    ff_glu=True,
+                    rotary_pos_emb=True,
+                ))
+        else:
+            self.text_transformer = Transformer(causal=False, seq_len=text_seq_len, dim=dim_text, depth=text_enc_depth,
+                                                heads=text_heads)
+            self.speech_transformer = Transformer(causal=False, seq_len=speech_seq_len, dim=dim_speech,
+                                                  depth=speech_enc_depth, heads=speech_heads)
+
+        self.temperature = nn.Parameter(torch.tensor(1.))
+        self.text_mask_percentage = text_mask_percentage
+        self.voice_mask_percentage = voice_mask_percentage
+        self.wav_token_compression = wav_token_compression
+        self.xformers = use_xformers
+        if not use_xformers:
+            self.text_pos_emb = nn.Embedding(text_seq_len, dim_text)
+            self.speech_pos_emb = nn.Embedding(num_speech_tokens, dim_speech)
+
+    def forward(
+            self,
+            text,
+            speech_tokens,
+            return_loss=False
+    ):
+        b, device = text.shape[0], text.device
+        if self.training:
+            text_mask = torch.rand_like(text.float()) > self.text_mask_percentage
+            voice_mask = torch.rand_like(speech_tokens.float()) > self.voice_mask_percentage
+        else:
+            text_mask = torch.ones_like(text.float()).bool()
+            voice_mask = torch.ones_like(speech_tokens.float()).bool()
+
+        text_emb = self.text_emb(text)
+        speech_emb = self.speech_emb(speech_tokens)
+
+        if not self.xformers:
+            text_emb += self.text_pos_emb(torch.arange(text.shape[1], device=device))
+            speech_emb += self.speech_pos_emb(torch.arange(speech_emb.shape[1], device=device))
+
+        enc_text = self.text_transformer(text_emb, mask=text_mask)
+        enc_speech = self.speech_transformer(speech_emb, mask=voice_mask)
+
+        text_latents = masked_mean(enc_text, text_mask, dim=1)
+        speech_latents = masked_mean(enc_speech, voice_mask, dim=1)
+
+        text_latents = self.to_text_latent(text_latents)
+        speech_latents = self.to_speech_latent(speech_latents)
+
+        text_latents, speech_latents = map(lambda t: F.normalize(t, p=2, dim=-1), (text_latents, speech_latents))
+
+        temp = self.temperature.exp()
+
+        if not return_loss:
+            sim = einsum('n d, n d -> n', text_latents, speech_latents) * temp
+            return sim
+
+        sim = einsum('i d, j d -> i j', text_latents, speech_latents) * temp
+        labels = torch.arange(b, device=device)
+        loss = (F.cross_entropy(sim, labels) + F.cross_entropy(sim.t(), labels)) / 2
+        return loss
+
+
+if __name__ == '__main__':
+    clip = CLVP(text_mask_percentage=.2, voice_mask_percentage=.2)
+    clip(torch.randint(0,256,(2,120)),
+         torch.tensor([50,100]),
+         torch.randint(0,8192,(2,250)),
+         torch.tensor([101,102]),
+         return_loss=True)
+    nonloss = clip(torch.randint(0,256,(2,120)),
+         torch.tensor([50,100]),
+         torch.randint(0,8192,(2,250)),
+         torch.tensor([101,102]),
+         return_loss=False)
+    print(nonloss.shape)
\ No newline at end of file
diff --git a/tortoise/tortoise/models/cvvp.py b/tortoise/tortoise/models/cvvp.py
new file mode 100644
index 0000000..544ca47
--- /dev/null
+++ b/tortoise/tortoise/models/cvvp.py
@@ -0,0 +1,142 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import einsum
+
+from tortoise.models.arch_util import AttentionBlock
+from tortoise.models.xtransformers import ContinuousTransformerWrapper, Encoder
+
+
+def exists(val):
+    return val is not None
+
+
+def masked_mean(t, mask):
+    t = t.masked_fill(~mask, 0.)
+    return t.sum(dim=1) / mask.sum(dim=1)
+
+
+class CollapsingTransformer(nn.Module):
+    def __init__(self, model_dim, output_dims, heads, dropout, depth, mask_percentage=0, **encoder_kwargs):
+        super().__init__()
+        self.transformer = ContinuousTransformerWrapper(
+            max_seq_len=-1,
+            use_pos_emb=False,
+            attn_layers=Encoder(
+                dim=model_dim,
+                depth=depth,
+                heads=heads,
+                ff_dropout=dropout,
+                ff_mult=1,
+                attn_dropout=dropout,
+                use_rmsnorm=True,
+                ff_glu=True,
+                rotary_pos_emb=True,
+                **encoder_kwargs,
+            ))
+        self.pre_combiner = nn.Sequential(nn.Conv1d(model_dim, output_dims, 1),
+                                          AttentionBlock(
+            output_dims, num_heads=heads, do_checkpoint=False),
+            nn.Conv1d(output_dims, output_dims, 1))
+        self.mask_percentage = mask_percentage
+
+    def forward(self, x, **transformer_kwargs):
+        h = self.transformer(x, **transformer_kwargs)
+        h = h.permute(0, 2, 1)
+        h = self.pre_combiner(h).permute(0, 2, 1)
+        if self.training:
+            mask = torch.rand_like(h.float()) > self.mask_percentage
+        else:
+            mask = torch.ones_like(h.float()).bool()
+        return masked_mean(h, mask)
+
+
+class ConvFormatEmbedding(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super().__init__()
+        self.emb = nn.Embedding(*args, **kwargs)
+
+    def forward(self, x):
+        y = self.emb(x)
+        return y.permute(0, 2, 1)
+
+
+class CVVP(nn.Module):
+    def __init__(
+            self,
+            model_dim=512,
+            transformer_heads=8,
+            dropout=.1,
+            conditioning_enc_depth=8,
+            cond_mask_percentage=0,
+            mel_channels=80,
+            mel_codes=None,
+            speech_enc_depth=8,
+            speech_mask_percentage=0,
+            latent_multiplier=1,
+    ):
+        super().__init__()
+        latent_dim = latent_multiplier*model_dim
+        self.temperature = nn.Parameter(torch.tensor(1.))
+
+        self.cond_emb = nn.Sequential(nn.Conv1d(mel_channels, model_dim//2, kernel_size=5, stride=2, padding=2),
+                                      nn.Conv1d(model_dim//2, model_dim, kernel_size=3, stride=2, padding=1))
+        self.conditioning_transformer = CollapsingTransformer(
+            model_dim, model_dim, transformer_heads, dropout, conditioning_enc_depth, cond_mask_percentage)
+        self.to_conditioning_latent = nn.Linear(
+            latent_dim, latent_dim, bias=False)
+
+        if mel_codes is None:
+            self.speech_emb = nn.Conv1d(
+                mel_channels, model_dim, kernel_size=5, padding=2)
+        else:
+            self.speech_emb = ConvFormatEmbedding(mel_codes, model_dim)
+        self.speech_transformer = CollapsingTransformer(
+            model_dim, latent_dim, transformer_heads, dropout, speech_enc_depth, speech_mask_percentage)
+        self.to_speech_latent = nn.Linear(
+            latent_dim, latent_dim, bias=False)
+
+    def get_grad_norm_parameter_groups(self):
+        return {
+            'conditioning': list(self.conditioning_transformer.parameters()),
+            'speech': list(self.speech_transformer.parameters()),
+        }
+
+    def forward(
+            self,
+            mel_cond,
+            mel_input,
+            return_loss=False
+    ):
+        cond_emb = self.cond_emb(mel_cond).permute(0, 2, 1)
+        enc_cond = self.conditioning_transformer(cond_emb)
+        cond_latents = self.to_conditioning_latent(enc_cond)
+
+        speech_emb = self.speech_emb(mel_input).permute(0, 2, 1)
+        enc_speech = self.speech_transformer(speech_emb)
+        speech_latents = self.to_speech_latent(enc_speech)
+
+        cond_latents, speech_latents = map(lambda t: F.normalize(
+            t, p=2, dim=-1), (cond_latents, speech_latents))
+        temp = self.temperature.exp()
+
+        if not return_loss:
+            sim = einsum('n d, n d -> n', cond_latents,
+                         speech_latents) * temp
+            return sim
+
+        sim = einsum('i d, j d -> i j', cond_latents,
+                     speech_latents) * temp
+        labels = torch.arange(
+            cond_latents.shape[0], device=mel_input.device)
+        loss = (F.cross_entropy(sim, labels) +
+                F.cross_entropy(sim.t(), labels)) / 2
+
+        return loss
+
+
+if __name__ == '__main__':
+    clvp = CVVP()
+    clvp(torch.randn(2, 80, 100),
+         torch.randn(2, 80, 95),
+         return_loss=True)
diff --git a/tortoise/tortoise/models/diffusion_decoder.py b/tortoise/tortoise/models/diffusion_decoder.py
new file mode 100644
index 0000000..e969129
--- /dev/null
+++ b/tortoise/tortoise/models/diffusion_decoder.py
@@ -0,0 +1,336 @@
+import math
+import random
+from abc import abstractmethod
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import autocast
+
+from tortoise.models.arch_util import normalization, AttentionBlock
+
+
+def is_latent(t):
+    return t.dtype == torch.float
+
+
+def is_sequence(t):
+    return t.dtype == torch.long
+
+
+def timestep_embedding(timesteps, dim, max_period=10000):
+    """
+    Create sinusoidal timestep embeddings.
+
+    :param timesteps: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an [N x dim] Tensor of positional embeddings.
+    """
+    half = dim // 2
+    freqs = torch.exp(
+        -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+    ).to(device=timesteps.device)
+    args = timesteps[:, None].float() * freqs[None]
+    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+    if dim % 2:
+        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+    return embedding
+
+
+class TimestepBlock(nn.Module):
+    @abstractmethod
+    def forward(self, x, emb):
+        """
+        Apply the module to `x` given `emb` timestep embeddings.
+        """
+
+
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+    def forward(self, x, emb):
+        for layer in self:
+            if isinstance(layer, TimestepBlock):
+                x = layer(x, emb)
+            else:
+                x = layer(x)
+        return x
+
+
+class ResBlock(TimestepBlock):
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        dims=2,
+        kernel_size=3,
+        efficient_config=True,
+        use_scale_shift_norm=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_scale_shift_norm = use_scale_shift_norm
+        padding = {1: 0, 3: 1, 5: 2}[kernel_size]
+        eff_kernel = 1 if efficient_config else 3
+        eff_padding = 0 if efficient_config else 1
+
+        self.in_layers = nn.Sequential(
+            normalization(channels),
+            nn.SiLU(),
+            nn.Conv1d(channels, self.out_channels, eff_kernel, padding=eff_padding),
+        )
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(
+                emb_channels,
+                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+            ),
+        )
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels),
+            nn.SiLU(),
+            nn.Dropout(p=dropout),
+                nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        else:
+            self.skip_connection = nn.Conv1d(channels, self.out_channels, eff_kernel, padding=eff_padding)
+
+    def forward(self, x, emb):
+        h = self.in_layers(x)
+        emb_out = self.emb_layers(emb).type(h.dtype)
+        while len(emb_out.shape) < len(h.shape):
+            emb_out = emb_out[..., None]
+        if self.use_scale_shift_norm:
+            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+            scale, shift = torch.chunk(emb_out, 2, dim=1)
+            h = out_norm(h) * (1 + scale) + shift
+            h = out_rest(h)
+        else:
+            h = h + emb_out
+            h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class DiffusionLayer(TimestepBlock):
+    def __init__(self, model_channels, dropout, num_heads):
+        super().__init__()
+        self.resblk = ResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True)
+        self.attn = AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True)
+
+    def forward(self, x, time_emb):
+        y = self.resblk(x, time_emb)
+        return self.attn(y)
+
+
+class DiffusionTts(nn.Module):
+    def __init__(
+            self,
+            model_channels=512,
+            num_layers=8,
+            in_channels=100,
+            in_latent_channels=512,
+            in_tokens=8193,
+            out_channels=200,  # mean and variance
+            dropout=0,
+            use_fp16=False,
+            num_heads=16,
+            # Parameters for regularization.
+            layer_drop=.1,
+            unconditioned_percentage=.1,  # This implements a mechanism similar to what is used in classifier-free training.
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.dropout = dropout
+        self.num_heads = num_heads
+        self.unconditioned_percentage = unconditioned_percentage
+        self.enable_fp16 = use_fp16
+        self.layer_drop = layer_drop
+
+        self.inp_block = nn.Conv1d(in_channels, model_channels, 3, 1, 1)
+        self.time_embed = nn.Sequential(
+            nn.Linear(model_channels, model_channels),
+            nn.SiLU(),
+            nn.Linear(model_channels, model_channels),
+        )
+
+        # Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
+        # This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
+        # complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
+        # transformer network.
+        self.code_embedding = nn.Embedding(in_tokens, model_channels)
+        self.code_converter = nn.Sequential(
+            AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
+            AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
+            AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
+        )
+        self.code_norm = normalization(model_channels)
+        self.latent_conditioner = nn.Sequential(
+            nn.Conv1d(in_latent_channels, model_channels, 3, padding=1),
+            AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
+            AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
+            AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
+            AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True),
+        )
+        self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2),
+                                                 nn.Conv1d(model_channels, model_channels*2,3,padding=1,stride=2),
+                                                 AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
+                                                 AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
+                                                 AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
+                                                 AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False),
+                                                 AttentionBlock(model_channels*2, num_heads, relative_pos_embeddings=True, do_checkpoint=False))
+        self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1))
+        self.conditioning_timestep_integrator = TimestepEmbedSequential(
+            DiffusionLayer(model_channels, dropout, num_heads),
+            DiffusionLayer(model_channels, dropout, num_heads),
+            DiffusionLayer(model_channels, dropout, num_heads),
+        )
+
+        self.integrating_conv = nn.Conv1d(model_channels*2, model_channels, kernel_size=1)
+        self.mel_head = nn.Conv1d(model_channels, in_channels, kernel_size=3, padding=1)
+
+        self.layers = nn.ModuleList([DiffusionLayer(model_channels, dropout, num_heads) for _ in range(num_layers)] +
+                                    [ResBlock(model_channels, model_channels, dropout, dims=1, use_scale_shift_norm=True) for _ in range(3)])
+
+        self.out = nn.Sequential(
+            normalization(model_channels),
+            nn.SiLU(),
+            nn.Conv1d(model_channels, out_channels, 3, padding=1),
+        )
+
+    def get_grad_norm_parameter_groups(self):
+        groups = {
+            'minicoder': list(self.contextual_embedder.parameters()),
+            'layers': list(self.layers.parameters()),
+            'code_converters': list(self.code_embedding.parameters()) + list(self.code_converter.parameters()) + list(self.latent_conditioner.parameters()) + list(self.latent_conditioner.parameters()),
+            'timestep_integrator': list(self.conditioning_timestep_integrator.parameters()) + list(self.integrating_conv.parameters()),
+            'time_embed': list(self.time_embed.parameters()),
+        }
+        return groups
+
+    def get_conditioning(self, conditioning_input):
+        speech_conditioning_input = conditioning_input.unsqueeze(1) if len(
+            conditioning_input.shape) == 3 else conditioning_input
+        conds = []
+        for j in range(speech_conditioning_input.shape[1]):
+            conds.append(self.contextual_embedder(speech_conditioning_input[:, j]))
+        conds = torch.cat(conds, dim=-1)
+        conds = conds.mean(dim=-1)
+        return conds
+
+    def timestep_independent(self, aligned_conditioning, conditioning_latent, expected_seq_len, return_code_pred):
+        # Shuffle aligned_latent to BxCxS format
+        if is_latent(aligned_conditioning):
+            aligned_conditioning = aligned_conditioning.permute(0, 2, 1)
+
+        cond_scale, cond_shift = torch.chunk(conditioning_latent, 2, dim=1)
+        if is_latent(aligned_conditioning):
+            code_emb = self.latent_conditioner(aligned_conditioning)
+        else:
+            code_emb = self.code_embedding(aligned_conditioning).permute(0, 2, 1)
+            code_emb = self.code_converter(code_emb)
+        code_emb = self.code_norm(code_emb) * (1 + cond_scale.unsqueeze(-1)) + cond_shift.unsqueeze(-1)
+
+        unconditioned_batches = torch.zeros((code_emb.shape[0], 1, 1), device=code_emb.device)
+        # Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
+        if self.training and self.unconditioned_percentage > 0:
+            unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
+                                               device=code_emb.device) < self.unconditioned_percentage
+            code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(aligned_conditioning.shape[0], 1, 1),
+                                   code_emb)
+        expanded_code_emb = F.interpolate(code_emb, size=expected_seq_len, mode='nearest')
+
+        if not return_code_pred:
+            return expanded_code_emb
+        else:
+            mel_pred = self.mel_head(expanded_code_emb)
+            # Multiply mel_pred by !unconditioned_branches, which drops the gradient on unconditioned branches. This is because we don't want that gradient being used to train parameters through the codes_embedder as it unbalances contributions to that network from the MSE loss.
+            mel_pred = mel_pred * unconditioned_batches.logical_not()
+            return expanded_code_emb, mel_pred
+
+    def forward(self, x, timesteps, aligned_conditioning=None, conditioning_latent=None, precomputed_aligned_embeddings=None, conditioning_free=False, return_code_pred=False):
+        """
+        Apply the model to an input batch.
+
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :param aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced.
+        :param conditioning_latent: a pre-computed conditioning latent; see get_conditioning().
+        :param precomputed_aligned_embeddings: Embeddings returned from self.timestep_independent()
+        :param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        assert precomputed_aligned_embeddings is not None or (aligned_conditioning is not None and conditioning_latent is not None)
+        assert not (return_code_pred and precomputed_aligned_embeddings is not None)  # These two are mutually exclusive.
+
+        unused_params = []
+        if conditioning_free:
+            code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
+            unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
+            unused_params.extend(list(self.latent_conditioner.parameters()))
+        else:
+            if precomputed_aligned_embeddings is not None:
+                code_emb = precomputed_aligned_embeddings
+            else:
+                code_emb, mel_pred = self.timestep_independent(aligned_conditioning, conditioning_latent, x.shape[-1], True)
+                if is_latent(aligned_conditioning):
+                    unused_params.extend(list(self.code_converter.parameters()) + list(self.code_embedding.parameters()))
+                else:
+                    unused_params.extend(list(self.latent_conditioner.parameters()))
+
+            unused_params.append(self.unconditioned_embedding)
+
+        time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+        code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
+        x = self.inp_block(x)
+        x = torch.cat([x, code_emb], dim=1)
+        x = self.integrating_conv(x)
+        for i, lyr in enumerate(self.layers):
+            # Do layer drop where applicable. Do not drop first and last layers.
+            if self.training and self.layer_drop > 0 and i != 0 and i != (len(self.layers)-1) and random.random() < self.layer_drop:
+                unused_params.extend(list(lyr.parameters()))
+            else:
+                # First and last blocks will have autocast disabled for improved precision.
+                if not torch.backends.mps.is_available():
+                    with autocast(x.device.type, enabled=self.enable_fp16 and i != 0):
+                        x = lyr(x, time_emb)
+                else:
+                    x = lyr(x, time_emb)
+
+        x = x.float()
+        out = self.out(x)
+
+        # Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
+        extraneous_addition = 0
+        for p in unused_params:
+            extraneous_addition = extraneous_addition + p.mean()
+        out = out + extraneous_addition * 0
+
+        if return_code_pred:
+            return out, mel_pred
+        return out
+
+
+if __name__ == '__main__':
+    clip = torch.randn(2, 100, 400)
+    aligned_latent = torch.randn(2,388,512)
+    aligned_sequence = torch.randint(0,8192,(2,100))
+    cond = torch.randn(2, 100, 400)
+    ts = torch.LongTensor([600, 600])
+    model = DiffusionTts(512, layer_drop=.3, unconditioned_percentage=.5)
+    # Test with latent aligned conditioning
+    #o = model(clip, ts, aligned_latent, cond)
+    # Test with sequence aligned conditioning
+    o = model(clip, ts, aligned_sequence, cond)
+
diff --git a/tortoise/tortoise/models/hifigan_decoder.py b/tortoise/tortoise/models/hifigan_decoder.py
new file mode 100644
index 0000000..ae2f627
--- /dev/null
+++ b/tortoise/tortoise/models/hifigan_decoder.py
@@ -0,0 +1,303 @@
+# adopted from https://github.com/jik876/hifi-gan/blob/master/models.py
+import torch
+from torch import nn
+from torch.nn import Conv1d, ConvTranspose1d
+from torch.nn import functional as F
+from torch.nn.utils import remove_weight_norm, weight_norm
+
+LRELU_SLOPE = 0.1
+
+
+def get_padding(k, d):
+    return int((k * d - d) / 2)
+
+
+class ResBlock1(torch.nn.Module):
+    """Residual Block Type 1. It has 3 convolutional layers in each convolutional block.
+
+    Network::
+
+        x -> lrelu -> conv1_1 -> conv1_2 -> conv1_3 -> z -> lrelu -> conv2_1 -> conv2_2 -> conv2_3 -> o -> + -> o
+        |--------------------------------------------------------------------------------------------------|
+
+
+    Args:
+        channels (int): number of hidden channels for the convolutional layers.
+        kernel_size (int): size of the convolution filter in each layer.
+        dilations (list): list of dilation value for each conv layer in a block.
+    """
+
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
+        super().__init__()
+        self.convs1 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[2],
+                        padding=get_padding(kernel_size, dilation[2]),
+                    )
+                ),
+            ]
+        )
+
+        self.convs2 = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
+                ),
+                weight_norm(
+                    Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
+                ),
+                weight_norm(
+                    Conv1d(channels, channels, kernel_size, 1, dilation=1, padding=get_padding(kernel_size, 1))
+                ),
+            ]
+        )
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor): input tensor.
+        Returns:
+            Tensor: output tensor.
+        Shapes:
+            x: [B, C, T]
+        """
+        for c1, c2 in zip(self.convs1, self.convs2):
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            xt = c1(xt)
+            xt = F.leaky_relu(xt, LRELU_SLOPE)
+            xt = c2(xt)
+            x = xt + x
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs1:
+            remove_weight_norm(l)
+        for l in self.convs2:
+            remove_weight_norm(l)
+
+
+class ResBlock2(torch.nn.Module):
+    """Residual Block Type 2. It has 1 convolutional layers in each convolutional block.
+
+    Network::
+
+        x -> lrelu -> conv1-> -> z -> lrelu -> conv2-> o -> + -> o
+        |---------------------------------------------------|
+
+
+    Args:
+        channels (int): number of hidden channels for the convolutional layers.
+        kernel_size (int): size of the convolution filter in each layer.
+        dilations (list): list of dilation value for each conv layer in a block.
+    """
+
+    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
+        super().__init__()
+        self.convs = nn.ModuleList(
+            [
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[0],
+                        padding=get_padding(kernel_size, dilation[0]),
+                    )
+                ),
+                weight_norm(
+                    Conv1d(
+                        channels,
+                        channels,
+                        kernel_size,
+                        1,
+                        dilation=dilation[1],
+                        padding=get_padding(kernel_size, dilation[1]),
+                    )
+                ),
+            ]
+        )
+
+    def forward(self, x):
+        for c in self.convs:
+            xt = F.leaky_relu(x, LRELU_SLOPE)
+            xt = c(xt)
+            x = xt + x
+        return x
+
+    def remove_weight_norm(self):
+        for l in self.convs:
+            remove_weight_norm(l)
+
+
+class HifiganGenerator(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        resblock_type,
+        resblock_dilation_sizes,
+        resblock_kernel_sizes,
+        upsample_kernel_sizes,
+        upsample_initial_channel,
+        upsample_factors,
+        inference_padding=5,
+        cond_channels=0,
+        conv_pre_weight_norm=True,
+        conv_post_weight_norm=True,
+        conv_post_bias=True,
+    ):
+        r"""HiFiGAN Generator with Multi-Receptive Field Fusion (MRF)
+
+        Network:
+            x -> lrelu -> upsampling_layer -> resblock1_k1x1 -> z1 -> + -> z_sum / #resblocks -> lrelu -> conv_post_7x1 -> tanh -> o
+                                                 ..          -> zI ---|
+                                              resblockN_kNx1 -> zN ---'
+
+        Args:
+            in_channels (int): number of input tensor channels.
+            out_channels (int): number of output tensor channels.
+            resblock_type (str): type of the `ResBlock`. '1' or '2'.
+            resblock_dilation_sizes (List[List[int]]): list of dilation values in each layer of a `ResBlock`.
+            resblock_kernel_sizes (List[int]): list of kernel sizes for each `ResBlock`.
+            upsample_kernel_sizes (List[int]): list of kernel sizes for each transposed convolution.
+            upsample_initial_channel (int): number of channels for the first upsampling layer. This is divided by 2
+                for each consecutive upsampling layer.
+            upsample_factors (List[int]): upsampling factors (stride) for each upsampling layer.
+            inference_padding (int): constant padding applied to the input at inference time. Defaults to 5.
+        """
+        super().__init__()
+        self.inference_padding = inference_padding
+        self.num_kernels = len(resblock_kernel_sizes)
+        self.num_upsamples = len(upsample_factors)
+        # initial upsampling layers
+        self.conv_pre = weight_norm(Conv1d(in_channels, upsample_initial_channel, 7, 1, padding=3))
+        resblock = ResBlock1 if resblock_type == "1" else ResBlock2
+        # upsampling layers
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(upsample_factors, upsample_kernel_sizes)):
+            self.ups.append(
+                weight_norm(
+                    ConvTranspose1d(
+                        upsample_initial_channel // (2**i),
+                        upsample_initial_channel // (2 ** (i + 1)),
+                        k,
+                        u,
+                        padding=(k - u) // 2,
+                    )
+                )
+            )
+        # MRF blocks
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = upsample_initial_channel // (2 ** (i + 1))
+            for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
+                self.resblocks.append(resblock(ch, k, d))
+        # post convolution layer
+        self.conv_post = weight_norm(Conv1d(ch, out_channels, 7, 1, padding=3, bias=conv_post_bias))
+        if cond_channels > 0:
+            self.cond_layer = nn.Conv1d(cond_channels, upsample_initial_channel, 1)
+
+        if not conv_pre_weight_norm:
+            remove_weight_norm(self.conv_pre)
+
+        if not conv_post_weight_norm:
+            remove_weight_norm(self.conv_post)
+
+        self.device = torch.device('cuda' if torch.cuda.is_available() else'cpu')
+        if torch.backends.mps.is_available():
+            self.device = torch.device('mps')
+
+    def forward(self, x, g=None):
+        """
+        Args:
+            x (Tensor): feature input tensor.
+            g (Tensor): global conditioning input tensor.
+
+        Returns:
+            Tensor: output waveform.
+
+        Shapes:
+            x: [B, C, T]
+            Tensor: [B, 1, T]
+        """
+        o = self.conv_pre(x)
+        if hasattr(self, "cond_layer"):
+            o = o + self.cond_layer(g)
+        for i in range(self.num_upsamples):
+            o = F.leaky_relu(o, LRELU_SLOPE)
+            o = self.ups[i](o)
+            z_sum = None
+            for j in range(self.num_kernels):
+                if z_sum is None:
+                    z_sum = self.resblocks[i * self.num_kernels + j](o)
+                else:
+                    z_sum += self.resblocks[i * self.num_kernels + j](o)
+            o = z_sum / self.num_kernels
+        o = F.leaky_relu(o)
+        o = self.conv_post(o)
+        o = torch.tanh(o)
+        return o
+
+    @torch.no_grad()
+    def inference(self, c, g=None):
+        """
+        Args:
+            x (Tensor): conditioning input tensor.
+
+        Returns:
+            Tensor: output waveform.
+
+        Shapes:
+            x: [B, C, T]
+            Tensor: [B, 1, T]
+        """
+        # c = c.to(self.conv_pre.weight.device)
+        # c = torch.nn.functional.pad(c, (self.inference_padding, self.inference_padding), "replicate")
+        up_1 = torch.nn.functional.interpolate(
+                c.transpose(1,2),
+                scale_factor=[1024 / 256],
+                mode="linear",
+            )
+        up_2 = torch.nn.functional.interpolate(
+            up_1,
+            scale_factor=[24000 / 22050],
+            mode="linear",
+        )
+        g = g.unsqueeze(0)
+        return self.forward(up_2.to(self.device), g.transpose(1,2))
+
+    def remove_weight_norm(self):
+        print("Removing weight norm...")
+        for l in self.ups:
+            remove_weight_norm(l)
+        for l in self.resblocks:
+            l.remove_weight_norm()
+        remove_weight_norm(self.conv_pre)
+        remove_weight_norm(self.conv_post)
diff --git a/tortoise/tortoise/models/random_latent_generator.py b/tortoise/tortoise/models/random_latent_generator.py
new file mode 100644
index 0000000..e90ef21
--- /dev/null
+++ b/tortoise/tortoise/models/random_latent_generator.py
@@ -0,0 +1,55 @@
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
+    if bias is not None:
+        rest_dim = [1] * (input.ndim - bias.ndim - 1)
+        return (
+            F.leaky_relu(
+                input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=negative_slope
+            )
+            * scale
+        )
+    else:
+        return F.leaky_relu(input, negative_slope=0.2) * scale
+
+
+class EqualLinear(nn.Module):
+    def __init__(
+        self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1
+    ):
+        super().__init__()
+        self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
+        else:
+            self.bias = None
+        self.scale = (1 / math.sqrt(in_dim)) * lr_mul
+        self.lr_mul = lr_mul
+
+    def forward(self, input):
+        out = F.linear(input, self.weight * self.scale)
+        out = fused_leaky_relu(out, self.bias * self.lr_mul)
+        return out
+
+
+class RandomLatentConverter(nn.Module):
+    def __init__(self, channels):
+        super().__init__()
+        self.layers = nn.Sequential(*[EqualLinear(channels, channels, lr_mul=.1) for _ in range(5)],
+                                    nn.Linear(channels, channels))
+        self.channels = channels
+
+    def forward(self, ref):
+        r = torch.randn(ref.shape[0], self.channels, device=ref.device)
+        y = self.layers(r)
+        return y
+
+
+if __name__ == '__main__':
+    model = RandomLatentConverter(512)
+    model(torch.randn(5,512))
\ No newline at end of file
diff --git a/tortoise/tortoise/models/stream_generator.py b/tortoise/tortoise/models/stream_generator.py
new file mode 100644
index 0000000..a8dd07b
--- /dev/null
+++ b/tortoise/tortoise/models/stream_generator.py
@@ -0,0 +1,1057 @@
+# Adapted from: https://github.com/LowinLi/transformers-stream-generator
+
+from transformers import (
+    GenerationConfig,
+    GenerationMixin,
+    LogitsProcessorList,
+    StoppingCriteriaList,
+    DisjunctiveConstraint,
+    BeamSearchScorer,
+    PhrasalConstraint,
+    ConstrainedBeamSearchScorer,
+    PreTrainedModel,
+)
+import numpy as np
+import random
+import warnings
+import inspect
+from transformers.generation.utils import GenerateOutput, SampleOutput, logger
+import torch
+from typing import Callable, List, Optional, Union
+from torch import nn
+import torch.distributed as dist
+import copy
+
+
+def setup_seed(seed):
+    if seed == -1:
+        return
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    torch.backends.cudnn.deterministic = True
+
+
+class StreamGenerationConfig(GenerationConfig):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.do_stream = kwargs.pop("do_stream", False)
+
+
+class NewGenerationMixin(GenerationMixin):
+    @torch.no_grad()
+    def generate(
+        self,
+        inputs: Optional[torch.Tensor] = None,
+        generation_config: Optional[StreamGenerationConfig] = None,
+        logits_processor: Optional[LogitsProcessorList] = None,
+        stopping_criteria: Optional[StoppingCriteriaList] = None,
+        prefix_allowed_tokens_fn: Optional[
+            Callable[[int, torch.Tensor], List[int]]
+        ] = None,
+        synced_gpus: Optional[bool] = False,
+        seed=0,
+        **kwargs,
+    ) -> Union[GenerateOutput, torch.LongTensor]:
+        r"""
+
+        Generates sequences of token ids for models with a language modeling head.
+
+        <Tip warning={true}>
+
+        Most generation-controlling parameters are set in `generation_config` which, if not passed, will be set to the
+        model's default generation configuration. You can override any `generation_config` by passing the corresponding
+        parameters to generate(), e.g. `.generate(inputs, num_beams=4, do_sample=True)`.
+
+        For an overview of generation strategies and code examples, check out the [following
+        guide](./generation_strategies).
+
+        </Tip>
+
+        Parameters:
+            inputs (`torch.Tensor` of varying shape depending on the modality, *optional*):
+                The sequence used as a prompt for the generation or as model inputs to the encoder. If `None` the
+                method initializes it with `bos_token_id` and a batch size of 1. For decoder-only models `inputs`
+                should of in the format of `input_ids`. For encoder-decoder models *inputs* can represent any of
+                `input_ids`, `input_values`, `input_features`, or `pixel_values`.
+            generation_config (`~generation.GenerationConfig`, *optional*):
+                The generation configuration to be used as base parametrization for the generation call. `**kwargs`
+                passed to generate matching the attributes of `generation_config` will override them. If
+                `generation_config` is not provided, the default will be used, which had the following loading
+                priority: 1) from the `generation_config.json` model file, if it exists; 2) from the model
+                configuration. Please note that unspecified parameters will inherit [`~generation.GenerationConfig`]'s
+                default values, whose documentation should be checked to parameterize generation.
+            logits_processor (`LogitsProcessorList`, *optional*):
+                Custom logits processors that complement the default logits processors built from arguments and
+                generation config. If a logit processor is passed that is already created with the arguments or a
+                generation config an error is thrown. This feature is intended for advanced users.
+            stopping_criteria (`StoppingCriteriaList`, *optional*):
+                Custom stopping criteria that complement the default stopping criteria built from arguments and a
+                generation config. If a stopping criteria is passed that is already created with the arguments or a
+                generation config an error is thrown. This feature is intended for advanced users.
+            prefix_allowed_tokens_fn (`Callable[[int, torch.Tensor], List[int]]`, *optional*):
+                If provided, this function constraints the beam search to allowed tokens only at each step. If not
+                provided no constraint is applied. This function takes 2 arguments: the batch ID `batch_id` and
+                `input_ids`. It has to return a list with the allowed tokens for the next generation step conditioned
+                on the batch ID `batch_id` and the previously generated tokens `inputs_ids`. This argument is useful
+                for constrained generation conditioned on the prefix, as described in [Autoregressive Entity
+                Retrieval](https://arxiv.org/abs/2010.00904).
+            synced_gpus (`bool`, *optional*, defaults to `False`):
+                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
+            kwargs:
+                Ad hoc parametrization of `generate_config` and/or additional model-specific kwargs that will be
+                forwarded to the `forward` function of the model. If the model is an encoder-decoder model, encoder
+                specific kwargs should not be prefixed and decoder specific kwargs should be prefixed with *decoder_*.
+
+        Return:
+            [`~utils.ModelOutput`] or `torch.LongTensor`: A [`~utils.ModelOutput`] (if `return_dict_in_generate=True`
+            or when `config.return_dict_in_generate=True`) or a `torch.FloatTensor`.
+
+                If the model is *not* an encoder-decoder model (`model.config.is_encoder_decoder=False`), the possible
+                [`~utils.ModelOutput`] types are:
+
+                    - [`~generation.GreedySearchDecoderOnlyOutput`],
+                    - [`~generation.SampleDecoderOnlyOutput`],
+                    - [`~generation.BeamSearchDecoderOnlyOutput`],
+                    - [`~generation.BeamSampleDecoderOnlyOutput`]
+
+                If the model is an encoder-decoder model (`model.config.is_encoder_decoder=True`), the possible
+                [`~utils.ModelOutput`] types are:
+
+                    - [`~generation.GreedySearchEncoderDecoderOutput`],
+                    - [`~generation.SampleEncoderDecoderOutput`],
+                    - [`~generation.BeamSearchEncoderDecoderOutput`],
+                    - [`~generation.BeamSampleEncoderDecoderOutput`]
+        """
+        setup_seed(seed)
+        # 1. Handle `generation_config` and kwargs that might update it, and validate the `.generate()` call
+        self._validate_model_class()
+
+        # priority: `generation_config` argument > `model.generation_config` (the default generation config)
+        if generation_config is None:
+            # legacy: users may modify the model configuration to control generation -- update the generation config
+            # model attribute accordingly, if it was created from the model config
+            if self.generation_config._from_model_config:
+                new_generation_config = StreamGenerationConfig.from_model_config(
+                    self.config
+                )
+                if new_generation_config != self.generation_config:
+                    warnings.warn(
+                        "You have modified the pretrained model configuration to control generation. This is a"
+                        " deprecated strategy to control generation and will be removed soon, in a future version."
+                        " Please use a generation configuration file (see"
+                        " https://huggingface.co/docs/transformers/main_classes/text_generation)"
+                    )
+                    self.generation_config = new_generation_config
+            generation_config = self.generation_config
+
+        generation_config = copy.deepcopy(generation_config)
+        model_kwargs = generation_config.update(
+            **kwargs
+        )  # All unused kwargs must be model kwargs
+        # self._validate_model_kwargs(model_kwargs.copy())
+
+        # 2. Set generation parameters if not already defined
+        logits_processor = (
+            logits_processor if logits_processor is not None else LogitsProcessorList()
+        )
+        stopping_criteria = (
+            stopping_criteria
+            if stopping_criteria is not None
+            else StoppingCriteriaList()
+        )
+
+        if (
+            generation_config.pad_token_id is None
+            and generation_config.eos_token_id is not None
+        ):
+            if model_kwargs.get("attention_mask", None) is None:
+                logger.warning(
+                    "The attention mask and the pad token id were not set. As a consequence, you may observe "
+                    "unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results."
+                )
+            eos_token_id = generation_config.eos_token_id
+            if isinstance(eos_token_id, list):
+                eos_token_id = eos_token_id[0]
+            logger.warning(
+                f"Setting `pad_token_id` to `eos_token_id`:{eos_token_id} for open-end generation."
+            )
+            generation_config.pad_token_id = eos_token_id
+
+        # 3. Define model inputs
+        # inputs_tensor has to be defined
+        # model_input_name is defined if model-specific keyword input is passed
+        # otherwise model_input_name is None
+        # all model-specific keyword inputs are removed from `model_kwargs`
+        inputs_tensor, model_input_name, model_kwargs = self._prepare_model_inputs(
+            inputs, generation_config.bos_token_id, model_kwargs
+        )
+        batch_size = inputs_tensor.shape[0]
+
+        # 4. Define other model kwargs
+        model_kwargs["output_attentions"] = generation_config.output_attentions
+        model_kwargs["output_hidden_states"] = generation_config.output_hidden_states
+        model_kwargs["use_cache"] = generation_config.use_cache
+
+        accepts_attention_mask = "attention_mask" in set(
+            inspect.signature(self.forward).parameters.keys()
+        )
+        requires_attention_mask = "encoder_outputs" not in model_kwargs
+
+        if (
+            model_kwargs.get("attention_mask", None) is None
+            and requires_attention_mask
+            and accepts_attention_mask
+        ):
+            model_kwargs[
+                "attention_mask"
+            ] = self._prepare_attention_mask_for_generation(
+                inputs_tensor,
+                generation_config.pad_token_id,
+                generation_config.eos_token_id,
+            )
+
+        # decoder-only models should use left-padding for generation
+        if not self.config.is_encoder_decoder:
+            if (
+                generation_config.pad_token_id is not None
+                and torch.sum(inputs_tensor[:, -1] == generation_config.pad_token_id)
+                > 0
+            ):
+                logger.warning(
+                    "A decoder-only architecture is being used, but right-padding was detected! For correct "
+                    "generation results, please set `padding_side='left'` when initializing the tokenizer."
+                )
+
+        if self.config.is_encoder_decoder and "encoder_outputs" not in model_kwargs:
+            # if model is encoder decoder encoder_outputs are created
+            # and added to `model_kwargs`
+            model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(
+                inputs_tensor, model_kwargs, model_input_name
+            )
+
+        # 5. Prepare `input_ids` which will be used for auto-regressive generation
+        if self.config.is_encoder_decoder:
+            input_ids = self._prepare_decoder_input_ids_for_generation(
+                batch_size,
+                decoder_start_token_id=generation_config.decoder_start_token_id,
+                bos_token_id=generation_config.bos_token_id,
+                model_kwargs=model_kwargs,
+                device=inputs_tensor.device,
+            )
+        else:
+            # if decoder-only then inputs_tensor has to be `input_ids`
+            input_ids = inputs_tensor
+
+        # 6. Prepare `max_length` depending on other stopping criteria.
+        input_ids_seq_length = input_ids.shape[-1]
+        has_default_max_length = (
+            kwargs.get("max_length") is None
+            and generation_config.max_length is not None
+        )
+        if has_default_max_length and generation_config.max_new_tokens is None:
+            warnings.warn(
+                "Neither `max_length` nor `max_new_tokens` has been set, `max_length` will default to"
+                f" {generation_config.max_length} (`generation_config.max_length`). Controlling `max_length` via the"
+                " config is deprecated and `max_length` will be removed from the config in v5 of Transformers -- we"
+                " recommend using `max_new_tokens` to control the maximum length of the generation.",
+                UserWarning,
+            )
+        elif has_default_max_length and generation_config.max_new_tokens is not None:
+            generation_config.max_length = (
+                generation_config.max_new_tokens + input_ids_seq_length
+            )
+        elif (
+            not has_default_max_length and generation_config.max_new_tokens is not None
+        ):
+            raise ValueError(
+                "Both `max_new_tokens` and `max_length` have been set but they serve the same purpose -- setting a"
+                " limit to the generated output length. Remove one of those arguments. Please refer to the"
+                " documentation for more information. "
+                "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)"
+            )
+
+        if (
+            generation_config.min_length is not None
+            and generation_config.min_length > generation_config.max_length
+        ):
+            raise ValueError(
+                f"Unfeasible length constraints: the minimum length ({generation_config.min_length}) is larger than"
+                f" the maximum length ({generation_config.max_length})"
+            )
+        if input_ids_seq_length >= generation_config.max_length:
+            input_ids_string = (
+                "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
+            )
+            logger.warning(
+                f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
+                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
+                " increasing `max_new_tokens`."
+            )
+
+        # 7. determine generation mode
+        is_constraint_gen_mode = (
+            generation_config.constraints is not None
+            or generation_config.force_words_ids is not None
+        )
+
+        is_contrastive_search_gen_mode = (
+            generation_config.top_k is not None
+            and generation_config.top_k > 1
+            and generation_config.do_sample is False
+            and generation_config.penalty_alpha is not None
+            and generation_config.penalty_alpha > 0
+        )
+
+        is_greedy_gen_mode = (
+            (generation_config.num_beams == 1)
+            and (generation_config.num_beam_groups == 1)
+            and generation_config.do_sample is False
+            and not is_constraint_gen_mode
+            and not is_contrastive_search_gen_mode
+        )
+        is_sample_gen_mode = (
+            (generation_config.num_beams == 1)
+            and (generation_config.num_beam_groups == 1)
+            and generation_config.do_sample is True
+            and generation_config.do_stream is False
+            and not is_constraint_gen_mode
+            and not is_contrastive_search_gen_mode
+        )
+        is_sample_gen_stream_mode = (
+            (generation_config.num_beams == 1)
+            and (generation_config.num_beam_groups == 1)
+            and generation_config.do_stream is True
+            and not is_constraint_gen_mode
+            and not is_contrastive_search_gen_mode
+        )
+        is_beam_gen_mode = (
+            (generation_config.num_beams > 1)
+            and (generation_config.num_beam_groups == 1)
+            and generation_config.do_sample is False
+            and not is_constraint_gen_mode
+            and not is_contrastive_search_gen_mode
+        )
+        is_beam_sample_gen_mode = (
+            (generation_config.num_beams > 1)
+            and (generation_config.num_beam_groups == 1)
+            and generation_config.do_sample is True
+            and not is_constraint_gen_mode
+            and not is_contrastive_search_gen_mode
+        )
+        is_group_beam_gen_mode = (
+            (generation_config.num_beams > 1)
+            and (generation_config.num_beam_groups > 1)
+            and not is_constraint_gen_mode
+            and not is_contrastive_search_gen_mode
+        )
+
+        if generation_config.num_beam_groups > generation_config.num_beams:
+            raise ValueError(
+                "`num_beam_groups` has to be smaller or equal to `num_beams`"
+            )
+        if is_group_beam_gen_mode and generation_config.do_sample is True:
+            raise ValueError(
+                "Diverse beam search cannot be used in sampling mode. Make sure that `do_sample` is set to `False`."
+            )
+
+        if self.device.type != input_ids.device.type:
+            warnings.warn(
+                "You are calling .generate() with the `input_ids` being on a device type different"
+                f" than your model's device. `input_ids` is on {input_ids.device.type}, whereas the model"
+                f" is on {self.device.type}. You may experience unexpected behaviors or slower generation."
+                " Please make sure that you have put `input_ids` to the"
+                f" correct device by calling for example input_ids = input_ids.to('{self.device.type}') before"
+                " running `.generate()`.",
+                UserWarning,
+            )
+        # 8. prepare distribution pre_processing samplers
+        logits_processor = self._get_logits_processor(
+            generation_config=generation_config,
+            input_ids_seq_length=input_ids_seq_length,
+            encoder_input_ids=inputs_tensor,
+            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
+            logits_processor=logits_processor,
+        )
+
+        # 9. prepare stopping criteria
+        stopping_criteria = self._get_stopping_criteria(
+            generation_config=generation_config, stopping_criteria=stopping_criteria
+        )
+        # 10. go into different generation modes
+        if is_greedy_gen_mode:
+            if generation_config.num_return_sequences > 1:
+                raise ValueError(
+                    f"num_return_sequences has to be 1, but is {generation_config.num_return_sequences} when doing"
+                    " greedy search."
+                )
+
+            # 11. run greedy search
+            return self.greedy_search(
+                input_ids,
+                logits_processor=logits_processor,
+                stopping_criteria=stopping_criteria,
+                pad_token_id=generation_config.pad_token_id,
+                eos_token_id=generation_config.eos_token_id,
+                output_scores=generation_config.output_scores,
+                return_dict_in_generate=generation_config.return_dict_in_generate,
+                synced_gpus=synced_gpus,
+                **model_kwargs,
+            )
+
+        elif is_contrastive_search_gen_mode:
+            if generation_config.num_return_sequences > 1:
+                raise ValueError(
+                    f"num_return_sequences has to be 1, but is {generation_config.num_return_sequences} when doing"
+                    " contrastive search."
+                )
+
+            return self.contrastive_search(
+                input_ids,
+                top_k=generation_config.top_k,
+                penalty_alpha=generation_config.penalty_alpha,
+                logits_processor=logits_processor,
+                stopping_criteria=stopping_criteria,
+                pad_token_id=generation_config.pad_token_id,
+                eos_token_id=generation_config.eos_token_id,
+                output_scores=generation_config.output_scores,
+                return_dict_in_generate=generation_config.return_dict_in_generate,
+                synced_gpus=synced_gpus,
+                **model_kwargs,
+            )
+
+        elif is_sample_gen_mode:
+            # 11. prepare logits warper
+            logits_warper = self._get_logits_warper(generation_config)
+
+            # 12. expand input_ids with `num_return_sequences` additional sequences per batch
+            input_ids, model_kwargs = self._expand_inputs_for_generation(
+                input_ids=input_ids,
+                expand_size=generation_config.num_return_sequences,
+                is_encoder_decoder=self.config.is_encoder_decoder,
+                **model_kwargs,
+            )
+
+            # 13. run sample
+            return self.sample(
+                input_ids,
+                logits_processor=logits_processor,
+                logits_warper=logits_warper,
+                stopping_criteria=stopping_criteria,
+                pad_token_id=generation_config.pad_token_id,
+                eos_token_id=generation_config.eos_token_id,
+                output_scores=generation_config.output_scores,
+                return_dict_in_generate=generation_config.return_dict_in_generate,
+                synced_gpus=synced_gpus,
+                **model_kwargs,
+            )
+        elif is_sample_gen_stream_mode:
+            # 11. prepare logits warper
+            logits_warper = self._get_logits_warper(generation_config)
+
+            # 12. expand input_ids with `num_return_sequences` additional sequences per batch
+            input_ids, model_kwargs = self._expand_inputs_for_generation(
+                input_ids=input_ids,
+                expand_size=generation_config.num_return_sequences,
+                is_encoder_decoder=self.config.is_encoder_decoder,
+                **model_kwargs,
+            )
+
+            # 13. run sample
+            return self.sample_stream(
+                input_ids,
+                logits_processor=logits_processor,
+                logits_warper=logits_warper,
+                stopping_criteria=stopping_criteria,
+                pad_token_id=generation_config.pad_token_id,
+                eos_token_id=generation_config.eos_token_id,
+                output_scores=generation_config.output_scores,
+                return_dict_in_generate=generation_config.return_dict_in_generate,
+                synced_gpus=synced_gpus,
+                **model_kwargs,
+            )
+        elif is_beam_gen_mode:
+            if generation_config.num_return_sequences > generation_config.num_beams:
+                raise ValueError(
+                    "`num_return_sequences` has to be smaller or equal to `num_beams`."
+                )
+
+            if stopping_criteria.max_length is None:
+                raise ValueError(
+                    "`max_length` needs to be a stopping_criteria for now."
+                )
+
+            # 11. prepare beam search scorer
+            beam_scorer = BeamSearchScorer(
+                batch_size=batch_size,
+                num_beams=generation_config.num_beams,
+                device=inputs_tensor.device,
+                length_penalty=generation_config.length_penalty,
+                do_early_stopping=generation_config.early_stopping,
+                num_beam_hyps_to_keep=generation_config.num_return_sequences,
+            )
+            # 12. interleave input_ids with `num_beams` additional sequences per batch
+            input_ids, model_kwargs = self._expand_inputs_for_generation(
+                input_ids=input_ids,
+                expand_size=generation_config.num_beams,
+                is_encoder_decoder=self.config.is_encoder_decoder,
+                **model_kwargs,
+            )
+            # 13. run beam search
+            return self.beam_search(
+                input_ids,
+                beam_scorer,
+                logits_processor=logits_processor,
+                stopping_criteria=stopping_criteria,
+                pad_token_id=generation_config.pad_token_id,
+                eos_token_id=generation_config.eos_token_id,
+                output_scores=generation_config.output_scores,
+                return_dict_in_generate=generation_config.return_dict_in_generate,
+                synced_gpus=synced_gpus,
+                **model_kwargs,
+            )
+
+        elif is_beam_sample_gen_mode:
+            # 11. prepare logits warper
+            logits_warper = self._get_logits_warper(generation_config)
+
+            if stopping_criteria.max_length is None:
+                raise ValueError(
+                    "`max_length` needs to be a stopping_criteria for now."
+                )
+            # 12. prepare beam search scorer
+            beam_scorer = BeamSearchScorer(
+                batch_size=batch_size * generation_config.num_return_sequences,
+                num_beams=generation_config.num_beams,
+                device=inputs_tensor.device,
+                length_penalty=generation_config.length_penalty,
+                do_early_stopping=generation_config.early_stopping,
+            )
+
+            # 13. interleave input_ids with `num_beams` additional sequences per batch
+            input_ids, model_kwargs = self._expand_inputs_for_generation(
+                input_ids=input_ids,
+                expand_size=generation_config.num_beams
+                * generation_config.num_return_sequences,
+                is_encoder_decoder=self.config.is_encoder_decoder,
+                **model_kwargs,
+            )
+
+            # 14. run beam sample
+            return self.beam_sample(
+                input_ids,
+                beam_scorer,
+                logits_processor=logits_processor,
+                logits_warper=logits_warper,
+                stopping_criteria=stopping_criteria,
+                pad_token_id=generation_config.pad_token_id,
+                eos_token_id=generation_config.eos_token_id,
+                output_scores=generation_config.output_scores,
+                return_dict_in_generate=generation_config.return_dict_in_generate,
+                synced_gpus=synced_gpus,
+                **model_kwargs,
+            )
+
+        elif is_group_beam_gen_mode:
+            if generation_config.num_return_sequences > generation_config.num_beams:
+                raise ValueError(
+                    "`num_return_sequences` has to be smaller or equal to `num_beams`."
+                )
+
+            if generation_config.num_beams % generation_config.num_beam_groups != 0:
+                raise ValueError(
+                    "`num_beams` should be divisible by `num_beam_groups` for group beam search."
+                )
+
+            if stopping_criteria.max_length is None:
+                raise ValueError(
+                    "`max_length` needs to be a stopping_criteria for now."
+                )
+
+            has_default_typical_p = (
+                kwargs.get("typical_p") is None and generation_config.typical_p == 1.0
+            )
+            if not has_default_typical_p:
+                raise ValueError(
+                    "Decoder argument `typical_p` is not supported with beam groups."
+                )
+
+            # 11. prepare beam search scorer
+            beam_scorer = BeamSearchScorer(
+                batch_size=batch_size,
+                num_beams=generation_config.num_beams,
+                max_length=stopping_criteria.max_length,
+                device=inputs_tensor.device,
+                length_penalty=generation_config.length_penalty,
+                do_early_stopping=generation_config.early_stopping,
+                num_beam_hyps_to_keep=generation_config.num_return_sequences,
+                num_beam_groups=generation_config.num_beam_groups,
+            )
+            # 12. interleave input_ids with `num_beams` additional sequences per batch
+            input_ids, model_kwargs = self._expand_inputs_for_generation(
+                input_ids=input_ids,
+                expand_size=generation_config.num_beams,
+                is_encoder_decoder=self.config.is_encoder_decoder,
+                **model_kwargs,
+            )
+            # 13. run beam search
+            return self.group_beam_search(
+                input_ids,
+                beam_scorer,
+                logits_processor=logits_processor,
+                stopping_criteria=stopping_criteria,
+                pad_token_id=generation_config.pad_token_id,
+                eos_token_id=generation_config.eos_token_id,
+                output_scores=generation_config.output_scores,
+                return_dict_in_generate=generation_config.return_dict_in_generate,
+                synced_gpus=synced_gpus,
+                **model_kwargs,
+            )
+
+        elif is_constraint_gen_mode:
+            if generation_config.num_return_sequences > generation_config.num_beams:
+                raise ValueError(
+                    "`num_return_sequences` has to be smaller or equal to `num_beams`."
+                )
+
+            if stopping_criteria.max_length is None:
+                raise ValueError(
+                    "`max_length` needs to be a stopping_criteria for now."
+                )
+
+            if generation_config.num_beams <= 1:
+                raise ValueError(
+                    "`num_beams` needs to be greater than 1 for constrained generation."
+                )
+
+            if generation_config.do_sample:
+                raise ValueError(
+                    "`do_sample` needs to be false for constrained generation."
+                )
+
+            if (
+                generation_config.num_beam_groups is not None
+                and generation_config.num_beam_groups > 1
+            ):
+                raise ValueError(
+                    "`num_beam_groups` not supported yet for constrained generation."
+                )
+
+            final_constraints = []
+            if generation_config.constraints is not None:
+                final_constraints = generation_config.constraints
+
+            if generation_config.force_words_ids is not None:
+
+                def typeerror():
+                    raise ValueError(
+                        "`force_words_ids` has to either be a `List[List[List[int]]]` or `List[List[int]]`"
+                        f"of positive integers, but is {generation_config.force_words_ids}."
+                    )
+
+                if (
+                    not isinstance(generation_config.force_words_ids, list)
+                    or len(generation_config.force_words_ids) == 0
+                ):
+                    typeerror()
+
+                for word_ids in generation_config.force_words_ids:
+                    if isinstance(word_ids[0], list):
+                        if not isinstance(word_ids, list) or len(word_ids) == 0:
+                            typeerror()
+                        if any(
+                            not isinstance(token_ids, list) for token_ids in word_ids
+                        ):
+                            typeerror()
+                        if any(
+                            any(
+                                (not isinstance(token_id, int) or token_id < 0)
+                                for token_id in token_ids
+                            )
+                            for token_ids in word_ids
+                        ):
+                            typeerror()
+
+                        constraint = DisjunctiveConstraint(word_ids)
+                    else:
+                        if not isinstance(word_ids, list) or len(word_ids) == 0:
+                            typeerror()
+                        if any(
+                            (not isinstance(token_id, int) or token_id < 0)
+                            for token_id in word_ids
+                        ):
+                            typeerror()
+
+                        constraint = PhrasalConstraint(word_ids)
+                    final_constraints.append(constraint)
+
+            # 11. prepare beam search scorer
+            constrained_beam_scorer = ConstrainedBeamSearchScorer(
+                constraints=final_constraints,
+                batch_size=batch_size,
+                num_beams=generation_config.num_beams,
+                device=inputs_tensor.device,
+                length_penalty=generation_config.length_penalty,
+                do_early_stopping=generation_config.early_stopping,
+                num_beam_hyps_to_keep=generation_config.num_return_sequences,
+            )
+            # 12. interleave input_ids with `num_beams` additional sequences per batch
+            input_ids, model_kwargs = self._expand_inputs_for_generation(
+                input_ids=input_ids,
+                expand_size=generation_config.num_beams,
+                is_encoder_decoder=self.config.is_encoder_decoder,
+                **model_kwargs,
+            )
+            # 13. run beam search
+            return self.constrained_beam_search(
+                input_ids,
+                constrained_beam_scorer=constrained_beam_scorer,
+                logits_processor=logits_processor,
+                stopping_criteria=stopping_criteria,
+                pad_token_id=generation_config.pad_token_id,
+                eos_token_id=generation_config.eos_token_id,
+                output_scores=generation_config.output_scores,
+                return_dict_in_generate=generation_config.return_dict_in_generate,
+                synced_gpus=synced_gpus,
+                **model_kwargs,
+            )
+
+    @torch.no_grad()
+    def sample_stream(
+        self,
+        input_ids: torch.LongTensor,
+        logits_processor: Optional[LogitsProcessorList] = None,
+        stopping_criteria: Optional[StoppingCriteriaList] = None,
+        logits_warper: Optional[LogitsProcessorList] = None,
+        max_length: Optional[int] = None,
+        pad_token_id: Optional[int] = None,
+        eos_token_id: Optional[Union[int, List[int]]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_scores: Optional[bool] = None,
+        return_dict_in_generate: Optional[bool] = None,
+        synced_gpus: Optional[bool] = False,
+        **model_kwargs,
+    ) -> Union[SampleOutput, torch.LongTensor]:
+        r"""
+        Generates sequences of token ids for models with a language modeling head using **multinomial sampling** and
+        can be used for text-decoder, text-to-text, speech-to-text, and vision-to-text models.
+
+        <Tip warning={true}>
+
+        In most cases, you do not need to call [`~generation.GenerationMixin.sample`] directly. Use generate() instead.
+        For an overview of generation strategies and code examples, check the [following
+        guide](./generation_strategies).
+
+        </Tip>
+
+        Parameters:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                The sequence used as a prompt for the generation.
+            logits_processor (`LogitsProcessorList`, *optional*):
+                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsProcessor`]
+                used to modify the prediction scores of the language modeling head applied at each generation step.
+            stopping_criteria (`StoppingCriteriaList`, *optional*):
+                An instance of [`StoppingCriteriaList`]. List of instances of class derived from [`StoppingCriteria`]
+                used to tell if the generation loop should stop.
+            logits_warper (`LogitsProcessorList`, *optional*):
+                An instance of [`LogitsProcessorList`]. List of instances of class derived from [`LogitsWarper`] used
+                to warp the prediction score distribution of the language modeling head applied before multinomial
+                sampling at each generation step.
+            max_length (`int`, *optional*, defaults to 20):
+                **DEPRECATED**. Use `logits_processor` or `stopping_criteria` directly to cap the number of generated
+                tokens. The maximum length of the sequence to be generated.
+            pad_token_id (`int`, *optional*):
+                The id of the *padding* token.
+            eos_token_id (`int`, *optional*):
+                The id of the *end-of-sequence* token.
+            output_attentions (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more details.
+            output_hidden_states (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more details.
+            output_scores (`bool`, *optional*, defaults to `False`):
+                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
+            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+            synced_gpus (`bool`, *optional*, defaults to `False`):
+                Whether to continue running the while loop until max_length (needed for ZeRO stage 3)
+            model_kwargs:
+                Additional model specific kwargs will be forwarded to the `forward` function of the model. If model is
+                an encoder-decoder model the kwargs should include `encoder_outputs`.
+
+        Return:
+            [`~generation.SampleDecoderOnlyOutput`], [`~generation.SampleEncoderDecoderOutput`] or `torch.LongTensor`:
+            A `torch.LongTensor` containing the generated tokens (default behaviour) or a
+            [`~generation.SampleDecoderOnlyOutput`] if `model.config.is_encoder_decoder=False` and
+            `return_dict_in_generate=True` or a [`~generation.SampleEncoderDecoderOutput`] if
+            `model.config.is_encoder_decoder=True`.
+
+        Examples:
+
+        ```python
+        >>> from transformers import (
+        ...     AutoTokenizer,
+        ...     AutoModelForCausalLM,
+        ...     LogitsProcessorList,
+        ...     MinLengthLogitsProcessor,
+        ...     TopKLogitsWarper,
+        ...     TemperatureLogitsWarper,
+        ...     StoppingCriteriaList,
+        ...     MaxLengthCriteria,
+        ... )
+        >>> import torch
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
+        >>> model = AutoModelForCausalLM.from_pretrained("gpt2")
+
+        >>> # set pad_token_id to eos_token_id because GPT2 does not have a EOS token
+        >>> model.config.pad_token_id = model.config.eos_token_id
+        >>> model.generation_config.pad_token_id = model.config.eos_token_id
+
+        >>> input_prompt = "Today is a beautiful day, and"
+        >>> input_ids = tokenizer(input_prompt, return_tensors="pt").input_ids
+
+        >>> # instantiate logits processors
+        >>> logits_processor = LogitsProcessorList(
+        ...     [
+        ...         MinLengthLogitsProcessor(15, eos_token_id=model.generation_config.eos_token_id),
+        ...     ]
+        ... )
+        >>> # instantiate logits processors
+        >>> logits_warper = LogitsProcessorList(
+        ...     [
+        ...         TopKLogitsWarper(50),
+        ...         TemperatureLogitsWarper(0.7),
+        ...     ]
+        ... )
+
+        >>> stopping_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=20)])
+
+        >>> torch.manual_seed(0)  # doctest: +IGNORE_RESULT
+        >>> outputs = model.sample(
+        ...     input_ids,
+        ...     logits_processor=logits_processor,
+        ...     logits_warper=logits_warper,
+        ...     stopping_criteria=stopping_criteria,
+        ... )
+
+        >>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        ['Today is a beautiful day, and a wonderful day.\n\nI was lucky enough to meet the']
+        ```"""
+        # init values
+        logits_processor = (
+            logits_processor if logits_processor is not None else LogitsProcessorList()
+        )
+        stopping_criteria = (
+            stopping_criteria
+            if stopping_criteria is not None
+            else StoppingCriteriaList()
+        )
+        if max_length is not None:
+            warnings.warn(
+                "`max_length` is deprecated in this function, use"
+                " `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
+                UserWarning,
+            )
+            stopping_criteria = validate_stopping_criteria(
+                stopping_criteria, max_length
+            )
+        logits_warper = (
+            logits_warper if logits_warper is not None else LogitsProcessorList()
+        )
+        pad_token_id = (
+            pad_token_id
+            if pad_token_id is not None
+            else self.generation_config.pad_token_id
+        )
+        eos_token_id = (
+            eos_token_id
+            if eos_token_id is not None
+            else self.generation_config.eos_token_id
+        )
+        if isinstance(eos_token_id, int):
+            eos_token_id = [eos_token_id]
+        output_scores = (
+            output_scores
+            if output_scores is not None
+            else self.generation_config.output_scores
+        )
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.generation_config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.generation_config.output_hidden_states
+        )
+        return_dict_in_generate = (
+            return_dict_in_generate
+            if return_dict_in_generate is not None
+            else self.generation_config.return_dict_in_generate
+        )
+
+        # init attention / hidden states / scores tuples
+        scores = () if (return_dict_in_generate and output_scores) else None
+        decoder_attentions = (
+            () if (return_dict_in_generate and output_attentions) else None
+        )
+        cross_attentions = (
+            () if (return_dict_in_generate and output_attentions) else None
+        )
+        decoder_hidden_states = (
+            () if (return_dict_in_generate and output_hidden_states) else None
+        )
+
+        # keep track of which sequences are already finished
+        unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
+
+        this_peer_finished = False  # used by synced_gpus only
+        # auto-regressive generation
+        while True:
+            if synced_gpus:
+                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
+                # The following logic allows an early break if all peers finished generating their sequence
+                this_peer_finished_flag = torch.tensor(
+                    0.0 if this_peer_finished else 1.0
+                ).to(input_ids.device)
+                # send 0.0 if we finished, 1.0 otherwise
+                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
+                # did all peers finish? the reduced sum will be 0.0 then
+                if this_peer_finished_flag.item() == 0.0:
+                    break
+
+            # prepare model inputs
+            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
+
+            # forward pass to get next token
+            outputs = self(
+                **model_inputs,
+                return_dict=True,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+            )
+
+            if synced_gpus and this_peer_finished:
+                continue  # don't waste resources running the code we don't need
+
+            next_token_logits = outputs.logits[:, -1, :]
+
+            # pre-process distribution
+            next_token_scores = logits_processor(input_ids, next_token_logits)
+            next_token_scores = logits_warper(input_ids, next_token_scores)
+
+            # Store scores, attentions and hidden_states when required
+            if return_dict_in_generate:
+                if output_scores:
+                    scores += (next_token_scores,)
+                if output_attentions:
+                    decoder_attentions += (
+                        (outputs.decoder_attentions,)
+                        if self.config.is_encoder_decoder
+                        else (outputs.attentions,)
+                    )
+                    if self.config.is_encoder_decoder:
+                        cross_attentions += (outputs.cross_attentions,)
+
+                if output_hidden_states:
+                    decoder_hidden_states += (
+                        (outputs.decoder_hidden_states,)
+                        if self.config.is_encoder_decoder
+                        else (outputs.hidden_states,)
+                    )
+
+            # sample
+            probs = nn.functional.softmax(next_token_scores, dim=-1)
+            next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
+
+            # finished sentences should have their next token be a padding token
+            if eos_token_id is not None:
+                if pad_token_id is None:
+                    raise ValueError(
+                        "If `eos_token_id` is defined, make sure that `pad_token_id` is defined."
+                    )
+                next_tokens = next_tokens * unfinished_sequences + pad_token_id * (
+                    1 - unfinished_sequences
+                )
+            yield next_tokens, self.final_norm(outputs.hidden_states[-1][:, -1])
+            # update generated ids, model inputs, and length for next step
+            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
+            model_kwargs = self._update_model_kwargs_for_generation(
+                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
+            )
+
+            # if eos_token was found in one sentence, set sentence to finished
+            if eos_token_id is not None:
+                unfinished_sequences = unfinished_sequences.mul(
+                    (sum(next_tokens != i for i in eos_token_id)).long()
+                )
+
+            # stop when each sentence is finished, or if we exceed the maximum length
+            if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
+                if not synced_gpus:
+                    break
+                else:
+                    this_peer_finished = True
+
+
+def init_stream_support():
+    """Overload PreTrainedModel for streaming."""
+    PreTrainedModel.generate_stream = NewGenerationMixin.generate
+    PreTrainedModel.sample_stream = NewGenerationMixin.sample_stream
+
+
+if __name__ == "__main__":
+    from transformers import PreTrainedModel
+    from transformers import AutoTokenizer, AutoModelForCausalLM
+
+    PreTrainedModel.generate = NewGenerationMixin.generate
+    PreTrainedModel.sample_stream = NewGenerationMixin.sample_stream
+    model = AutoModelForCausalLM.from_pretrained(
+        "bigscience/bloom-560m", torch_dtype=torch.float16
+    )
+
+    tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-560m")
+    model = model.to("cuda:0")
+    model = model.eval()
+    prompt_text = "hello? \n"
+    input_ids = tokenizer(
+        prompt_text, return_tensors="pt", add_special_tokens=False
+    ).input_ids
+    input_ids = input_ids.to("cuda:0")
+
+    with torch.no_grad():
+        result = model.generate(
+            input_ids,
+            max_new_tokens=200,
+            do_sample=True,
+            top_k=30,
+            top_p=0.85,
+            temperature=0.35,
+            repetition_penalty=1.2,
+            early_stopping=True,
+            seed=0,
+        )
+        print(tokenizer.decode(result, skip_special_tokens=True))
+        generator = model.generate(
+            input_ids,
+            max_new_tokens=200,
+            do_sample=True,
+            top_k=30,
+            top_p=0.85,
+            temperature=0.35,
+            repetition_penalty=1.2,
+            early_stopping=True,
+            seed=0,
+            do_stream=True,
+        )
+        stream_result = ""
+        for x in generator:
+            chunk = tokenizer.decode(x, skip_special_tokens=True)
+            stream_result += chunk
+        print(stream_result)
diff --git a/tortoise/tortoise/models/transformer.py b/tortoise/tortoise/models/transformer.py
new file mode 100644
index 0000000..d16cbc9
--- /dev/null
+++ b/tortoise/tortoise/models/transformer.py
@@ -0,0 +1,219 @@
+from functools import partial
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+from rotary_embedding_torch import RotaryEmbedding
+from torch import nn
+
+
+# helpers
+
+
+def exists(val):
+    return val is not None
+
+
+def default(val, d):
+    return val if exists(val) else d
+
+
+def cast_tuple(val, depth = 1):
+    if isinstance(val, list):
+        val = tuple(val)
+    return val if isinstance(val, tuple) else (val,) * depth
+
+
+def max_neg_value(t):
+    return -torch.finfo(t.dtype).max
+
+
+def stable_softmax(t, dim = -1, alpha = 32 ** 2):
+    t = t / alpha
+    t = t - torch.amax(t, dim = dim, keepdim = True).detach()
+    return (t * alpha).softmax(dim = dim)
+
+
+def route_args(router, args, depth):
+    routed_args = [(dict(), dict()) for _ in range(depth)]
+    matched_keys = [key for key in args.keys() if key in router]
+
+    for key in matched_keys:
+        val = args[key]
+        for depth, ((f_args, g_args), routes) in enumerate(zip(routed_args, router[key])):
+            new_f_args, new_g_args = map(lambda route: ({key: val} if route else {}), routes)
+            routed_args[depth] = ({**f_args, **new_f_args}, {**g_args, **new_g_args})
+    return routed_args
+
+
+# classes
+class SequentialSequence(nn.Module):
+    def __init__(self, layers, args_route = {}, layer_dropout = 0.):
+        super().__init__()
+        assert all(len(route) == len(layers) for route in args_route.values()), 'each argument route map must have the same depth as the number of sequential layers'
+        self.layers = layers
+        self.args_route = args_route
+        self.layer_dropout = layer_dropout
+
+    def forward(self, x, **kwargs):
+        args = route_args(self.args_route, kwargs, len(self.layers))
+        layers_and_args = list(zip(self.layers, args))
+
+        for (f, g), (f_args, g_args) in layers_and_args:
+            x = x + f(x, **f_args)
+            x = x + g(x, **g_args)
+        return x
+
+
+class DivideMax(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        self.dim = dim
+
+    def forward(self, x):
+        maxes = x.amax(dim = self.dim, keepdim = True).detach()
+        return x / maxes
+
+
+# https://arxiv.org/abs/2103.17239
+class LayerScale(nn.Module):
+    def __init__(self, dim, depth, fn):
+        super().__init__()
+        if depth <= 18:
+            init_eps = 0.1
+        elif depth > 18 and depth <= 24:
+            init_eps = 1e-5
+        else:
+            init_eps = 1e-6
+
+        scale = torch.zeros(1, 1, dim).fill_(init_eps)
+        self.scale = nn.Parameter(scale)
+        self.fn = fn
+    def forward(self, x, **kwargs):
+        return self.fn(x, **kwargs) * self.scale
+
+# layer norm
+
+
+class PreNorm(nn.Module):
+    def __init__(self, dim, fn, sandwich = False):
+        super().__init__()
+        self.norm = nn.LayerNorm(dim)
+        self.norm_out = nn.LayerNorm(dim) if sandwich else nn.Identity()
+        self.fn = fn
+
+    def forward(self, x, **kwargs):
+        x = self.norm(x)
+        x = self.fn(x, **kwargs)
+        return self.norm_out(x)
+
+# feed forward
+
+
+class GEGLU(nn.Module):
+    def forward(self, x):
+        x, gates = x.chunk(2, dim = -1)
+        return x * F.gelu(gates)
+
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, dropout = 0., mult = 4.):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(dim, dim * mult * 2),
+            GEGLU(),
+            nn.Dropout(dropout),
+            nn.Linear(dim * mult, dim)
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+# Attention
+
+
+class Attention(nn.Module):
+    def __init__(self, dim, seq_len, causal = True, heads = 8, dim_head = 64, dropout = 0.):
+        super().__init__()
+        inner_dim = dim_head *  heads
+        self.heads = heads
+        self.seq_len = seq_len
+        self.scale = dim_head ** -0.5
+
+        self.causal = causal
+
+        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
+        self.to_out = nn.Sequential(
+            nn.Linear(inner_dim, dim),
+            nn.Dropout(dropout)
+        )
+
+    def forward(self, x, mask = None):
+        b, n, _, h, device = *x.shape, self.heads, x.device
+        softmax = torch.softmax
+
+        qkv = self.to_qkv(x).chunk(3, dim = -1)
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
+
+        q = q * self.scale
+
+        dots = torch.einsum('b h i d, b h j d -> b h i j', q, k)
+        mask_value = max_neg_value(dots)
+
+        if exists(mask):
+            mask = rearrange(mask, 'b j -> b () () j')
+            dots.masked_fill_(~mask, mask_value)
+            del mask
+
+        if self.causal:
+            i, j = dots.shape[-2:]
+            mask = torch.ones(i, j, device = device).triu_(j - i + 1).bool()
+            dots.masked_fill_(mask, mask_value)
+
+        attn = softmax(dots, dim=-1)
+
+        out = torch.einsum('b h i j, b h j d -> b h i d', attn, v)
+        out = rearrange(out, 'b h n d -> b n (h d)')
+        out = self.to_out(out)
+        return out
+
+
+# main transformer class
+class Transformer(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        depth,
+        seq_len,
+        causal = True,
+        heads = 8,
+        dim_head = 64,
+        ff_mult = 4,
+        attn_dropout = 0.,
+        ff_dropout = 0.,
+        sparse_attn = False,
+        sandwich_norm = False,
+    ):
+        super().__init__()
+        layers = nn.ModuleList([])
+        sparse_layer = cast_tuple(sparse_attn, depth)
+
+        for ind, sparse_attn in zip(range(depth), sparse_layer):
+            attn = Attention(dim, causal = causal, seq_len = seq_len, heads = heads, dim_head = dim_head, dropout = attn_dropout)
+
+            ff = FeedForward(dim, mult = ff_mult, dropout = ff_dropout)
+
+            layers.append(nn.ModuleList([
+                LayerScale(dim, ind + 1, PreNorm(dim, attn, sandwich = sandwich_norm)),
+                LayerScale(dim, ind + 1, PreNorm(dim, ff, sandwich = sandwich_norm))
+            ]))
+
+        execute_type = SequentialSequence
+        route_attn = ((True, False),) * depth
+        attn_route_map = {'mask': route_attn}
+
+        self.layers = execute_type(layers, args_route = attn_route_map)
+
+    def forward(self, x, **kwargs):
+        return self.layers(x, **kwargs)
diff --git a/tortoise/tortoise/models/vocoder.py b/tortoise/tortoise/models/vocoder.py
new file mode 100644
index 0000000..8b60dbd
--- /dev/null
+++ b/tortoise/tortoise/models/vocoder.py
@@ -0,0 +1,327 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+MAX_WAV_VALUE = 32768.0
+
+class KernelPredictor(torch.nn.Module):
+    ''' Kernel predictor for the location-variable convolutions'''
+
+    def __init__(
+            self,
+            cond_channels,
+            conv_in_channels,
+            conv_out_channels,
+            conv_layers,
+            conv_kernel_size=3,
+            kpnet_hidden_channels=64,
+            kpnet_conv_size=3,
+            kpnet_dropout=0.0,
+            kpnet_nonlinear_activation="LeakyReLU",
+            kpnet_nonlinear_activation_params={"negative_slope": 0.1},
+    ):
+        '''
+        Args:
+            cond_channels (int): number of channel for the conditioning sequence,
+            conv_in_channels (int): number of channel for the input sequence,
+            conv_out_channels (int): number of channel for the output sequence,
+            conv_layers (int): number of layers
+        '''
+        super().__init__()
+
+        self.conv_in_channels = conv_in_channels
+        self.conv_out_channels = conv_out_channels
+        self.conv_kernel_size = conv_kernel_size
+        self.conv_layers = conv_layers
+
+        kpnet_kernel_channels = conv_in_channels * conv_out_channels * conv_kernel_size * conv_layers  # l_w
+        kpnet_bias_channels = conv_out_channels * conv_layers  # l_b
+
+        self.input_conv = nn.Sequential(
+            nn.utils.weight_norm(nn.Conv1d(cond_channels, kpnet_hidden_channels, 5, padding=2, bias=True)),
+            getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
+        )
+
+        self.residual_convs = nn.ModuleList()
+        padding = (kpnet_conv_size - 1) // 2
+        for _ in range(3):
+            self.residual_convs.append(
+                nn.Sequential(
+                    nn.Dropout(kpnet_dropout),
+                    nn.utils.weight_norm(
+                        nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding,
+                                  bias=True)),
+                    getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
+                    nn.utils.weight_norm(
+                        nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding,
+                                  bias=True)),
+                    getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
+                )
+            )
+        self.kernel_conv = nn.utils.weight_norm(
+            nn.Conv1d(kpnet_hidden_channels, kpnet_kernel_channels, kpnet_conv_size, padding=padding, bias=True))
+        self.bias_conv = nn.utils.weight_norm(
+            nn.Conv1d(kpnet_hidden_channels, kpnet_bias_channels, kpnet_conv_size, padding=padding, bias=True))
+
+    def forward(self, c):
+        '''
+        Args:
+            c (Tensor): the conditioning sequence (batch, cond_channels, cond_length)
+        '''
+        batch, _, cond_length = c.shape
+        c = self.input_conv(c)
+        for residual_conv in self.residual_convs:
+            residual_conv.to(c.device)
+            c = c + residual_conv(c)
+        k = self.kernel_conv(c)
+        b = self.bias_conv(c)
+        kernels = k.contiguous().view(
+            batch,
+            self.conv_layers,
+            self.conv_in_channels,
+            self.conv_out_channels,
+            self.conv_kernel_size,
+            cond_length,
+        )
+        bias = b.contiguous().view(
+            batch,
+            self.conv_layers,
+            self.conv_out_channels,
+            cond_length,
+        )
+
+        return kernels, bias
+
+    def remove_weight_norm(self):
+        nn.utils.remove_weight_norm(self.input_conv[0])
+        nn.utils.remove_weight_norm(self.kernel_conv)
+        nn.utils.remove_weight_norm(self.bias_conv)
+        for block in self.residual_convs:
+            nn.utils.remove_weight_norm(block[1])
+            nn.utils.remove_weight_norm(block[3])
+
+
+class LVCBlock(torch.nn.Module):
+    '''the location-variable convolutions'''
+
+    def __init__(
+            self,
+            in_channels,
+            cond_channels,
+            stride,
+            dilations=[1, 3, 9, 27],
+            lReLU_slope=0.2,
+            conv_kernel_size=3,
+            cond_hop_length=256,
+            kpnet_hidden_channels=64,
+            kpnet_conv_size=3,
+            kpnet_dropout=0.0,
+    ):
+        super().__init__()
+
+        self.cond_hop_length = cond_hop_length
+        self.conv_layers = len(dilations)
+        self.conv_kernel_size = conv_kernel_size
+
+        self.kernel_predictor = KernelPredictor(
+            cond_channels=cond_channels,
+            conv_in_channels=in_channels,
+            conv_out_channels=2 * in_channels,
+            conv_layers=len(dilations),
+            conv_kernel_size=conv_kernel_size,
+            kpnet_hidden_channels=kpnet_hidden_channels,
+            kpnet_conv_size=kpnet_conv_size,
+            kpnet_dropout=kpnet_dropout,
+            kpnet_nonlinear_activation_params={"negative_slope": lReLU_slope}
+        )
+
+        self.convt_pre = nn.Sequential(
+            nn.LeakyReLU(lReLU_slope),
+            nn.utils.weight_norm(nn.ConvTranspose1d(in_channels, in_channels, 2 * stride, stride=stride,
+                                                    padding=stride // 2 + stride % 2, output_padding=stride % 2)),
+        )
+
+        self.conv_blocks = nn.ModuleList()
+        for dilation in dilations:
+            self.conv_blocks.append(
+                nn.Sequential(
+                    nn.LeakyReLU(lReLU_slope),
+                    nn.utils.weight_norm(nn.Conv1d(in_channels, in_channels, conv_kernel_size,
+                                                   padding=dilation * (conv_kernel_size - 1) // 2, dilation=dilation)),
+                    nn.LeakyReLU(lReLU_slope),
+                )
+            )
+
+    def forward(self, x, c):
+        ''' forward propagation of the location-variable convolutions.
+        Args:
+            x (Tensor): the input sequence (batch, in_channels, in_length)
+            c (Tensor): the conditioning sequence (batch, cond_channels, cond_length)
+
+        Returns:
+            Tensor: the output sequence (batch, in_channels, in_length)
+        '''
+        _, in_channels, _ = x.shape  # (B, c_g, L')
+
+        x = self.convt_pre(x)  # (B, c_g, stride * L')
+        kernels, bias = self.kernel_predictor(c)
+
+        for i, conv in enumerate(self.conv_blocks):
+            output = conv(x)  # (B, c_g, stride * L')
+
+            k = kernels[:, i, :, :, :, :]  # (B, 2 * c_g, c_g, kernel_size, cond_length)
+            b = bias[:, i, :, :]  # (B, 2 * c_g, cond_length)
+
+            output = self.location_variable_convolution(output, k, b,
+                                                        hop_size=self.cond_hop_length)  # (B, 2 * c_g, stride * L'): LVC
+            x = x + torch.sigmoid(output[:, :in_channels, :]) * torch.tanh(
+                output[:, in_channels:, :])  # (B, c_g, stride * L'): GAU
+
+        return x
+
+    def location_variable_convolution(self, x, kernel, bias, dilation=1, hop_size=256):
+        ''' perform location-variable convolution operation on the input sequence (x) using the local convolution kernl.
+        Time: 414 μs ± 309 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each), test on NVIDIA V100.
+        Args:
+            x (Tensor): the input sequence (batch, in_channels, in_length).
+            kernel (Tensor): the local convolution kernel (batch, in_channel, out_channels, kernel_size, kernel_length)
+            bias (Tensor): the bias for the local convolution (batch, out_channels, kernel_length)
+            dilation (int): the dilation of convolution.
+            hop_size (int): the hop_size of the conditioning sequence.
+        Returns:
+            (Tensor): the output sequence after performing local convolution. (batch, out_channels, in_length).
+        '''
+        batch, _, in_length = x.shape
+        batch, _, out_channels, kernel_size, kernel_length = kernel.shape
+        assert in_length == (kernel_length * hop_size), "length of (x, kernel) is not matched"
+
+        padding = dilation * int((kernel_size - 1) / 2)
+        x = F.pad(x, (padding, padding), 'constant', 0)  # (batch, in_channels, in_length + 2*padding)
+        x = x.unfold(2, hop_size + 2 * padding, hop_size)  # (batch, in_channels, kernel_length, hop_size + 2*padding)
+
+        if hop_size < dilation:
+            x = F.pad(x, (0, dilation), 'constant', 0)
+        x = x.unfold(3, dilation,
+                     dilation)  # (batch, in_channels, kernel_length, (hop_size + 2*padding)/dilation, dilation)
+        x = x[:, :, :, :, :hop_size]
+        x = x.transpose(3, 4)  # (batch, in_channels, kernel_length, dilation, (hop_size + 2*padding)/dilation)
+        x = x.unfold(4, kernel_size, 1)  # (batch, in_channels, kernel_length, dilation, _, kernel_size)
+
+        o = torch.einsum('bildsk,biokl->bolsd', x, kernel)
+        o = o.to(memory_format=torch.channels_last_3d)
+        bias = bias.unsqueeze(-1).unsqueeze(-1).to(memory_format=torch.channels_last_3d)
+        o = o + bias
+        o = o.contiguous().view(batch, out_channels, -1)
+
+        return o
+
+    def remove_weight_norm(self):
+        self.kernel_predictor.remove_weight_norm()
+        nn.utils.remove_weight_norm(self.convt_pre[1])
+        for block in self.conv_blocks:
+            nn.utils.remove_weight_norm(block[1])
+
+
+class UnivNetGenerator(nn.Module):
+    """
+    UnivNet Generator
+    
+    Originally from https://github.com/mindslab-ai/univnet/blob/master/model/generator.py.
+    """
+
+    def __init__(self, noise_dim=64, channel_size=32, dilations=[1,3,9,27], strides=[8,8,4], lReLU_slope=.2, kpnet_conv_size=3,
+                 # Below are MEL configurations options that this generator requires.
+                 hop_length=256, n_mel_channels=100):
+        super(UnivNetGenerator, self).__init__()
+        self.mel_channel = n_mel_channels
+        self.noise_dim = noise_dim
+        self.hop_length = hop_length
+        channel_size = channel_size
+        kpnet_conv_size = kpnet_conv_size
+
+        self.res_stack = nn.ModuleList()
+        hop_length = 1
+        for stride in strides:
+            hop_length = stride * hop_length
+            self.res_stack.append(
+                LVCBlock(
+                    channel_size,
+                    n_mel_channels,
+                    stride=stride,
+                    dilations=dilations,
+                    lReLU_slope=lReLU_slope,
+                    cond_hop_length=hop_length,
+                    kpnet_conv_size=kpnet_conv_size
+                )
+            )
+
+        self.conv_pre = \
+            nn.utils.weight_norm(nn.Conv1d(noise_dim, channel_size, 7, padding=3, padding_mode='reflect'))
+
+        self.conv_post = nn.Sequential(
+            nn.LeakyReLU(lReLU_slope),
+            nn.utils.weight_norm(nn.Conv1d(channel_size, 1, 7, padding=3, padding_mode='reflect')),
+            nn.Tanh(),
+        )
+
+    def forward(self, c, z):
+        '''
+        Args:
+            c (Tensor): the conditioning sequence of mel-spectrogram (batch, mel_channels, in_length)
+            z (Tensor): the noise sequence (batch, noise_dim, in_length)
+
+        '''
+        z = self.conv_pre(z)  # (B, c_g, L)
+
+        for res_block in self.res_stack:
+            res_block.to(z.device)
+            z = res_block(z, c)  # (B, c_g, L * s_0 * ... * s_i)
+
+        z = self.conv_post(z)  # (B, 1, L * 256)
+
+        return z
+
+    def eval(self, inference=False):
+        super(UnivNetGenerator, self).eval()
+        # don't remove weight norm while validation in training loop
+        if inference:
+            self.remove_weight_norm()
+
+    def remove_weight_norm(self):
+        nn.utils.remove_weight_norm(self.conv_pre)
+
+        for layer in self.conv_post:
+            if len(layer.state_dict()) != 0:
+                nn.utils.remove_weight_norm(layer)
+
+        for res_block in self.res_stack:
+            res_block.remove_weight_norm()
+
+    def inference(self, c, z=None):
+        # pad input mel with zeros to cut artifact
+        # see https://github.com/seungwonpark/melgan/issues/8
+        zero = torch.full((c.shape[0], self.mel_channel, 10), -11.5129).to(c.device)
+        mel = torch.cat((c, zero), dim=2)
+
+        if z is None:
+            z = torch.randn(c.shape[0], self.noise_dim, mel.size(2)).to(mel.device)
+
+        audio = self.forward(mel, z)
+        audio = audio[:, :, :-(self.hop_length * 10)]
+        audio = audio.clamp(min=-1, max=1)
+        return audio
+
+
+if __name__ == '__main__':
+    model = UnivNetGenerator()
+
+    c = torch.randn(3, 100, 10)
+    z = torch.randn(3, 64, 10)
+    print(c.shape)
+
+    y = model(c, z)
+    print(y.shape)
+    assert y.shape == torch.Size([3, 1, 2560])
+
+    pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+    print(pytorch_total_params)
diff --git a/tortoise/tortoise/models/xtransformers.py b/tortoise/tortoise/models/xtransformers.py
new file mode 100644
index 0000000..8be2df4
--- /dev/null
+++ b/tortoise/tortoise/models/xtransformers.py
@@ -0,0 +1,1248 @@
+import math
+from collections import namedtuple
+from functools import partial
+from inspect import isfunction
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange, repeat
+from torch import nn, einsum
+
+DEFAULT_DIM_HEAD = 64
+
+Intermediates = namedtuple('Intermediates', [
+    'pre_softmax_attn',
+    'post_softmax_attn'
+])
+
+LayerIntermediates = namedtuple('Intermediates', [
+    'hiddens',
+    'attn_intermediates',
+    'past_key_values',
+])
+
+
+# helpers
+
+def exists(val):
+    return val is not None
+
+
+def default(val, d):
+    if exists(val):
+        return val
+    return d() if isfunction(d) else d
+
+
+def cast_tuple(val, depth):
+    return val if isinstance(val, tuple) else (val,) * depth
+
+
+class always():
+    def __init__(self, val):
+        self.val = val
+
+    def __call__(self, *args, **kwargs):
+        return self.val
+
+
+class not_equals():
+    def __init__(self, val):
+        self.val = val
+
+    def __call__(self, x, *args, **kwargs):
+        return x != self.val
+
+
+class equals():
+    def __init__(self, val):
+        self.val = val
+
+    def __call__(self, x, *args, **kwargs):
+        return x == self.val
+
+
+def max_neg_value(tensor):
+    return -torch.finfo(tensor.dtype).max
+
+
+def l2norm(t):
+    return F.normalize(t, p=2, dim=-1)
+
+
+# init helpers
+
+def init_zero_(layer):
+    nn.init.constant_(layer.weight, 0.)
+    if exists(layer.bias):
+        nn.init.constant_(layer.bias, 0.)
+
+
+# keyword argument helpers
+
+def pick_and_pop(keys, d):
+    values = list(map(lambda key: d.pop(key), keys))
+    return dict(zip(keys, values))
+
+
+def group_dict_by_key(cond, d):
+    return_val = [dict(), dict()]
+    for key in d.keys():
+        match = bool(cond(key))
+        ind = int(not match)
+        return_val[ind][key] = d[key]
+    return (*return_val,)
+
+
+def string_begins_with(prefix, str):
+    return str.startswith(prefix)
+
+
+def group_by_key_prefix(prefix, d):
+    return group_dict_by_key(partial(string_begins_with, prefix), d)
+
+
+def groupby_prefix_and_trim(prefix, d):
+    kwargs_with_prefix, kwargs = group_dict_by_key(partial(string_begins_with, prefix), d)
+    kwargs_without_prefix = dict(map(lambda x: (x[0][len(prefix):], x[1]), tuple(kwargs_with_prefix.items())))
+    return kwargs_without_prefix, kwargs
+
+
+# activations
+
+class ReluSquared(nn.Module):
+    def forward(self, x):
+        return F.relu(x) ** 2
+
+
+# positional embeddings
+
+class AbsolutePositionalEmbedding(nn.Module):
+    def __init__(self, dim, max_seq_len):
+        super().__init__()
+        self.scale = dim ** -0.5
+        self.emb = nn.Embedding(max_seq_len, dim)
+
+    def forward(self, x):
+        n = torch.arange(x.shape[1], device=x.device)
+        pos_emb = self.emb(n)
+        pos_emb = rearrange(pos_emb, 'n d -> () n d')
+        return pos_emb * self.scale
+
+
+class FixedPositionalEmbedding(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer('inv_freq', inv_freq)
+
+    def forward(self, x, seq_dim=1, offset=0):
+        t = torch.arange(x.shape[seq_dim], device=x.device).type_as(self.inv_freq) + offset
+        sinusoid_inp = torch.einsum('i , j -> i j', t, self.inv_freq)
+        emb = torch.cat((sinusoid_inp.sin(), sinusoid_inp.cos()), dim=-1)
+        return rearrange(emb, 'n d -> () n d')
+
+
+class RelativePositionBias(nn.Module):
+    def __init__(self, scale, causal=False, num_buckets=32, max_distance=128, heads=8):
+        super().__init__()
+        self.scale = scale
+        self.causal = causal
+        self.num_buckets = num_buckets
+        self.max_distance = max_distance
+        self.relative_attention_bias = nn.Embedding(num_buckets, heads)
+
+    @staticmethod
+    def _relative_position_bucket(relative_position, causal=True, num_buckets=32, max_distance=128):
+        ret = 0
+        n = -relative_position
+        if not causal:
+            num_buckets //= 2
+            ret += (n < 0).long() * num_buckets
+            n = torch.abs(n)
+        else:
+            n = torch.max(n, torch.zeros_like(n))
+
+        max_exact = num_buckets // 2
+        is_small = n < max_exact
+
+        val_if_large = max_exact + (
+                torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
+        ).long()
+        val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
+
+        ret += torch.where(is_small, n, val_if_large)
+        return ret
+
+    def forward(self, qk_dots):
+        i, j, device = *qk_dots.shape[-2:], qk_dots.device
+        q_pos = torch.arange(i, dtype=torch.long, device=device)
+        k_pos = torch.arange(j, dtype=torch.long, device=device)
+        rel_pos = k_pos[None, :] - q_pos[:, None]
+        rp_bucket = self._relative_position_bucket(rel_pos, causal=self.causal, num_buckets=self.num_buckets,
+                                                   max_distance=self.max_distance)
+        values = self.relative_attention_bias(rp_bucket)
+        bias = rearrange(values, 'i j h -> () h i j')
+        return qk_dots + (bias * self.scale)
+
+
+class AlibiPositionalBias(nn.Module):
+    def __init__(self, heads, **kwargs):
+        super().__init__()
+        self.heads = heads
+        slopes = torch.Tensor(self._get_slopes(heads))
+        slopes = rearrange(slopes, 'h -> () h () ()')
+        self.register_buffer('slopes', slopes, persistent=False)
+        self.register_buffer('bias', None, persistent=False)
+
+    @staticmethod
+    def _get_slopes(heads):
+        def get_slopes_power_of_2(n):
+            start = (2 ** (-2 ** -(math.log2(n) - 3)))
+            ratio = start
+            return [start * ratio ** i for i in range(n)]
+
+        if math.log2(heads).is_integer():
+            return get_slopes_power_of_2(heads)
+
+        closest_power_of_2 = 2 ** math.floor(math.log2(heads))
+        return get_slopes_power_of_2(closest_power_of_2) + get_slopes_power_of_2(2 * closest_power_of_2)[0::2][
+                                                           :heads - closest_power_of_2]
+
+    def forward(self, qk_dots):
+        h, i, j, device = *qk_dots.shape[-3:], qk_dots.device
+
+        if exists(self.bias) and self.bias.shape[-1] >= j:
+            return qk_dots + self.bias[..., :j]
+
+        bias = torch.arange(j, device=device)
+        bias = rearrange(bias, 'j -> () () () j')
+        bias = bias * self.slopes
+
+        num_heads_unalibied = h - bias.shape[1]
+        bias = F.pad(bias, (0, 0, 0, 0, 0, num_heads_unalibied))
+
+        self.register_buffer('bias', bias, persistent=False)
+        return qk_dots + self.bias
+
+
+class LearnedAlibiPositionalBias(AlibiPositionalBias):
+    def __init__(self, heads, bidirectional=False):
+        super().__init__(heads)
+        los_slopes = torch.log(self.slopes)
+        self.learned_logslopes = nn.Parameter(los_slopes)
+
+        self.bidirectional = bidirectional
+        if self.bidirectional:
+            self.learned_logslopes_future = nn.Parameter(los_slopes)
+
+    def forward(self, qk_dots):
+        h, i, j, device = *qk_dots.shape[-3:], qk_dots.device
+
+        def get_slopes(param):
+            return F.pad(param.exp(), (0, 0, 0, 0, 0, h - param.shape[1]))
+
+        if exists(self.bias) and self.bias.shape[-1] >= j:
+            bias = self.bias[..., :i, :j]
+        else:
+            i_arange = torch.arange(i, device=device)
+            j_arange = torch.arange(j, device=device)
+            bias = rearrange(j_arange, 'j -> 1 1 1 j') - rearrange(i_arange, 'i -> 1 1 i 1')
+            self.register_buffer('bias', bias, persistent=False)
+
+        if self.bidirectional:
+            past_slopes = get_slopes(self.learned_logslopes)
+            future_slopes = get_slopes(self.learned_logslopes_future)
+            bias = torch.tril(bias * past_slopes) + torch.triu(bias * future_slopes)
+        else:
+            slopes = get_slopes(self.learned_logslopes)
+            bias = bias * slopes
+
+        return qk_dots + bias
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer('inv_freq', inv_freq)
+
+    def forward(self, max_seq_len, device):
+        t = torch.arange(max_seq_len, device=device).type_as(self.inv_freq)
+        freqs = torch.einsum('i , j -> i j', t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        return rearrange(emb, 'n d -> () () n d')
+
+
+def rotate_half(x):
+    x = rearrange(x, '... (j d) -> ... j d', j=2)
+    x1, x2 = x.unbind(dim=-2)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(t, freqs):
+    seq_len = t.shape[-2]
+    freqs = freqs[:, :, -seq_len:]
+    return (t * freqs.cos()) + (rotate_half(t) * freqs.sin())
+
+
+# norms
+
+class Scale(nn.Module):
+    def __init__(self, value, fn):
+        super().__init__()
+        self.value = value
+        self.fn = fn
+
+    def forward(self, x, **kwargs):
+        out = self.fn(x, **kwargs)
+        scale_fn = lambda t: t * self.value
+
+        if not isinstance(out, tuple):
+            return scale_fn(out)
+
+        return (scale_fn(out[0]), *out[1:])
+
+
+class Rezero(nn.Module):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+        self.g = nn.Parameter(torch.zeros(1))
+
+    def forward(self, x, **kwargs):
+        out = self.fn(x, **kwargs)
+        rezero_fn = lambda t: t * self.g
+
+        if not isinstance(out, tuple):
+            return rezero_fn(out)
+
+        return (rezero_fn(out[0]), *out[1:])
+
+
+class ScaleNorm(nn.Module):
+    def __init__(self, dim, eps=1e-5):
+        super().__init__()
+        self.scale = dim ** -0.5
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(1))
+
+    def forward(self, x):
+        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
+        return x / norm.clamp(min=self.eps) * self.g
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-8):
+        super().__init__()
+        self.scale = dim ** -0.5
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(dim))
+
+    def forward(self, x):
+        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
+        return x / norm.clamp(min=self.eps) * self.g
+
+
+class RMSScaleShiftNorm(nn.Module):
+    def __init__(self, dim, eps=1e-8):
+        super().__init__()
+        self.scale = dim ** -0.5
+        self.eps = eps
+        self.g = nn.Parameter(torch.ones(dim))
+        self.scale_shift_process = nn.Linear(dim * 2, dim * 2)
+
+    def forward(self, x, norm_scale_shift_inp):
+        norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
+        norm = x / norm.clamp(min=self.eps) * self.g
+
+        ss_emb = self.scale_shift_process(norm_scale_shift_inp)
+        scale, shift = torch.chunk(ss_emb, 2, dim=1)
+        h = norm * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+        return h
+
+
+# residual and residual gates
+
+class Residual(nn.Module):
+    def __init__(self, dim, scale_residual=False):
+        super().__init__()
+        self.residual_scale = nn.Parameter(torch.ones(dim)) if scale_residual else None
+
+    def forward(self, x, residual):
+        if exists(self.residual_scale):
+            residual = residual * self.residual_scale
+
+        return x + residual
+
+
+class GRUGating(nn.Module):
+    def __init__(self, dim, scale_residual=False):
+        super().__init__()
+        self.gru = nn.GRUCell(dim, dim)
+        self.residual_scale = nn.Parameter(torch.ones(dim)) if scale_residual else None
+
+    def forward(self, x, residual):
+        if exists(self.residual_scale):
+            residual = residual * self.residual_scale
+
+        gated_output = self.gru(
+            rearrange(x, 'b n d -> (b n) d'),
+            rearrange(residual, 'b n d -> (b n) d')
+        )
+
+        return gated_output.reshape_as(x)
+
+
+# token shifting
+
+def shift(t, amount, mask=None):
+    if amount == 0:
+        return t
+
+    if exists(mask):
+        t = t.masked_fill(~mask[..., None], 0.)
+
+    return F.pad(t, (0, 0, amount, -amount), value=0.)
+
+
+class ShiftTokens(nn.Module):
+    def __init__(self, shifts, fn):
+        super().__init__()
+        self.fn = fn
+        self.shifts = tuple(shifts)
+
+    def forward(self, x, **kwargs):
+        mask = kwargs.get('mask', None)
+        shifts = self.shifts
+        segments = len(shifts)
+        feats_per_shift = x.shape[-1] // segments
+        splitted = x.split(feats_per_shift, dim=-1)
+        segments_to_shift, rest = splitted[:segments], splitted[segments:]
+        segments_to_shift = list(map(lambda args: shift(*args, mask=mask), zip(segments_to_shift, shifts)))
+        x = torch.cat((*segments_to_shift, *rest), dim=-1)
+        return self.fn(x, **kwargs)
+
+
+# feedforward
+
+class GLU(nn.Module):
+    def __init__(self, dim_in, dim_out, activation):
+        super().__init__()
+        self.act = activation
+        self.proj = nn.Linear(dim_in, dim_out * 2)
+
+    def forward(self, x):
+        x, gate = self.proj(x).chunk(2, dim=-1)
+        return x * self.act(gate)
+
+
+class FeedForward(nn.Module):
+    def __init__(
+            self,
+            dim,
+            dim_out=None,
+            mult=4,
+            glu=False,
+            relu_squared=False,
+            post_act_ln=False,
+            dropout=0.,
+            zero_init_output=False
+    ):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = default(dim_out, dim)
+        activation = ReluSquared() if relu_squared else nn.GELU()
+
+        project_in = nn.Sequential(
+            nn.Linear(dim, inner_dim),
+            activation
+        ) if not glu else GLU(dim, inner_dim, activation)
+
+        self.net = nn.Sequential(
+            project_in,
+            nn.LayerNorm(inner_dim) if post_act_ln else nn.Identity(),
+            nn.Dropout(dropout),
+            nn.Linear(inner_dim, dim_out)
+        )
+
+        # init last linear layer to 0
+        if zero_init_output:
+            init_zero_(self.net[-1])
+
+    def forward(self, x):
+        return self.net(x)
+
+
+# attention.
+
+class Attention(nn.Module):
+    def __init__(
+            self,
+            dim,
+            dim_head=DEFAULT_DIM_HEAD,
+            heads=8,
+            causal=False,
+            talking_heads=False,
+            head_scale=False,
+            collab_heads=False,
+            collab_compression=.3,
+            sparse_topk=None,
+            use_entmax15=False,
+            num_mem_kv=0,
+            dropout=0.,
+            on_attn=False,
+            gate_values=False,
+            zero_init_output=False,
+            max_attend_past=None,
+            qk_norm=False,
+            scale_init_value=None,
+            rel_pos_bias=False,
+            rel_pos_num_buckets=32,
+            rel_pos_max_distance=128,
+    ):
+        super().__init__()
+        self.scale = dim_head ** -0.5
+
+        self.heads = heads
+        self.causal = causal
+        self.max_attend_past = max_attend_past
+
+        qk_dim = v_dim = dim_head * heads
+
+        # collaborative heads
+        self.collab_heads = collab_heads
+        if self.collab_heads:
+            qk_dim = int(collab_compression * qk_dim)
+            self.collab_mixing = nn.Parameter(torch.randn(heads, qk_dim))
+
+        self.to_q = nn.Linear(dim, qk_dim, bias=False)
+        self.to_k = nn.Linear(dim, qk_dim, bias=False)
+        self.to_v = nn.Linear(dim, v_dim, bias=False)
+
+        self.dropout = nn.Dropout(dropout)
+
+        # add GLU gating for aggregated values, from alphafold2
+        self.to_v_gate = None
+        if gate_values:
+            self.to_v_gate = nn.Linear(dim, v_dim)
+            nn.init.constant_(self.to_v_gate.weight, 0)
+            nn.init.constant_(self.to_v_gate.bias, 1)
+
+        # cosine sim attention
+        self.qk_norm = qk_norm
+        if qk_norm:
+            scale_init_value = default(scale_init_value,
+                                       -3)  # if not provided, initialize as though it were sequence length of 1024
+            self.scale = nn.Parameter(torch.ones(1, heads, 1, 1) * scale_init_value)
+
+        # talking heads
+        self.talking_heads = talking_heads
+        if talking_heads:
+            self.pre_softmax_proj = nn.Parameter(torch.randn(heads, heads))
+            self.post_softmax_proj = nn.Parameter(torch.randn(heads, heads))
+
+        # head scaling
+        self.head_scale = head_scale
+        if head_scale:
+            self.head_scale_params = nn.Parameter(torch.ones(1, heads, 1, 1))
+
+        # explicit topk sparse attention
+        self.sparse_topk = sparse_topk
+
+        # entmax
+        self.attn_fn = F.softmax
+
+        # add memory key / values
+        self.num_mem_kv = num_mem_kv
+        if num_mem_kv > 0:
+            self.mem_k = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
+            self.mem_v = nn.Parameter(torch.randn(heads, num_mem_kv, dim_head))
+
+        # attention on attention
+        self.attn_on_attn = on_attn
+        self.to_out = nn.Sequential(nn.Linear(v_dim, dim * 2), nn.GLU()) if on_attn else nn.Linear(v_dim, dim)
+
+        self.rel_pos_bias = rel_pos_bias
+        if rel_pos_bias:
+            assert rel_pos_num_buckets <= rel_pos_max_distance, 'number of relative position buckets must be less than the relative position max distance'
+            self.rel_pos = RelativePositionBias(scale=dim_head ** 0.5, causal=causal, heads=heads,
+                                                num_buckets=rel_pos_num_buckets, max_distance=rel_pos_max_distance)
+
+        # init output projection 0
+        if zero_init_output:
+            init_zero_(self.to_out)
+
+    def forward(
+            self,
+            x,
+            context=None,
+            mask=None,
+            context_mask=None,
+            attn_mask=None,
+            sinusoidal_emb=None,
+            rotary_pos_emb=None,
+            prev_attn=None,
+            mem=None,
+            layer_past=None,
+    ):
+        b, n, _, h, talking_heads, collab_heads, head_scale, scale, device, has_context = *x.shape, self.heads, self.talking_heads, self.collab_heads, self.head_scale, self.scale, x.device, exists(
+            context)
+        kv_input = default(context, x)
+
+        q_input = x
+        k_input = kv_input
+        v_input = kv_input
+
+        if exists(mem):
+            k_input = torch.cat((mem, k_input), dim=-2)
+            v_input = torch.cat((mem, v_input), dim=-2)
+
+        if exists(sinusoidal_emb):
+            # in shortformer, the query would start at a position offset depending on the past cached memory
+            offset = k_input.shape[-2] - q_input.shape[-2]
+            q_input = q_input + sinusoidal_emb(q_input, offset=offset)
+            k_input = k_input + sinusoidal_emb(k_input)
+
+        q = self.to_q(q_input)
+        k = self.to_k(k_input)
+        v = self.to_v(v_input)
+
+        if not collab_heads:
+            q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), (q, k, v))
+        else:
+            q = einsum('b i d, h d -> b h i d', q, self.collab_mixing)
+            k = rearrange(k, 'b n d -> b () n d')
+            v = rearrange(v, 'b n (h d) -> b h n d', h=h)
+
+        if layer_past is not None:
+            past_key, past_value = layer_past
+            k = torch.cat([past_key, k], dim=-2)
+            v = torch.cat([past_value, v], dim=-2)
+        k_cache = k
+        v_cache = v
+
+        if exists(rotary_pos_emb) and not has_context:
+            l = rotary_pos_emb.shape[-1]
+            (ql, qr), (kl, kr), (vl, vr) = map(lambda t: (t[..., :l], t[..., l:]), (q, k, v))
+            ql, kl, vl = map(lambda t: apply_rotary_pos_emb(t, rotary_pos_emb), (ql, kl, vl))
+            q, k, v = map(lambda t: torch.cat(t, dim=-1), ((ql, qr), (kl, kr), (vl, vr)))
+
+        input_mask = None
+        if any(map(exists, (mask, context_mask))):
+            q_mask = default(mask, lambda: torch.ones((b, n), device=device).bool())
+            k_mask = q_mask if not exists(context) else context_mask
+            k_mask = default(k_mask, lambda: torch.ones((b, k.shape[-2]), device=device).bool())
+            q_mask = rearrange(q_mask, 'b i -> b () i ()')
+            k_mask = rearrange(k_mask, 'b j -> b () () j')
+            input_mask = q_mask * k_mask
+
+        if self.num_mem_kv > 0:
+            mem_k, mem_v = map(lambda t: repeat(t, 'h n d -> b h n d', b=b), (self.mem_k, self.mem_v))
+            k = torch.cat((mem_k, k), dim=-2)
+            v = torch.cat((mem_v, v), dim=-2)
+            if exists(input_mask):
+                input_mask = F.pad(input_mask, (self.num_mem_kv, 0), value=True)
+
+        if collab_heads:
+            k = k.expand(-1, h, -1, -1)
+
+        if self.qk_norm:
+            q, k = map(l2norm, (q, k))
+            scale = 1 / (self.scale.exp().clamp(min=1e-2))
+
+        dots = einsum('b h i d, b h j d -> b h i j', q, k) * scale
+        mask_value = max_neg_value(dots)
+
+        if exists(prev_attn):
+            dots = dots + prev_attn
+
+        pre_softmax_attn = dots.clone()
+
+        if talking_heads:
+            dots = einsum('b h i j, h k -> b k i j', dots, self.pre_softmax_proj).contiguous()
+
+        if self.rel_pos_bias:
+            dots = self.rel_pos(dots)
+
+        if exists(input_mask):
+            dots.masked_fill_(~input_mask, mask_value)
+            del input_mask
+
+        if exists(attn_mask):
+            assert 2 <= attn_mask.ndim <= 4, 'attention mask must have greater than 2 dimensions but less than or equal to 4'
+            if attn_mask.ndim == 2:
+                attn_mask = rearrange(attn_mask, 'i j -> () () i j')
+            elif attn_mask.ndim == 3:
+                attn_mask = rearrange(attn_mask, 'h i j -> () h i j')
+            dots.masked_fill_(~attn_mask, mask_value)
+
+        if exists(self.max_attend_past):
+            i, j = dots.shape[-2:]
+            range_q = torch.arange(j - i, j, device=device)
+            range_k = torch.arange(j, device=device)
+            dist = rearrange(range_q, 'i -> () () i ()') - rearrange(range_k, 'j -> () () () j')
+            mask = dist > self.max_attend_past
+            dots.masked_fill_(mask, mask_value)
+            del mask
+
+        if self.causal:
+            i, j = dots.shape[-2:]
+            r = torch.arange(i, device=device)
+            mask = rearrange(r, 'i -> () () i ()') < rearrange(r, 'j -> () () () j')
+            mask = F.pad(mask, (j - i, 0), value=False)
+            dots.masked_fill_(mask, mask_value)
+            del mask
+
+        if exists(self.sparse_topk) and self.sparse_topk < dots.shape[-1]:
+            top, _ = dots.topk(self.sparse_topk, dim=-1)
+            vk = top[..., -1].unsqueeze(-1).expand_as(dots)
+            mask = dots < vk
+            dots.masked_fill_(mask, mask_value)
+            del mask
+
+        attn = self.attn_fn(dots, dim=-1)
+        post_softmax_attn = attn.clone()
+
+        attn = self.dropout(attn)
+
+        if talking_heads:
+            attn = einsum('b h i j, h k -> b k i j', attn, self.post_softmax_proj).contiguous()
+
+        out = einsum('b h i j, b h j d -> b h i d', attn, v)
+
+        if head_scale:
+            out = out * self.head_scale_params
+
+        out = rearrange(out, 'b h n d -> b n (h d)')
+
+        if exists(self.to_v_gate):
+            gates = self.to_v_gate(x)
+            out = out * gates.sigmoid()
+
+        intermediates = Intermediates(
+            pre_softmax_attn=pre_softmax_attn,
+            post_softmax_attn=post_softmax_attn
+        )
+
+        return self.to_out(out), intermediates, k_cache, v_cache
+
+
+class AttentionLayers(nn.Module):
+    def __init__(
+            self,
+            dim,
+            depth,
+            heads=8,
+            causal=False,
+            cross_attend=False,
+            only_cross=False,
+            use_scalenorm=False,
+            use_rms_scaleshift_norm=False,
+            use_rmsnorm=False,
+            use_rezero=False,
+            alibi_pos_bias=False,
+            alibi_num_heads=None,
+            alibi_learned=False,
+            position_infused_attn=False,
+            rotary_pos_emb=False,
+            rotary_emb_dim=None,
+            custom_layers=None,
+            sandwich_coef=None,
+            par_ratio=None,
+            residual_attn=False,
+            cross_residual_attn=False,
+            macaron=False,
+            pre_norm=True,
+            gate_residual=False,
+            scale_residual=False,
+            shift_tokens=0,
+            sandwich_norm=False,
+            use_qk_norm_attn=False,
+            qk_norm_attn_seq_len=None,
+            zero_init_branch_output=False,
+            **kwargs
+    ):
+        super().__init__()
+        ff_kwargs, kwargs = groupby_prefix_and_trim('ff_', kwargs)
+        attn_kwargs, _ = groupby_prefix_and_trim('attn_', kwargs)
+
+        dim_head = attn_kwargs.get('dim_head', DEFAULT_DIM_HEAD)
+
+        self.dim = dim
+        self.depth = depth
+        self.layers = nn.ModuleList([])
+        self.causal = causal
+
+        rel_pos_bias = 'rel_pos_bias' in attn_kwargs
+        self.has_pos_emb = position_infused_attn or rel_pos_bias or rotary_pos_emb
+        self.pia_pos_emb = FixedPositionalEmbedding(dim) if position_infused_attn else None
+
+        rotary_emb_dim = max(default(rotary_emb_dim, dim_head // 2), 32)
+        self.rotary_pos_emb = RotaryEmbedding(rotary_emb_dim) if rotary_pos_emb else None
+
+        assert not (
+                    alibi_pos_bias and rel_pos_bias), 'you can only choose Alibi positional bias or T5 relative positional bias, not both'
+
+        if alibi_pos_bias:
+            alibi_num_heads = default(alibi_num_heads, heads)
+            assert alibi_num_heads <= heads, 'number of ALiBi heads must be less than the total number of heads'
+            alibi_pos_klass = LearnedAlibiPositionalBias if alibi_learned or not causal else AlibiPositionalBias
+            self.rel_pos = alibi_pos_klass(heads=alibi_num_heads, bidirectional=not causal)
+        else:
+            self.rel_pos = None
+
+        assert not (not pre_norm and sandwich_norm), 'sandwich norm cannot be used when not using prenorm'
+        self.pre_norm = pre_norm
+        self.sandwich_norm = sandwich_norm
+
+        self.residual_attn = residual_attn
+        self.cross_residual_attn = cross_residual_attn
+        self.cross_attend = cross_attend
+
+        norm_class = ScaleNorm if use_scalenorm else nn.LayerNorm
+        norm_class = RMSNorm if use_rmsnorm else norm_class
+        norm_class = RMSScaleShiftNorm if use_rms_scaleshift_norm else norm_class
+        norm_fn = partial(norm_class, dim)
+
+        norm_fn = nn.Identity if use_rezero else norm_fn
+        branch_fn = Rezero if use_rezero else None
+
+        if cross_attend and not only_cross:
+            default_block = ('a', 'c', 'f')
+        elif cross_attend and only_cross:
+            default_block = ('c', 'f')
+        else:
+            default_block = ('a', 'f')
+
+        if macaron:
+            default_block = ('f',) + default_block
+
+        # qk normalization
+
+        if use_qk_norm_attn:
+            attn_scale_init_value = -math.log(math.log2(qk_norm_attn_seq_len ** 2 - qk_norm_attn_seq_len)) if exists(
+                qk_norm_attn_seq_len) else None
+            attn_kwargs = {**attn_kwargs, 'qk_norm': True, 'scale_init_value': attn_scale_init_value}
+
+        # zero init
+
+        if zero_init_branch_output:
+            attn_kwargs = {**attn_kwargs, 'zero_init_output': True}
+            ff_kwargs = {**ff_kwargs, 'zero_init_output': True}
+
+        # calculate layer block order
+
+        if exists(custom_layers):
+            layer_types = custom_layers
+        elif exists(par_ratio):
+            par_depth = depth * len(default_block)
+            assert 1 < par_ratio <= par_depth, 'par ratio out of range'
+            default_block = tuple(filter(not_equals('f'), default_block))
+            par_attn = par_depth // par_ratio
+            depth_cut = par_depth * 2 // 3  # 2 / 3 attention layer cutoff suggested by PAR paper
+            par_width = (depth_cut + depth_cut // par_attn) // par_attn
+            assert len(default_block) <= par_width, 'default block is too large for par_ratio'
+            par_block = default_block + ('f',) * (par_width - len(default_block))
+            par_head = par_block * par_attn
+            layer_types = par_head + ('f',) * (par_depth - len(par_head))
+        elif exists(sandwich_coef):
+            assert sandwich_coef > 0 and sandwich_coef <= depth, 'sandwich coefficient should be less than the depth'
+            layer_types = ('a',) * sandwich_coef + default_block * (depth - sandwich_coef) + ('f',) * sandwich_coef
+        else:
+            layer_types = default_block * depth
+
+        self.layer_types = layer_types
+        self.num_attn_layers = len(list(filter(equals('a'), layer_types)))
+
+        # calculate token shifting
+
+        shift_tokens = cast_tuple(shift_tokens, len(layer_types))
+
+        # iterate and construct layers
+
+        for ind, (layer_type, layer_shift_tokens) in enumerate(zip(self.layer_types, shift_tokens)):
+            is_last_layer = ind == (len(self.layer_types) - 1)
+
+            if layer_type == 'a':
+                layer = Attention(dim, heads=heads, causal=causal, **attn_kwargs)
+            elif layer_type == 'c':
+                layer = Attention(dim, heads=heads, **attn_kwargs)
+            elif layer_type == 'f':
+                layer = FeedForward(dim, **ff_kwargs)
+                layer = layer if not macaron else Scale(0.5, layer)
+            else:
+                raise Exception(f'invalid layer type {layer_type}')
+
+            if layer_shift_tokens > 0:
+                shift_range_upper = layer_shift_tokens + 1
+                shift_range_lower = -layer_shift_tokens if not causal else 0
+                layer = ShiftTokens(range(shift_range_lower, shift_range_upper), layer)
+
+            if exists(branch_fn):
+                layer = branch_fn(layer)
+
+            residual_fn = GRUGating if gate_residual else Residual
+            residual = residual_fn(dim, scale_residual=scale_residual)
+
+            layer_uses_qk_norm = use_qk_norm_attn and layer_type in ('a', 'c')
+
+            pre_branch_norm = norm_fn() if pre_norm and not layer_uses_qk_norm else None
+            post_branch_norm = norm_fn() if sandwich_norm or layer_uses_qk_norm else None
+            post_main_norm = norm_fn() if not pre_norm and not is_last_layer else None
+
+            norms = nn.ModuleList([
+                pre_branch_norm,
+                post_branch_norm,
+                post_main_norm
+            ])
+
+            self.layers.append(nn.ModuleList([
+                norms,
+                layer,
+                residual
+            ]))
+
+    def forward(
+            self,
+            x,
+            context=None,
+            full_context=None,  # for passing a list of hidden states from an encoder
+            mask=None,
+            context_mask=None,
+            attn_mask=None,
+            mems=None,
+            return_hiddens=False,
+            norm_scale_shift_inp=None,
+            past_key_values=None,
+            expected_seq_len=None,
+    ):
+
+        assert not (self.cross_attend ^ (exists(context) or exists(
+            full_context))), 'context must be passed in if cross_attend is set to True'
+        assert context is None or full_context is None, 'only one of full_context or context can be provided'
+
+        hiddens = []
+        intermediates = []
+        prev_attn = None
+        prev_cross_attn = None
+
+        mems = mems.copy() if exists(mems) else [None] * self.num_attn_layers
+        norm_args = {}
+        if exists(norm_scale_shift_inp):
+            norm_args['norm_scale_shift_inp'] = norm_scale_shift_inp
+
+        rotary_pos_emb = None
+        if exists(self.rotary_pos_emb):
+            if not self.training and self.causal:
+                assert expected_seq_len is not None, "To decode a transformer with rotary embeddings, you must specify an `expected_seq_len`"
+            elif expected_seq_len is None:
+                expected_seq_len = 0
+            seq_len = x.shape[1]
+            if past_key_values is not None:
+                seq_len += past_key_values[0][0].shape[-2]
+            max_rotary_emb_length = max(list(map(lambda m: (m.shape[1] if exists(m) else 0) + seq_len, mems)) + [expected_seq_len])
+            rotary_pos_emb = self.rotary_pos_emb(max_rotary_emb_length, x.device)
+
+        present_key_values = []
+        cross_attn_count = 0
+        for ind, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
+            if layer_type == 'a':
+                layer_mem = mems.pop(0) if mems else None
+
+            residual = x
+
+            pre_branch_norm, post_branch_norm, post_main_norm = norm
+
+            if exists(pre_branch_norm):
+                x = pre_branch_norm(x, **norm_args)
+
+            if layer_type == 'a' or layer_type == 'c':
+                if past_key_values is not None:
+                    layer_kv = past_key_values.pop(0)
+                    layer_past = tuple(s.to(x.device) for s in layer_kv)
+                else:
+                    layer_past = None
+
+            if layer_type == 'a':
+                out, inter, k, v = block(x, None, mask, None, attn_mask, self.pia_pos_emb, rotary_pos_emb,
+                                        prev_attn, layer_mem, layer_past)
+            elif layer_type == 'c':
+                if exists(full_context):
+                    out, inter, k, v = block(x, full_context[cross_attn_count], mask, context_mask, None, None,
+                                            None, prev_attn, None, layer_past)
+                else:
+                    out, inter, k, v = block(x, context, mask, context_mask, None, None, None, prev_attn, None, layer_past)
+            elif layer_type == 'f':
+                out = block(x)
+
+            if layer_type == 'a' or layer_type == 'c' and present_key_values is not None:
+                present_key_values.append((k.detach(), v.detach()))
+
+            if exists(post_branch_norm):
+                out = post_branch_norm(out, **norm_args)
+
+            x = residual_fn(out, residual)
+
+            if layer_type in ('a', 'c'):
+                intermediates.append(inter)
+
+            if layer_type == 'a' and self.residual_attn:
+                prev_attn = inter.pre_softmax_attn
+            elif layer_type == 'c' and self.cross_residual_attn:
+                prev_cross_attn = inter.pre_softmax_attn
+
+            if exists(post_main_norm):
+                x = post_main_norm(x, **norm_args)
+
+            if layer_type == 'c':
+                cross_attn_count += 1
+
+            if layer_type == 'f':
+                hiddens.append(x)
+
+        if return_hiddens:
+            intermediates = LayerIntermediates(
+                hiddens=hiddens,
+                attn_intermediates=intermediates,
+                past_key_values=present_key_values
+            )
+
+            return x, intermediates
+
+        return x
+
+
+class Encoder(AttentionLayers):
+    def __init__(self, **kwargs):
+        assert 'causal' not in kwargs, 'cannot set causality on encoder'
+        super().__init__(causal=False, **kwargs)
+
+
+class Decoder(AttentionLayers):
+    def __init__(self, **kwargs):
+        assert 'causal' not in kwargs, 'cannot set causality on decoder'
+        super().__init__(causal=True, **kwargs)
+
+
+class CrossAttender(AttentionLayers):
+    def __init__(self, **kwargs):
+        super().__init__(cross_attend=True, only_cross=True, **kwargs)
+
+
+class ViTransformerWrapper(nn.Module):
+    def __init__(
+            self,
+            *,
+            image_size,
+            patch_size,
+            attn_layers,
+            num_classes=None,
+            dropout=0.,
+            emb_dropout=0.
+    ):
+        super().__init__()
+        assert isinstance(attn_layers, Encoder), 'attention layers must be an Encoder'
+        assert image_size % patch_size == 0, 'image dimensions must be divisible by the patch size'
+        dim = attn_layers.dim
+        num_patches = (image_size // patch_size) ** 2
+        patch_dim = 3 * patch_size ** 2
+
+        self.patch_size = patch_size
+
+        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
+        self.patch_to_embedding = nn.Linear(patch_dim, dim)
+        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
+        self.dropout = nn.Dropout(emb_dropout)
+
+        self.attn_layers = attn_layers
+        self.norm = nn.LayerNorm(dim)
+        self.mlp_head = FeedForward(dim, dim_out=num_classes, dropout=dropout) if exists(num_classes) else None
+
+    def forward(
+            self,
+            img,
+            return_embeddings=False
+    ):
+        p = self.patch_size
+
+        x = rearrange(img, 'b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1=p, p2=p)
+        x = self.patch_to_embedding(x)
+        b, n, _ = x.shape
+
+        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b=b)
+        x = torch.cat((cls_tokens, x), dim=1)
+        x = x + self.pos_embedding[:, :(n + 1)]
+        x = self.dropout(x)
+
+        x = self.attn_layers(x)
+        x = self.norm(x)
+
+        if not exists(self.mlp_head) or return_embeddings:
+            return x
+
+        return self.mlp_head(x[:, 0])
+
+
+class TransformerWrapper(nn.Module):
+    def __init__(
+            self,
+            *,
+            num_tokens,
+            max_seq_len,
+            attn_layers,
+            emb_dim=None,
+            max_mem_len=0.,
+            shift_mem_down=0,
+            emb_dropout=0.,
+            num_memory_tokens=None,
+            tie_embedding=False,
+            use_pos_emb=True
+    ):
+        super().__init__()
+        assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
+
+        dim = attn_layers.dim
+        emb_dim = default(emb_dim, dim)
+
+        self.max_seq_len = max_seq_len
+        self.max_mem_len = max_mem_len
+        self.shift_mem_down = shift_mem_down
+
+        self.token_emb = nn.Embedding(num_tokens, emb_dim)
+        self.pos_emb = AbsolutePositionalEmbedding(emb_dim, max_seq_len) if (
+                    use_pos_emb and not attn_layers.has_pos_emb) else always(0)
+        self.emb_dropout = nn.Dropout(emb_dropout)
+
+        self.project_emb = nn.Linear(emb_dim, dim) if emb_dim != dim else nn.Identity()
+        self.attn_layers = attn_layers
+        self.norm = nn.LayerNorm(dim)
+
+        self.init_()
+
+        self.to_logits = nn.Linear(dim, num_tokens) if not tie_embedding else lambda t: t @ self.token_emb.weight.t()
+
+        # memory tokens (like [cls]) from Memory Transformers paper
+        num_memory_tokens = default(num_memory_tokens, 0)
+        self.num_memory_tokens = num_memory_tokens
+        if num_memory_tokens > 0:
+            self.memory_tokens = nn.Parameter(torch.randn(num_memory_tokens, dim))
+
+    def init_(self):
+        nn.init.kaiming_normal_(self.token_emb.weight)
+
+    def forward(
+            self,
+            x,
+            return_embeddings=False,
+            mask=None,
+            return_hiddens=False,
+            return_attn=False,
+            mems=None,
+            use_cache=False,
+            **kwargs
+    ):
+        b, n, device, num_mem = *x.shape, x.device, self.num_memory_tokens
+        x = self.token_emb(x)
+        x = x + self.pos_emb(x)
+        x = self.emb_dropout(x)
+
+        x = self.project_emb(x)
+
+        if num_mem > 0:
+            mem = repeat(self.memory_tokens, 'n d -> b n d', b=b)
+            x = torch.cat((mem, x), dim=1)
+
+            # auto-handle masking after appending memory tokens
+            if exists(mask):
+                mask = F.pad(mask, (num_mem, 0), value=True)
+
+        if self.shift_mem_down and exists(mems):
+            mems_l, mems_r = mems[:self.shift_mem_down], mems[self.shift_mem_down:]
+            mems = [*mems_r, *mems_l]
+
+        x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
+        x = self.norm(x)
+
+        mem, x = x[:, :num_mem], x[:, num_mem:]
+
+        out = self.to_logits(x) if not return_embeddings else x
+
+        if return_hiddens:
+            hiddens = intermediates.hiddens
+            return out, hiddens
+
+        res = [out]
+        if return_attn:
+            attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
+            res.append(attn_maps)
+        if use_cache:
+            res.append(intermediates.past_key_values)
+
+        if len(res) > 1:
+            return tuple(res)
+        return res[0]
+
+
+class ContinuousTransformerWrapper(nn.Module):
+    def __init__(
+            self,
+            *,
+            max_seq_len,
+            attn_layers,
+            dim_in=None,
+            dim_out=None,
+            emb_dim=None,
+            emb_dropout=0.,
+            use_pos_emb=True
+    ):
+        super().__init__()
+        assert isinstance(attn_layers, AttentionLayers), 'attention layers must be one of Encoder or Decoder'
+
+        dim = attn_layers.dim
+
+        self.max_seq_len = max_seq_len
+
+        self.pos_emb = AbsolutePositionalEmbedding(dim, max_seq_len) if (
+                    use_pos_emb and not attn_layers.has_pos_emb) else always(0)
+        self.emb_dropout = nn.Dropout(emb_dropout)
+
+        self.project_in = nn.Linear(dim_in, dim) if exists(dim_in) else nn.Identity()
+
+        self.attn_layers = attn_layers
+        self.norm = nn.LayerNorm(dim)
+
+        self.project_out = nn.Linear(dim, dim_out) if exists(dim_out) else nn.Identity()
+
+    def forward(
+            self,
+            x,
+            return_embeddings=False,
+            mask=None,
+            return_attn=False,
+            mems=None,
+            use_cache=False,
+            **kwargs
+    ):
+        b, n, _, device = *x.shape, x.device
+
+        x = self.project_in(x)
+        x = x + self.pos_emb(x)
+        x = self.emb_dropout(x)
+
+        x, intermediates = self.attn_layers(x, mask=mask, mems=mems, return_hiddens=True, **kwargs)
+        x = self.norm(x)
+
+        out = self.project_out(x) if not return_embeddings else x
+
+        res = [out]
+        if return_attn:
+            attn_maps = list(map(lambda t: t.post_softmax_attn, intermediates.attn_intermediates))
+            res.append(attn_maps)
+        if use_cache:
+            res.append(intermediates.past_key_values)
+
+        if len(res) > 1:
+            return tuple(res)
+        return res[0]
+
diff --git a/tortoise/tortoise/read.py b/tortoise/tortoise/read.py
new file mode 100644
index 0000000..e5839aa
--- /dev/null
+++ b/tortoise/tortoise/read.py
@@ -0,0 +1,101 @@
+import argparse
+import os
+from time import time
+
+import torch
+import torchaudio
+
+from api import TextToSpeech, MODELS_DIR
+from utils.audio import load_audio, load_voices
+from utils.text import split_and_recombine_text
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--textfile', type=str, help='A file containing the text to read.', default="tortoise/data/riding_hood.txt")
+    parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) '
+                                                 'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='pat')
+    parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/longform/')
+    parser.add_argument('--output_name', type=str, help='How to name the output file', default='combined.wav')
+    parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='standard')
+    parser.add_argument('--regenerate', type=str, help='Comma-separated list of clip numbers to re-generate, or nothing.', default=None)
+    parser.add_argument('--candidates', type=int, help='How many output candidates to produce per-voice. Only the first candidate is actually used in the final product, the others can be used manually.', default=1)
+    parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this'
+                                                      'should only be specified if you have custom checkpoints.', default=MODELS_DIR)
+    parser.add_argument('--seed', type=int, help='Random seed which can be used to reproduce results.', default=None)
+    parser.add_argument('--produce_debug_state', type=bool, help='Whether or not to produce debug_state.pth, which can aid in reproducing problems. Defaults to true.', default=True)
+    parser.add_argument('--use_deepspeed', type=bool, help='Use deepspeed for speed bump.', default=False)
+    parser.add_argument('--kv_cache', type=bool, help='If you disable this please wait for a long a time to get the output', default=True)
+    parser.add_argument('--half', type=bool, help="float16(half) precision inference if True it's faster and take less vram and ram", default=True)
+
+
+    args = parser.parse_args()
+    if torch.backends.mps.is_available():
+        args.use_deepspeed = False
+    tts = TextToSpeech(models_dir=args.model_dir, use_deepspeed=args.use_deepspeed, kv_cache=args.kv_cache, half=args.half)
+
+    outpath = args.output_path
+    outname = args.output_name
+    selected_voices = args.voice.split(',')
+    regenerate = args.regenerate
+    if regenerate is not None:
+        regenerate = [int(e) for e in regenerate.split(',')]
+
+    # Process text
+    with open(args.textfile, 'r', encoding='utf-8') as f:
+        text = ' '.join([l for l in f.readlines()])
+    if '|' in text:
+        print("Found the '|' character in your text, which I will use as a cue for where to split it up. If this was not"
+              "your intent, please remove all '|' characters from the input.")
+        texts = text.split('|')
+    else:
+        texts = split_and_recombine_text(text)
+
+    seed = int(time()) if args.seed is None else args.seed
+    for selected_voice in selected_voices:
+        voice_outpath = os.path.join(outpath, selected_voice)
+        os.makedirs(voice_outpath, exist_ok=True)
+
+        if '&' in selected_voice:
+            voice_sel = selected_voice.split('&')
+        else:
+            voice_sel = [selected_voice]
+
+        voice_samples, conditioning_latents = load_voices(voice_sel)
+        all_parts = []
+        for j, text in enumerate(texts):
+            if regenerate is not None and j not in regenerate:
+                all_parts.append(load_audio(os.path.join(voice_outpath, f'{j}.wav'), 24000))
+                continue
+            gen = tts.tts_with_preset(text, voice_samples=voice_samples, conditioning_latents=conditioning_latents,
+                                      preset=args.preset, k=args.candidates, use_deterministic_seed=seed)
+            if args.candidates == 1:
+                audio_ = gen.squeeze(0).cpu()
+                torchaudio.save(os.path.join(voice_outpath, f'{j}.wav'), audio_, 24000)
+            else:
+                candidate_dir = os.path.join(voice_outpath, str(j))
+                os.makedirs(candidate_dir, exist_ok=True)
+                for k, g in enumerate(gen):
+                    torchaudio.save(os.path.join(candidate_dir, f'{k}.wav'), g.squeeze(0).cpu(), 24000)
+                audio_ = gen[0].squeeze(0).cpu()
+            all_parts.append(audio_)
+
+        if args.candidates == 1:
+            full_audio = torch.cat(all_parts, dim=-1)
+            torchaudio.save(os.path.join(voice_outpath, f"{outname}.wav"), full_audio, 24000)
+
+        if args.produce_debug_state:
+            os.makedirs('debug_states', exist_ok=True)
+            dbg_state = (seed, texts, voice_samples, conditioning_latents)
+            torch.save(dbg_state, f'debug_states/read_debug_{selected_voice}.pth')
+
+        # Combine each candidate's audio clips.
+        if args.candidates > 1:
+            audio_clips = []
+            for candidate in range(args.candidates):
+                for line in range(len(texts)):
+                    wav_file = os.path.join(voice_outpath, str(line), f"{candidate}.wav")
+                    audio_clips.append(load_audio(wav_file, 24000))
+                audio_clips = torch.cat(audio_clips, dim=-1)
+                torchaudio.save(os.path.join(voice_outpath, f"{outname}_{candidate:02d}.wav"), audio_clips, 24000)
+                audio_clips = []
diff --git a/tortoise/tortoise/read_fast.py b/tortoise/tortoise/read_fast.py
new file mode 100644
index 0000000..f2778d4
--- /dev/null
+++ b/tortoise/tortoise/read_fast.py
@@ -0,0 +1,77 @@
+import argparse
+import os
+from time import time
+
+import torch
+import torchaudio
+
+from api_fast import TextToSpeech, MODELS_DIR
+from utils.audio import load_audio, load_voices
+from utils.text import split_and_recombine_text
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--textfile', type=str, help='A file containing the text to read.', default="tortoise/data/riding_hood.txt")
+    parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) '
+                                                 'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='lj')
+    parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/longform/')
+    parser.add_argument('--output_name', type=str, help='How to name the output file', default='combined.wav')
+    parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='standard')
+    parser.add_argument('--regenerate', type=str, help='Comma-separated list of clip numbers to re-generate, or nothing.', default=None)
+    parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this'
+                                                      'should only be specified if you have custom checkpoints.', default=MODELS_DIR)
+    parser.add_argument('--seed', type=int, help='Random seed which can be used to reproduce results.', default=None)
+    parser.add_argument('--use_deepspeed', type=bool, help='Use deepspeed for speed bump.', default=False)
+    parser.add_argument('--kv_cache', type=bool, help='If you disable this please wait for a long a time to get the output', default=True)
+    parser.add_argument('--half', type=bool, help="float16(half) precision inference if True it's faster and take less vram and ram", default=True)
+
+
+    args = parser.parse_args()
+    if torch.backends.mps.is_available():
+        args.use_deepspeed = False
+    tts = TextToSpeech(models_dir=args.model_dir, use_deepspeed=args.use_deepspeed, kv_cache=args.kv_cache, half=args.half)
+
+    outpath = args.output_path
+    outname = args.output_name
+    selected_voices = args.voice.split(',')
+    regenerate = args.regenerate
+    if regenerate is not None:
+        regenerate = [int(e) for e in regenerate.split(',')]
+
+    # Process text
+    with open(args.textfile, 'r', encoding='utf-8') as f:
+        text = ' '.join([l for l in f.readlines()])
+    if '|' in text:
+        print("Found the '|' character in your text, which I will use as a cue for where to split it up. If this was not"
+              "your intent, please remove all '|' characters from the input.")
+        texts = text.split('|')
+    else:
+        texts = split_and_recombine_text(text)
+
+    seed = int(time()) if args.seed is None else args.seed
+    for selected_voice in selected_voices:
+        voice_outpath = os.path.join(outpath, selected_voice)
+        os.makedirs(voice_outpath, exist_ok=True)
+
+        if '&' in selected_voice:
+            voice_sel = selected_voice.split('&')
+        else:
+            voice_sel = [selected_voice]
+
+        voice_samples, conditioning_latents = load_voices(voice_sel)
+        all_parts = []
+        for j, text in enumerate(texts):
+            if regenerate is not None and j not in regenerate:
+                all_parts.append(load_audio(os.path.join(voice_outpath, f'{j}.wav'), 24000))
+                continue
+            start_time = time()
+            gen = tts.tts(text, voice_samples=voice_samples, use_deterministic_seed=seed)
+            end_time = time()
+            audio_ = gen.squeeze(0).cpu()
+            print("Time taken to generate the audio: ", end_time - start_time, "seconds")
+            print("RTF: ", (end_time - start_time) / (audio_.shape[1] / 24000))
+            torchaudio.save(os.path.join(voice_outpath, f'{j}.wav'), audio_, 24000)
+            all_parts.append(audio_)
+        full_audio = torch.cat(all_parts, dim=-1)
+        torchaudio.save(os.path.join(voice_outpath, f"{outname}.wav"), full_audio, 24000)
diff --git a/tortoise/tortoise/tts_stream.py b/tortoise/tortoise/tts_stream.py
new file mode 100644
index 0000000..94eaff5
--- /dev/null
+++ b/tortoise/tortoise/tts_stream.py
@@ -0,0 +1,85 @@
+import argparse
+import os
+from time import time
+
+import torch
+import torchaudio
+
+from api_fast import TextToSpeech, MODELS_DIR
+from utils.audio import load_audio, load_voices
+from utils.text import split_and_recombine_text
+import sounddevice as sd
+import queue
+import threading
+def play_audio(audio_queue):
+    while True:
+        chunk = audio_queue.get()
+        if chunk is None:
+            break
+        sd.play(chunk.cpu().numpy(), samplerate=24000)
+        sd.wait()
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--textfile', type=str, help='A file containing the text to read.', default="tortoise/data/riding_hood.txt")
+    parser.add_argument('--voice', type=str, help='Selects the voice to use for generation. See options in voices/ directory (and add your own!) '
+                                                 'Use the & character to join two voices together. Use a comma to perform inference on multiple voices.', default='lj')
+    parser.add_argument('--output_path', type=str, help='Where to store outputs.', default='results/longform/')
+    parser.add_argument('--output_name', type=str, help='How to name the output file', default='combined.wav')
+    parser.add_argument('--preset', type=str, help='Which voice preset to use.', default='standard')
+    parser.add_argument('--regenerate', type=str, help='Comma-separated list of clip numbers to re-generate, or nothing.', default=None)
+    parser.add_argument('--model_dir', type=str, help='Where to find pretrained model checkpoints. Tortoise automatically downloads these to .models, so this'
+                                                      'should only be specified if you have custom checkpoints.', default=MODELS_DIR)
+    parser.add_argument('--seed', type=int, help='Random seed which can be used to reproduce results.', default=None)
+    parser.add_argument('--use_deepspeed', type=bool, help='Use deepspeed for speed bump.', default=False)
+    parser.add_argument('--kv_cache', type=bool, help='If you disable this please wait for a long a time to get the output', default=True)
+    parser.add_argument('--half', type=bool, help="float16(half) precision inference if True it's faster and take less vram and ram", default=True)
+
+
+    args = parser.parse_args()
+    if torch.backends.mps.is_available():
+        args.use_deepspeed = False
+    tts = TextToSpeech(models_dir=args.model_dir, use_deepspeed=args.use_deepspeed, kv_cache=args.kv_cache, half=args.half)
+
+    outpath = args.output_path
+    outname = args.output_name
+    selected_voices = args.voice.split(',')
+    regenerate = args.regenerate
+    if regenerate is not None:
+        regenerate = [int(e) for e in regenerate.split(',')]
+
+    # Process text
+    with open(args.textfile, 'r', encoding='utf-8') as f:
+        text = ' '.join([l for l in f.readlines()])
+    if '|' in text:
+        print("Found the '|' character in your text, which I will use as a cue for where to split it up. If this was not"
+              "your intent, please remove all '|' characters from the input.")
+        texts = text.split('|')
+    else:
+        texts = split_and_recombine_text(text)
+    audio_queue = queue.Queue()
+    playback_thread = threading.Thread(target=play_audio, args=(audio_queue,))
+    playback_thread.start()
+    
+    seed = int(time()) if args.seed is None else args.seed
+    for selected_voice in selected_voices:
+        voice_outpath = os.path.join(outpath, selected_voice)
+        os.makedirs(voice_outpath, exist_ok=True)
+
+        if '&' in selected_voice:
+            voice_sel = selected_voice.split('&')
+        else:
+            voice_sel = [selected_voice]
+
+        voice_samples, conditioning_latents = load_voices(voice_sel)
+        all_parts = []
+        for j, text in enumerate(texts):
+            if regenerate is not None and j not in regenerate:
+                all_parts.append(load_audio(os.path.join(voice_outpath, f'{j}.wav'), 24000))
+                continue
+            start_time = time()
+            audio_generator = tts.tts_stream(text, voice_samples=voice_samples, use_deterministic_seed=seed)
+            for wav_chunk in audio_generator:
+                audio_queue.put(wav_chunk)
+    audio_queue.put(None)
+    playback_thread.join()
\ No newline at end of file
diff --git a/tortoise/tortoise/utils/__init__.py b/tortoise/tortoise/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tortoise/tortoise/utils/audio.py b/tortoise/tortoise/utils/audio.py
new file mode 100644
index 0000000..fc41487
--- /dev/null
+++ b/tortoise/tortoise/utils/audio.py
@@ -0,0 +1,204 @@
+import os
+from glob import glob
+
+import librosa
+import torch
+import torchaudio
+import numpy as np
+from scipy.io.wavfile import read
+
+from tortoise.utils.stft import STFT
+
+
+BUILTIN_VOICES_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../voices')
+
+
+def load_wav_to_torch(full_path):
+    sampling_rate, data = read(full_path)
+    if data.dtype == np.int32:
+        norm_fix = 2 ** 31
+    elif data.dtype == np.int16:
+        norm_fix = 2 ** 15
+    elif data.dtype == np.float16 or data.dtype == np.float32:
+        norm_fix = 1.
+    else:
+        raise NotImplemented(f"Provided data dtype not supported: {data.dtype}")
+    return (torch.FloatTensor(data.astype(np.float32)) / norm_fix, sampling_rate)
+
+
+def load_audio(audiopath, sampling_rate):
+    extension = os.path.splitext(audiopath)[1].casefold()
+    if extension == '.wav':
+        audio, lsr = load_wav_to_torch(audiopath)
+    elif extension == '.mp3':
+        audio, lsr = librosa.load(audiopath, sr=sampling_rate)
+        audio = torch.FloatTensor(audio)
+    else:
+        assert False, f"Unsupported audio format provided: {audiopath[-4:]}"
+
+    # Remove any channel data.
+    if len(audio.shape) > 1:
+        if audio.shape[0] < 5:
+            audio = audio[0]
+        else:
+            assert audio.shape[1] < 5
+            audio = audio[:, 0]
+
+    if lsr != sampling_rate:
+        audio = torchaudio.functional.resample(audio, lsr, sampling_rate)
+
+    # Check some assumptions about audio range. This should be automatically fixed in load_wav_to_torch, but might not be in some edge cases, where we should squawk.
+    # '2' is arbitrarily chosen since it seems like audio will often "overdrive" the [-1,1] bounds.
+    if torch.any(audio > 2) or not torch.any(audio < 0):
+        print(f"Error with {audiopath}. Max={audio.max()} min={audio.min()}")
+    audio.clip_(-1, 1)
+
+    return audio.unsqueeze(0)
+
+
+TACOTRON_MEL_MAX = 2.3143386840820312
+TACOTRON_MEL_MIN = -11.512925148010254
+
+
+def denormalize_tacotron_mel(norm_mel):
+    return ((norm_mel+1)/2)*(TACOTRON_MEL_MAX-TACOTRON_MEL_MIN)+TACOTRON_MEL_MIN
+
+
+def normalize_tacotron_mel(mel):
+    return 2 * ((mel - TACOTRON_MEL_MIN) / (TACOTRON_MEL_MAX - TACOTRON_MEL_MIN)) - 1
+
+
+def dynamic_range_compression(x, C=1, clip_val=1e-5):
+    """
+    PARAMS
+    ------
+    C: compression factor
+    """
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression(x, C=1):
+    """
+    PARAMS
+    ------
+    C: compression factor used to compress
+    """
+    return torch.exp(x) / C
+
+
+def get_voices(extra_voice_dirs=[]):
+    dirs = [BUILTIN_VOICES_DIR] + extra_voice_dirs
+    voices = {}
+    for d in dirs:
+        subs = os.listdir(d)
+        for sub in subs:
+            subj = os.path.join(d, sub)
+            if os.path.isdir(subj):
+                voices[sub] = list(glob(f'{subj}/*.wav')) + list(glob(f'{subj}/*.mp3')) + list(glob(f'{subj}/*.pth'))
+    return voices
+
+def save_pth(conds, save_path):
+    torch.save(conds, save_path)
+
+
+def load_voice(voice, extra_voice_dirs=[]):
+    if voice == 'random':
+        return None, None
+
+    voices = get_voices(extra_voice_dirs)
+    paths = voices[voice]
+    pth_files = [p for p in paths if p.endswith('.pth')]
+    if len(paths) == 1 and paths[0].endswith('.pth'):
+        return None, torch.load(paths[0])
+    else:
+        conds = []
+        for cond_path in paths:
+            if not cond_path.endswith('.pth'):
+                c = load_audio(cond_path, 22050)
+                conds.append(c)
+
+        if not pth_files:
+            pth_save_path = os.path.join(os.path.dirname(paths[0]), f"{voice}.pth")
+            save_pth(conds, pth_save_path)
+
+        return conds, None
+
+
+def load_voices(voices, extra_voice_dirs=[]):
+    latents = []
+    clips = []
+    for voice in voices:
+        if voice == 'random':
+            if len(voices) > 1:
+                print("Cannot combine a random voice with a non-random voice. Just using a random voice.")
+            return None, None
+        clip, latent = load_voice(voice, extra_voice_dirs)
+        if latent is None:
+            assert len(latents) == 0, "Can only combine raw audio voices or latent voices, not both. Do it yourself if you want this."
+            clips.extend(clip)
+        elif clip is None:
+            assert len(clips) == 0, "Can only combine raw audio voices or latent voices, not both. Do it yourself if you want this."
+            latents.append(latent)
+    if len(latents) == 0:
+        return clips, None
+    else:
+        latents_0 = torch.stack([l[0] for l in latents], dim=0).mean(dim=0)
+        latents_1 = torch.stack([l[1] for l in latents], dim=0).mean(dim=0)
+        latents = (latents_0,latents_1)
+        return None, latents
+
+
+class TacotronSTFT(torch.nn.Module):
+    def __init__(self, filter_length=1024, hop_length=256, win_length=1024,
+                 n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0,
+                 mel_fmax=8000.0):
+        super(TacotronSTFT, self).__init__()
+        self.n_mel_channels = n_mel_channels
+        self.sampling_rate = sampling_rate
+        self.stft_fn = STFT(filter_length, hop_length, win_length)
+        from librosa.filters import mel as librosa_mel_fn
+        mel_basis = librosa_mel_fn(
+            sr=sampling_rate, n_fft=filter_length, n_mels=n_mel_channels, fmin=mel_fmin, fmax=mel_fmax)
+        mel_basis = torch.from_numpy(mel_basis).float()
+        self.register_buffer('mel_basis', mel_basis)
+
+    def spectral_normalize(self, magnitudes):
+        output = dynamic_range_compression(magnitudes)
+        return output
+
+    def spectral_de_normalize(self, magnitudes):
+        output = dynamic_range_decompression(magnitudes)
+        return output
+
+    def mel_spectrogram(self, y):
+        """Computes mel-spectrograms from a batch of waves
+        PARAMS
+        ------
+        y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1]
+
+        RETURNS
+        -------
+        mel_output: torch.FloatTensor of shape (B, n_mel_channels, T)
+        """
+        assert(torch.min(y.data) >= -10)
+        assert(torch.max(y.data) <= 10)
+        y = torch.clip(y, min=-1, max=1)
+
+        magnitudes, phases = self.stft_fn.transform(y)
+        magnitudes = magnitudes.data
+        mel_output = torch.matmul(self.mel_basis, magnitudes)
+        mel_output = self.spectral_normalize(mel_output)
+        return mel_output
+
+
+def wav_to_univnet_mel(wav, do_normalization=False,
+                       device='cuda' if not torch.backends.mps.is_available() else 'mps',
+                       stft=None):
+    # Don't require stft to be passed, but use it if it is.
+    if stft is None:
+        stft = TacotronSTFT(1024, 256, 1024, 100, 24000, 0, 12000)
+        stft = stft.to(device)
+    mel = stft.mel_spectrogram(wav)
+    if do_normalization:
+        mel = normalize_tacotron_mel(mel)
+    return mel
diff --git a/tortoise/tortoise/utils/diffusion.py b/tortoise/tortoise/utils/diffusion.py
new file mode 100644
index 0000000..6d4d594
--- /dev/null
+++ b/tortoise/tortoise/utils/diffusion.py
@@ -0,0 +1,1250 @@
+"""
+This is an almost carbon copy of gaussian_diffusion.py from OpenAI's ImprovedDiffusion repo, which itself:
+
+This code started out as a PyTorch port of Ho et al's diffusion models:
+https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py
+
+Docstrings have been added, as well as DDIM sampling and a new collection of beta schedules.
+"""
+
+import enum
+import math
+
+import numpy as np
+import torch
+import torch as th
+from tqdm import tqdm
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    Compute the KL divergence between two gaussians.
+
+    Shapes are automatically broadcasted, so batches can be compared to
+    scalars, among other use cases.
+    """
+    tensor = None
+    for obj in (mean1, logvar1, mean2, logvar2):
+        if isinstance(obj, th.Tensor):
+            tensor = obj
+            break
+    assert tensor is not None, "at least one argument must be a Tensor"
+
+    # Force variances to be Tensors. Broadcasting helps convert scalars to
+    # Tensors, but it does not work for th.exp().
+    logvar1, logvar2 = [
+        x if isinstance(x, th.Tensor) else th.tensor(x).to(tensor)
+        for x in (logvar1, logvar2)
+    ]
+
+    return 0.5 * (
+        -1.0
+        + logvar2
+        - logvar1
+        + th.exp(logvar1 - logvar2)
+        + ((mean1 - mean2) ** 2) * th.exp(-logvar2)
+    )
+
+
+def approx_standard_normal_cdf(x):
+    """
+    A fast approximation of the cumulative distribution function of the
+    standard normal.
+    """
+    return 0.5 * (1.0 + th.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * th.pow(x, 3))))
+
+
+def discretized_gaussian_log_likelihood(x, *, means, log_scales):
+    """
+    Compute the log-likelihood of a Gaussian distribution discretizing to a
+    given image.
+
+    :param x: the target images. It is assumed that this was uint8 values,
+              rescaled to the range [-1, 1].
+    :param means: the Gaussian mean Tensor.
+    :param log_scales: the Gaussian log stddev Tensor.
+    :return: a tensor like x of log probabilities (in nats).
+    """
+    assert x.shape == means.shape == log_scales.shape
+    centered_x = x - means
+    inv_stdv = th.exp(-log_scales)
+    plus_in = inv_stdv * (centered_x + 1.0 / 255.0)
+    cdf_plus = approx_standard_normal_cdf(plus_in)
+    min_in = inv_stdv * (centered_x - 1.0 / 255.0)
+    cdf_min = approx_standard_normal_cdf(min_in)
+    log_cdf_plus = th.log(cdf_plus.clamp(min=1e-12))
+    log_one_minus_cdf_min = th.log((1.0 - cdf_min).clamp(min=1e-12))
+    cdf_delta = cdf_plus - cdf_min
+    log_probs = th.where(
+        x < -0.999,
+        log_cdf_plus,
+        th.where(x > 0.999, log_one_minus_cdf_min, th.log(cdf_delta.clamp(min=1e-12))),
+    )
+    assert log_probs.shape == x.shape
+    return log_probs
+
+
+def mean_flat(tensor):
+    """
+    Take the mean over all non-batch dimensions.
+    """
+    return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
+    """
+    Get a pre-defined beta schedule for the given name.
+
+    The beta schedule library consists of beta schedules which remain similar
+    in the limit of num_diffusion_timesteps.
+    Beta schedules may be added, but should not be removed or changed once
+    they are committed to maintain backwards compatibility.
+    """
+    if schedule_name == "linear":
+        # Linear schedule from Ho et al, extended to work for any number of
+        # diffusion steps.
+        scale = 1000 / num_diffusion_timesteps
+        beta_start = scale * 0.0001
+        beta_end = scale * 0.02
+        return np.linspace(
+            beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64
+        )
+    elif schedule_name == "cosine":
+        return betas_for_alpha_bar(
+            num_diffusion_timesteps,
+            lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
+        )
+    else:
+        raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+    """
+    Create a beta schedule that discretizes the given alpha_t_bar function,
+    which defines the cumulative product of (1-beta) over time from t = [0,1].
+
+    :param num_diffusion_timesteps: the number of betas to produce.
+    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+                      produces the cumulative product of (1-beta) up to that
+                      part of the diffusion process.
+    :param max_beta: the maximum beta to use; use values lower than 1 to
+                     prevent singularities.
+    """
+    betas = []
+    for i in range(num_diffusion_timesteps):
+        t1 = i / num_diffusion_timesteps
+        t2 = (i + 1) / num_diffusion_timesteps
+        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+    return np.array(betas)
+
+
+class ModelMeanType(enum.Enum):
+    """
+    Which type of output the model predicts.
+    """
+
+    PREVIOUS_X = 'previous_x'  # the model predicts x_{t-1}
+    START_X = 'start_x'  # the model predicts x_0
+    EPSILON = 'epsilon'  # the model predicts epsilon
+
+
+class ModelVarType(enum.Enum):
+    """
+    What is used as the model's output variance.
+
+    The LEARNED_RANGE option has been added to allow the model to predict
+    values between FIXED_SMALL and FIXED_LARGE, making its job easier.
+    """
+
+    LEARNED = 'learned'
+    FIXED_SMALL = 'fixed_small'
+    FIXED_LARGE = 'fixed_large'
+    LEARNED_RANGE = 'learned_range'
+
+
+class LossType(enum.Enum):
+    MSE = 'mse'  # use raw MSE loss (and KL when learning variances)
+    RESCALED_MSE = 'rescaled_mse'  # use raw MSE loss (with RESCALED_KL when learning variances)
+    KL = 'kl'  # use the variational lower-bound
+    RESCALED_KL = 'rescaled_kl'  # like KL, but rescale to estimate the full VLB
+
+    def is_vb(self):
+        return self == LossType.KL or self == LossType.RESCALED_KL
+
+
+class GaussianDiffusion:
+    """
+    Utilities for training and sampling diffusion models.
+
+    Ported directly from here, and then adapted over time to further experimentation.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
+
+    :param betas: a 1-D numpy array of betas for each diffusion timestep,
+                  starting at T and going to 1.
+    :param model_mean_type: a ModelMeanType determining what the model outputs.
+    :param model_var_type: a ModelVarType determining how variance is output.
+    :param loss_type: a LossType determining the loss function to use.
+    :param rescale_timesteps: if True, pass floating point timesteps into the
+                              model so that they are always scaled like in the
+                              original paper (0 to 1000).
+    """
+
+    def __init__(
+        self,
+        *,
+        betas,
+        model_mean_type,
+        model_var_type,
+        loss_type,
+        rescale_timesteps=False,
+        conditioning_free=False,
+        conditioning_free_k=1,
+        ramp_conditioning_free=True,
+    ):
+        self.model_mean_type = ModelMeanType(model_mean_type)
+        self.model_var_type = ModelVarType(model_var_type)
+        self.loss_type = LossType(loss_type)
+        self.rescale_timesteps = rescale_timesteps
+        self.conditioning_free = conditioning_free
+        self.conditioning_free_k = conditioning_free_k
+        self.ramp_conditioning_free = ramp_conditioning_free
+
+        # Use float64 for accuracy.
+        betas = np.array(betas, dtype=np.float64)
+        self.betas = betas
+        assert len(betas.shape) == 1, "betas must be 1-D"
+        assert (betas > 0).all() and (betas <= 1).all()
+
+        self.num_timesteps = int(betas.shape[0])
+
+        alphas = 1.0 - betas
+        self.alphas_cumprod = np.cumprod(alphas, axis=0)
+        self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
+        self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0)
+        assert self.alphas_cumprod_prev.shape == (self.num_timesteps,)
+
+        # calculations for diffusion q(x_t | x_{t-1}) and others
+        self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod)
+        self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod)
+        self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod)
+        self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod)
+        self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod - 1)
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        self.posterior_variance = (
+            betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
+        )
+        # log calculation clipped because the posterior variance is 0 at the
+        # beginning of the diffusion chain.
+        self.posterior_log_variance_clipped = np.log(
+            np.append(self.posterior_variance[1], self.posterior_variance[1:])
+        )
+        self.posterior_mean_coef1 = (
+            betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
+        )
+        self.posterior_mean_coef2 = (
+            (1.0 - self.alphas_cumprod_prev)
+            * np.sqrt(alphas)
+            / (1.0 - self.alphas_cumprod)
+        )
+
+    def q_mean_variance(self, x_start, t):
+        """
+        Get the distribution q(x_t | x_0).
+
+        :param x_start: the [N x C x ...] tensor of noiseless inputs.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+        """
+        mean = (
+            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+        )
+        variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+        log_variance = _extract_into_tensor(
+            self.log_one_minus_alphas_cumprod, t, x_start.shape
+        )
+        return mean, variance, log_variance
+
+    def q_sample(self, x_start, t, noise=None):
+        """
+        Diffuse the data for a given number of diffusion steps.
+
+        In other words, sample from q(x_t | x_0).
+
+        :param x_start: the initial data batch.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :param noise: if specified, the split-out normal noise.
+        :return: A noisy version of x_start.
+        """
+        if noise is None:
+            noise = th.randn_like(x_start)
+        assert noise.shape == x_start.shape
+        return (
+            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+            + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
+            * noise
+        )
+
+    def q_posterior_mean_variance(self, x_start, x_t, t):
+        """
+        Compute the mean and variance of the diffusion posterior:
+
+            q(x_{t-1} | x_t, x_0)
+
+        """
+        assert x_start.shape == x_t.shape
+        posterior_mean = (
+            _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+            + _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = _extract_into_tensor(
+            self.posterior_log_variance_clipped, t, x_t.shape
+        )
+        assert (
+            posterior_mean.shape[0]
+            == posterior_variance.shape[0]
+            == posterior_log_variance_clipped.shape[0]
+            == x_start.shape[0]
+        )
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(
+        self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None
+    ):
+        """
+        Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
+        the initial x, x_0.
+
+        :param model: the model, which takes a signal and a batch of timesteps
+                      as input.
+        :param x: the [N x C x ...] tensor at time t.
+        :param t: a 1-D Tensor of timesteps.
+        :param clip_denoised: if True, clip the denoised signal into [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample. Applies before
+            clip_denoised.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict with the following keys:
+                 - 'mean': the model mean output.
+                 - 'variance': the model variance output.
+                 - 'log_variance': the log of 'variance'.
+                 - 'pred_xstart': the prediction for x_0.
+        """
+        if model_kwargs is None:
+            model_kwargs = {}
+
+        B, C = x.shape[:2]
+        assert t.shape == (B,)
+        model_output = model(x, self._scale_timesteps(t), **model_kwargs)
+        if self.conditioning_free:
+            model_output_no_conditioning = model(x, self._scale_timesteps(t), conditioning_free=True, **model_kwargs)
+
+        if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:
+            assert model_output.shape == (B, C * 2, *x.shape[2:])
+            model_output, model_var_values = th.split(model_output, C, dim=1)
+            if self.conditioning_free:
+                model_output_no_conditioning, _ = th.split(model_output_no_conditioning, C, dim=1)
+            if self.model_var_type == ModelVarType.LEARNED:
+                model_log_variance = model_var_values
+                model_variance = th.exp(model_log_variance)
+            else:
+                min_log = _extract_into_tensor(
+                    self.posterior_log_variance_clipped, t, x.shape
+                )
+                max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)
+                # The model_var_values is [-1, 1] for [min_var, max_var].
+                frac = (model_var_values + 1) / 2
+                model_log_variance = frac * max_log + (1 - frac) * min_log
+                model_variance = th.exp(model_log_variance)
+        else:
+            model_variance, model_log_variance = {
+                # for fixedlarge, we set the initial (log-)variance like so
+                # to get a better decoder log likelihood.
+                ModelVarType.FIXED_LARGE: (
+                    np.append(self.posterior_variance[1], self.betas[1:]),
+                    np.log(np.append(self.posterior_variance[1], self.betas[1:])),
+                ),
+                ModelVarType.FIXED_SMALL: (
+                    self.posterior_variance,
+                    self.posterior_log_variance_clipped,
+                ),
+            }[self.model_var_type]
+            model_variance = _extract_into_tensor(model_variance, t, x.shape)
+            model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)
+
+        if self.conditioning_free:
+            if self.ramp_conditioning_free:
+                assert t.shape[0] == 1  # This should only be used in inference.
+                cfk = self.conditioning_free_k * (1 - self._scale_timesteps(t)[0].item() / self.num_timesteps)
+            else:
+                cfk = self.conditioning_free_k
+            model_output = (1 + cfk) * model_output - cfk * model_output_no_conditioning
+
+        def process_xstart(x):
+            if denoised_fn is not None:
+                x = denoised_fn(x)
+            if clip_denoised:
+                return x.clamp(-1, 1)
+            return x
+
+        if self.model_mean_type == ModelMeanType.PREVIOUS_X:
+            pred_xstart = process_xstart(
+                self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output)
+            )
+            model_mean = model_output
+        elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]:
+            if self.model_mean_type == ModelMeanType.START_X:
+                pred_xstart = process_xstart(model_output)
+            else:
+                pred_xstart = process_xstart(
+                    self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
+                )
+            model_mean, _, _ = self.q_posterior_mean_variance(
+                x_start=pred_xstart, x_t=x, t=t
+            )
+        else:
+            raise NotImplementedError(self.model_mean_type)
+
+        assert (
+            model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
+        )
+        return {
+            "mean": model_mean,
+            "variance": model_variance,
+            "log_variance": model_log_variance,
+            "pred_xstart": pred_xstart,
+        }
+
+    def _predict_xstart_from_eps(self, x_t, t, eps):
+        assert x_t.shape == eps.shape
+        return (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
+            - _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
+        )
+
+    def _predict_xstart_from_xprev(self, x_t, t, xprev):
+        assert x_t.shape == xprev.shape
+        return (  # (xprev - coef2*x_t) / coef1
+            _extract_into_tensor(1.0 / self.posterior_mean_coef1, t, x_t.shape) * xprev
+            - _extract_into_tensor(
+                self.posterior_mean_coef2 / self.posterior_mean_coef1, t, x_t.shape
+            )
+            * x_t
+        )
+
+    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+        return (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
+            - pred_xstart
+        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+    def _scale_timesteps(self, t):
+        if self.rescale_timesteps:
+            return t.float() * (1000.0 / self.num_timesteps)
+        return t
+
+    def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
+        """
+        Compute the mean for the previous step, given a function cond_fn that
+        computes the gradient of a conditional log probability with respect to
+        x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
+        condition on y.
+
+        This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
+        """
+        gradient = cond_fn(x, self._scale_timesteps(t), **model_kwargs)
+        new_mean = (
+            p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float()
+        )
+        return new_mean
+
+    def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
+        """
+        Compute what the p_mean_variance output would have been, should the
+        model's score function be conditioned by cond_fn.
+
+        See condition_mean() for details on cond_fn.
+
+        Unlike condition_mean(), this instead uses the conditioning strategy
+        from Song et al (2020).
+        """
+        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+
+        eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
+        eps = eps - (1 - alpha_bar).sqrt() * cond_fn(
+            x, self._scale_timesteps(t), **model_kwargs
+        )
+
+        out = p_mean_var.copy()
+        out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
+        out["mean"], _, _ = self.q_posterior_mean_variance(
+            x_start=out["pred_xstart"], x_t=x, t=t
+        )
+        return out
+
+    def p_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+    ):
+        """
+        Sample x_{t-1} from the model at the given timestep.
+
+        :param model: the model to sample from.
+        :param x: the current tensor at x_{t-1}.
+        :param t: the value of t, starting at 0 for the first diffusion step.
+        :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample.
+        :param cond_fn: if not None, this is a gradient function that acts
+                        similarly to the model.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :return: a dict containing the following keys:
+                 - 'sample': a random sample from the model.
+                 - 'pred_xstart': a prediction of x_0.
+        """
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        noise = th.randn_like(x)
+        nonzero_mask = (
+            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+        )  # no noise when t == 0
+        if cond_fn is not None:
+            out["mean"] = self.condition_mean(
+                cond_fn, out, x, t, model_kwargs=model_kwargs
+            )
+        sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
+        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+    def p_sample_loop(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+    ):
+        """
+        Generate samples from the model.
+
+        :param model: the model module.
+        :param shape: the shape of the samples, (N, C, H, W).
+        :param noise: if specified, the noise from the encoder to sample.
+                      Should be of the same shape as `shape`.
+        :param clip_denoised: if True, clip x_start predictions to [-1, 1].
+        :param denoised_fn: if not None, a function which applies to the
+            x_start prediction before it is used to sample.
+        :param cond_fn: if not None, this is a gradient function that acts
+                        similarly to the model.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :param device: if specified, the device to create the samples on.
+                       If not specified, use a model parameter's device.
+        :param progress: if True, show a tqdm progress bar.
+        :return: a non-differentiable batch of samples.
+        """
+        final = None
+        for sample in self.p_sample_loop_progressive(
+            model,
+            shape,
+            noise=noise,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            cond_fn=cond_fn,
+            model_kwargs=model_kwargs,
+            device=device,
+            progress=progress,
+        ):
+            final = sample
+        return final["sample"]
+
+    def p_sample_loop_progressive(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+    ):
+        """
+        Generate samples from the model and yield intermediate samples from
+        each timestep of diffusion.
+
+        Arguments are the same as p_sample_loop().
+        Returns a generator over dicts, where each dict is the return value of
+        p_sample().
+        """
+        if device is None:
+            device = next(model.parameters()).device
+        assert isinstance(shape, (tuple, list))
+        if noise is not None:
+            img = noise
+        else:
+            img = th.randn(*shape, device=device)
+        indices = list(range(self.num_timesteps))[::-1]
+
+        for i in tqdm(indices, disable=not progress):
+            t = th.tensor([i] * shape[0], device=device)
+            with th.no_grad():
+                out = self.p_sample(
+                    model,
+                    img,
+                    t,
+                    clip_denoised=clip_denoised,
+                    denoised_fn=denoised_fn,
+                    cond_fn=cond_fn,
+                    model_kwargs=model_kwargs,
+                )
+                yield out
+                img = out["sample"]
+
+    def ddim_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        eta=0.0,
+    ):
+        """
+        Sample x_{t-1} from the model using DDIM.
+
+        Same usage as p_sample().
+        """
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        if cond_fn is not None:
+            out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
+
+        # Usually our model outputs epsilon, but we re-derive it
+        # in case we used x_start or x_prev prediction.
+        eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
+
+        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
+        alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
+        sigma = (
+            eta
+            * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
+            * th.sqrt(1 - alpha_bar / alpha_bar_prev)
+        )
+        # Equation 12.
+        noise = th.randn_like(x)
+        mean_pred = (
+            out["pred_xstart"] * th.sqrt(alpha_bar_prev)
+            + th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps
+        )
+        nonzero_mask = (
+            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
+        )  # no noise when t == 0
+        sample = mean_pred + nonzero_mask * sigma * noise
+        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+
+    def ddim_reverse_sample(
+        self,
+        model,
+        x,
+        t,
+        clip_denoised=True,
+        denoised_fn=None,
+        model_kwargs=None,
+        eta=0.0,
+    ):
+        """
+        Sample x_{t+1} from the model using DDIM reverse ODE.
+        """
+        assert eta == 0.0, "Reverse ODE only for deterministic path"
+        out = self.p_mean_variance(
+            model,
+            x,
+            t,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            model_kwargs=model_kwargs,
+        )
+        # Usually our model outputs epsilon, but we re-derive it
+        # in case we used x_start or x_prev prediction.
+        eps = (
+            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x
+            - out["pred_xstart"]
+        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape)
+        alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape)
+
+        # Equation 12. reversed
+        mean_pred = (
+            out["pred_xstart"] * th.sqrt(alpha_bar_next)
+            + th.sqrt(1 - alpha_bar_next) * eps
+        )
+
+        return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}
+
+    def ddim_sample_loop(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        eta=0.0,
+    ):
+        """
+        Generate samples from the model using DDIM.
+
+        Same usage as p_sample_loop().
+        """
+        final = None
+        for sample in self.ddim_sample_loop_progressive(
+            model,
+            shape,
+            noise=noise,
+            clip_denoised=clip_denoised,
+            denoised_fn=denoised_fn,
+            cond_fn=cond_fn,
+            model_kwargs=model_kwargs,
+            device=device,
+            progress=progress,
+            eta=eta,
+        ):
+            final = sample
+        return final["sample"]
+
+    def ddim_sample_loop_progressive(
+        self,
+        model,
+        shape,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+        progress=False,
+        eta=0.0,
+    ):
+        """
+        Use DDIM to sample from the model and yield intermediate samples from
+        each timestep of DDIM.
+
+        Same usage as p_sample_loop_progressive().
+        """
+        if device is None:
+            device = next(model.parameters()).device
+        assert isinstance(shape, (tuple, list))
+        if noise is not None:
+            img = noise
+        else:
+            img = th.randn(*shape, device=device)
+        indices = list(range(self.num_timesteps))[::-1]
+
+        if progress:
+            # Lazy import so that we don't depend on tqdm.
+            from tqdm.auto import tqdm
+
+            indices = tqdm(indices, disable=not progress)
+
+        for i in indices:
+            t = th.tensor([i] * shape[0], device=device)
+            with th.no_grad():
+                out = self.ddim_sample(
+                    model,
+                    img,
+                    t,
+                    clip_denoised=clip_denoised,
+                    denoised_fn=denoised_fn,
+                    cond_fn=cond_fn,
+                    model_kwargs=model_kwargs,
+                    eta=eta,
+                )
+                yield out
+                img = out["sample"]
+
+    def _vb_terms_bpd(
+        self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None
+    ):
+        """
+        Get a term for the variational lower-bound.
+
+        The resulting units are bits (rather than nats, as one might expect).
+        This allows for comparison to other papers.
+
+        :return: a dict with the following keys:
+                 - 'output': a shape [N] tensor of NLLs or KLs.
+                 - 'pred_xstart': the x_0 predictions.
+        """
+        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
+            x_start=x_start, x_t=x_t, t=t
+        )
+        out = self.p_mean_variance(
+            model, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs
+        )
+        kl = normal_kl(
+            true_mean, true_log_variance_clipped, out["mean"], out["log_variance"]
+        )
+        kl = mean_flat(kl) / np.log(2.0)
+
+        decoder_nll = -discretized_gaussian_log_likelihood(
+            x_start, means=out["mean"], log_scales=0.5 * out["log_variance"]
+        )
+        assert decoder_nll.shape == x_start.shape
+        decoder_nll = mean_flat(decoder_nll) / np.log(2.0)
+
+        # At the first timestep return the decoder NLL,
+        # otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
+        output = th.where((t == 0), decoder_nll, kl)
+        return {"output": output, "pred_xstart": out["pred_xstart"]}
+
+    def training_losses(self, model, x_start, t, model_kwargs=None, noise=None):
+        """
+        Compute training losses for a single timestep.
+
+        :param model: the model to evaluate loss on.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :param t: a batch of timestep indices.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :param noise: if specified, the specific Gaussian noise to try to remove.
+        :return: a dict with the key "loss" containing a tensor of shape [N].
+                 Some mean or variance settings may also have other keys.
+        """
+        if model_kwargs is None:
+            model_kwargs = {}
+        if noise is None:
+            noise = th.randn_like(x_start)
+        x_t = self.q_sample(x_start, t, noise=noise)
+
+        terms = {}
+
+        if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL:
+            # TODO: support multiple model outputs for this mode.
+            terms["loss"] = self._vb_terms_bpd(
+                model=model,
+                x_start=x_start,
+                x_t=x_t,
+                t=t,
+                clip_denoised=False,
+                model_kwargs=model_kwargs,
+            )["output"]
+            if self.loss_type == LossType.RESCALED_KL:
+                terms["loss"] *= self.num_timesteps
+        elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
+            model_outputs = model(x_t, self._scale_timesteps(t), **model_kwargs)
+            if isinstance(model_outputs, tuple):
+                model_output = model_outputs[0]
+                terms['extra_outputs'] = model_outputs[1:]
+            else:
+                model_output = model_outputs
+
+            if self.model_var_type in [
+                ModelVarType.LEARNED,
+                ModelVarType.LEARNED_RANGE,
+            ]:
+                B, C = x_t.shape[:2]
+                assert model_output.shape == (B, C * 2, *x_t.shape[2:])
+                model_output, model_var_values = th.split(model_output, C, dim=1)
+                # Learn the variance using the variational bound, but don't let
+                # it affect our mean prediction.
+                frozen_out = th.cat([model_output.detach(), model_var_values], dim=1)
+                terms["vb"] = self._vb_terms_bpd(
+                    model=lambda *args, r=frozen_out: r,
+                    x_start=x_start,
+                    x_t=x_t,
+                    t=t,
+                    clip_denoised=False,
+                )["output"]
+                if self.loss_type == LossType.RESCALED_MSE:
+                    # Divide by 1000 for equivalence with initial implementation.
+                    # Without a factor of 1/1000, the VB term hurts the MSE term.
+                    terms["vb"] *= self.num_timesteps / 1000.0
+
+            if self.model_mean_type == ModelMeanType.PREVIOUS_X:
+                target = self.q_posterior_mean_variance(
+                    x_start=x_start, x_t=x_t, t=t
+                )[0]
+                x_start_pred = torch.zeros(x_start)  # Not supported.
+            elif self.model_mean_type == ModelMeanType.START_X:
+                target = x_start
+                x_start_pred = model_output
+            elif self.model_mean_type == ModelMeanType.EPSILON:
+                target = noise
+                x_start_pred = self._predict_xstart_from_eps(x_t, t, model_output)
+            else:
+                raise NotImplementedError(self.model_mean_type)
+            assert model_output.shape == target.shape == x_start.shape
+            terms["mse"] = mean_flat((target - model_output) ** 2)
+            terms["x_start_predicted"] = x_start_pred
+            if "vb" in terms:
+                terms["loss"] = terms["mse"] + terms["vb"]
+            else:
+                terms["loss"] = terms["mse"]
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        return terms
+
+    def autoregressive_training_losses(self, model, x_start, t, model_output_keys, gd_out_key, model_kwargs=None, noise=None):
+        """
+        Compute training losses for a single timestep.
+
+        :param model: the model to evaluate loss on.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :param t: a batch of timestep indices.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+        :param noise: if specified, the specific Gaussian noise to try to remove.
+        :return: a dict with the key "loss" containing a tensor of shape [N].
+                 Some mean or variance settings may also have other keys.
+        """
+        if model_kwargs is None:
+            model_kwargs = {}
+        if noise is None:
+            noise = th.randn_like(x_start)
+        x_t = self.q_sample(x_start, t, noise=noise)
+        terms = {}
+        if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL:
+            assert False  # not currently supported for this type of diffusion.
+        elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
+            model_outputs = model(x_t, x_start, self._scale_timesteps(t), **model_kwargs)
+            terms.update({k: o for k, o in zip(model_output_keys, model_outputs)})
+            model_output = terms[gd_out_key]
+            if self.model_var_type in [
+                ModelVarType.LEARNED,
+                ModelVarType.LEARNED_RANGE,
+            ]:
+                B, C = x_t.shape[:2]
+                assert model_output.shape == (B, C, 2, *x_t.shape[2:])
+                model_output, model_var_values = model_output[:, :, 0], model_output[:, :, 1]
+                # Learn the variance using the variational bound, but don't let
+                # it affect our mean prediction.
+                frozen_out = th.cat([model_output.detach(), model_var_values], dim=1)
+                terms["vb"] = self._vb_terms_bpd(
+                    model=lambda *args, r=frozen_out: r,
+                    x_start=x_start,
+                    x_t=x_t,
+                    t=t,
+                    clip_denoised=False,
+                )["output"]
+                if self.loss_type == LossType.RESCALED_MSE:
+                    # Divide by 1000 for equivalence with initial implementation.
+                    # Without a factor of 1/1000, the VB term hurts the MSE term.
+                    terms["vb"] *= self.num_timesteps / 1000.0
+
+            if self.model_mean_type == ModelMeanType.PREVIOUS_X:
+                target = self.q_posterior_mean_variance(
+                    x_start=x_start, x_t=x_t, t=t
+                )[0]
+                x_start_pred = torch.zeros(x_start)  # Not supported.
+            elif self.model_mean_type == ModelMeanType.START_X:
+                target = x_start
+                x_start_pred = model_output
+            elif self.model_mean_type == ModelMeanType.EPSILON:
+                target = noise
+                x_start_pred = self._predict_xstart_from_eps(x_t, t, model_output)
+            else:
+                raise NotImplementedError(self.model_mean_type)
+            assert model_output.shape == target.shape == x_start.shape
+            terms["mse"] = mean_flat((target - model_output) ** 2)
+            terms["x_start_predicted"] = x_start_pred
+            if "vb" in terms:
+                terms["loss"] = terms["mse"] + terms["vb"]
+            else:
+                terms["loss"] = terms["mse"]
+        else:
+            raise NotImplementedError(self.loss_type)
+
+        return terms
+
+    def _prior_bpd(self, x_start):
+        """
+        Get the prior KL term for the variational lower-bound, measured in
+        bits-per-dim.
+
+        This term can't be optimized, as it only depends on the encoder.
+
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :return: a batch of [N] KL values (in bits), one per batch element.
+        """
+        batch_size = x_start.shape[0]
+        t = th.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+        kl_prior = normal_kl(
+            mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0
+        )
+        return mean_flat(kl_prior) / np.log(2.0)
+
+    def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None):
+        """
+        Compute the entire variational lower-bound, measured in bits-per-dim,
+        as well as other related quantities.
+
+        :param model: the model to evaluate loss on.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :param clip_denoised: if True, clip denoised samples.
+        :param model_kwargs: if not None, a dict of extra keyword arguments to
+            pass to the model. This can be used for conditioning.
+
+        :return: a dict containing the following keys:
+                 - total_bpd: the total variational lower-bound, per batch element.
+                 - prior_bpd: the prior term in the lower-bound.
+                 - vb: an [N x T] tensor of terms in the lower-bound.
+                 - xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep.
+                 - mse: an [N x T] tensor of epsilon MSEs for each timestep.
+        """
+        device = x_start.device
+        batch_size = x_start.shape[0]
+
+        vb = []
+        xstart_mse = []
+        mse = []
+        for t in list(range(self.num_timesteps))[::-1]:
+            t_batch = th.tensor([t] * batch_size, device=device)
+            noise = th.randn_like(x_start)
+            x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise)
+            # Calculate VLB term at the current timestep
+            with th.no_grad():
+                out = self._vb_terms_bpd(
+                    model,
+                    x_start=x_start,
+                    x_t=x_t,
+                    t=t_batch,
+                    clip_denoised=clip_denoised,
+                    model_kwargs=model_kwargs,
+                )
+            vb.append(out["output"])
+            xstart_mse.append(mean_flat((out["pred_xstart"] - x_start) ** 2))
+            eps = self._predict_eps_from_xstart(x_t, t_batch, out["pred_xstart"])
+            mse.append(mean_flat((eps - noise) ** 2))
+
+        vb = th.stack(vb, dim=1)
+        xstart_mse = th.stack(xstart_mse, dim=1)
+        mse = th.stack(mse, dim=1)
+
+        prior_bpd = self._prior_bpd(x_start)
+        total_bpd = vb.sum(dim=1) + prior_bpd
+        return {
+            "total_bpd": total_bpd,
+            "prior_bpd": prior_bpd,
+            "vb": vb,
+            "xstart_mse": xstart_mse,
+            "mse": mse,
+        }
+
+
+def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
+    """
+    Get a pre-defined beta schedule for the given name.
+
+    The beta schedule library consists of beta schedules which remain similar
+    in the limit of num_diffusion_timesteps.
+    Beta schedules may be added, but should not be removed or changed once
+    they are committed to maintain backwards compatibility.
+    """
+    if schedule_name == "linear":
+        # Linear schedule from Ho et al, extended to work for any number of
+        # diffusion steps.
+        scale = 1000 / num_diffusion_timesteps
+        beta_start = scale * 0.0001
+        beta_end = scale * 0.02
+        return np.linspace(
+            beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64
+        )
+    elif schedule_name == "cosine":
+        return betas_for_alpha_bar(
+            num_diffusion_timesteps,
+            lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
+        )
+    else:
+        raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
+
+
+class SpacedDiffusion(GaussianDiffusion):
+    """
+    A diffusion process which can skip steps in a base diffusion process.
+
+    :param use_timesteps: a collection (sequence or set) of timesteps from the
+                          original diffusion process to retain.
+    :param kwargs: the kwargs to create the base diffusion process.
+    """
+
+    def __init__(self, use_timesteps, **kwargs):
+        self.use_timesteps = set(use_timesteps)
+        self.timestep_map = []
+        self.original_num_steps = len(kwargs["betas"])
+
+        base_diffusion = GaussianDiffusion(**kwargs)  # pylint: disable=missing-kwoa
+        last_alpha_cumprod = 1.0
+        new_betas = []
+        for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod):
+            if i in self.use_timesteps:
+                new_betas.append(1 - alpha_cumprod / last_alpha_cumprod)
+                last_alpha_cumprod = alpha_cumprod
+                self.timestep_map.append(i)
+        kwargs["betas"] = np.array(new_betas)
+        super().__init__(**kwargs)
+
+    def p_mean_variance(
+        self, model, *args, **kwargs
+    ):  # pylint: disable=signature-differs
+        return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)
+
+    def training_losses(
+        self, model, *args, **kwargs
+    ):  # pylint: disable=signature-differs
+        return super().training_losses(self._wrap_model(model), *args, **kwargs)
+
+    def autoregressive_training_losses(
+        self, model, *args, **kwargs
+    ):  # pylint: disable=signature-differs
+        return super().autoregressive_training_losses(self._wrap_model(model, True), *args, **kwargs)
+
+    def condition_mean(self, cond_fn, *args, **kwargs):
+        return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs)
+
+    def condition_score(self, cond_fn, *args, **kwargs):
+        return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs)
+
+    def _wrap_model(self, model, autoregressive=False):
+        if isinstance(model, _WrappedModel) or isinstance(model, _WrappedAutoregressiveModel):
+            return model
+        mod = _WrappedAutoregressiveModel if autoregressive else _WrappedModel
+        return mod(
+            model, self.timestep_map, self.rescale_timesteps, self.original_num_steps
+        )
+
+    def _scale_timesteps(self, t):
+        # Scaling is done by the wrapped model.
+        return t
+
+
+def space_timesteps(num_timesteps, section_counts):
+    """
+    Create a list of timesteps to use from an original diffusion process,
+    given the number of timesteps we want to take from equally-sized portions
+    of the original process.
+
+    For example, if there's 300 timesteps and the section counts are [10,15,20]
+    then the first 100 timesteps are strided to be 10 timesteps, the second 100
+    are strided to be 15 timesteps, and the final 100 are strided to be 20.
+
+    If the stride is a string starting with "ddim", then the fixed striding
+    from the DDIM paper is used, and only one section is allowed.
+
+    :param num_timesteps: the number of diffusion steps in the original
+                          process to divide up.
+    :param section_counts: either a list of numbers, or a string containing
+                           comma-separated numbers, indicating the step count
+                           per section. As a special case, use "ddimN" where N
+                           is a number of steps to use the striding from the
+                           DDIM paper.
+    :return: a set of diffusion steps from the original process to use.
+    """
+    if isinstance(section_counts, str):
+        if section_counts.startswith("ddim"):
+            desired_count = int(section_counts[len("ddim") :])
+            for i in range(1, num_timesteps):
+                if len(range(0, num_timesteps, i)) == desired_count:
+                    return set(range(0, num_timesteps, i))
+            raise ValueError(
+                f"cannot create exactly {num_timesteps} steps with an integer stride"
+            )
+        section_counts = [int(x) for x in section_counts.split(",")]
+    size_per = num_timesteps // len(section_counts)
+    extra = num_timesteps % len(section_counts)
+    start_idx = 0
+    all_steps = []
+    for i, section_count in enumerate(section_counts):
+        size = size_per + (1 if i < extra else 0)
+        if size < section_count:
+            raise ValueError(
+                f"cannot divide section of {size} steps into {section_count}"
+            )
+        if section_count <= 1:
+            frac_stride = 1
+        else:
+            frac_stride = (size - 1) / (section_count - 1)
+        cur_idx = 0.0
+        taken_steps = []
+        for _ in range(section_count):
+            taken_steps.append(start_idx + round(cur_idx))
+            cur_idx += frac_stride
+        all_steps += taken_steps
+        start_idx += size
+    return set(all_steps)
+
+
+class _WrappedModel:
+    def __init__(self, model, timestep_map, rescale_timesteps, original_num_steps):
+        self.model = model
+        self.timestep_map = timestep_map
+        self.rescale_timesteps = rescale_timesteps
+        self.original_num_steps = original_num_steps
+
+    def __call__(self, x, ts, **kwargs):
+        map_tensor = th.tensor(self.timestep_map, device=ts.device, dtype=ts.dtype)
+        new_ts = map_tensor[ts]
+        if self.rescale_timesteps:
+            new_ts = new_ts.float() * (1000.0 / self.original_num_steps)
+        return self.model(x, new_ts, **kwargs)
+
+
+class _WrappedAutoregressiveModel:
+    def __init__(self, model, timestep_map, rescale_timesteps, original_num_steps):
+        self.model = model
+        self.timestep_map = timestep_map
+        self.rescale_timesteps = rescale_timesteps
+        self.original_num_steps = original_num_steps
+
+    def __call__(self, x, x0, ts, **kwargs):
+        map_tensor = th.tensor(self.timestep_map, device=ts.device, dtype=ts.dtype)
+        new_ts = map_tensor[ts]
+        if self.rescale_timesteps:
+            new_ts = new_ts.float() * (1000.0 / self.original_num_steps)
+        return self.model(x, x0, new_ts, **kwargs)
+
+def _extract_into_tensor(arr, timesteps, broadcast_shape):
+    """
+    Extract values from a 1-D numpy array for a batch of indices.
+
+    :param arr: the 1-D numpy array.
+    :param timesteps: a tensor of indices into the array to extract.
+    :param broadcast_shape: a larger shape of K dimensions with the batch
+                            dimension equal to the length of timesteps.
+    :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
+    """
+    res = th.from_numpy(arr.astype(np.float32)).to(device=timesteps.device)[timesteps]
+    while len(res.shape) < len(broadcast_shape):
+        res = res[..., None]
+    return res.expand(broadcast_shape)
\ No newline at end of file
diff --git a/tortoise/tortoise/utils/stft.py b/tortoise/tortoise/utils/stft.py
new file mode 100644
index 0000000..f54eb96
--- /dev/null
+++ b/tortoise/tortoise/utils/stft.py
@@ -0,0 +1,193 @@
+"""
+BSD 3-Clause License
+
+Copyright (c) 2017, Prem Seetharaman
+All rights reserved.
+
+* Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice,
+  this list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice, this
+  list of conditions and the following disclaimer in the
+  documentation and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from this
+  software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+"""
+
+import torch
+import numpy as np
+import torch.nn.functional as F
+from torch.autograd import Variable
+from scipy.signal import get_window
+from librosa.util import pad_center, tiny
+import librosa.util as librosa_util
+
+
+def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
+                     n_fft=800, dtype=np.float32, norm=None):
+    """
+    # from librosa 0.6
+    Compute the sum-square envelope of a window function at a given hop length.
+
+    This is used to estimate modulation effects induced by windowing
+    observations in short-time fourier transforms.
+
+    Parameters
+    ----------
+    window : string, tuple, number, callable, or list-like
+        Window specification, as in `get_window`
+
+    n_frames : int > 0
+        The number of analysis frames
+
+    hop_length : int > 0
+        The number of samples to advance between frames
+
+    win_length : [optional]
+        The length of the window function.  By default, this matches `n_fft`.
+
+    n_fft : int > 0
+        The length of each analysis frame.
+
+    dtype : np.dtype
+        The data type of the output
+
+    Returns
+    -------
+    wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))`
+        The sum-squared envelope of the window function
+    """
+    if win_length is None:
+        win_length = n_fft
+
+    n = n_fft + hop_length * (n_frames - 1)
+    x = np.zeros(n, dtype=dtype)
+
+    # Compute the squared window at the desired length
+    win_sq = get_window(window, win_length, fftbins=True)
+    win_sq = librosa_util.normalize(win_sq, norm=norm)**2
+    win_sq = librosa_util.pad_center(win_sq, n_fft)
+
+    # Fill the envelope
+    for i in range(n_frames):
+        sample = i * hop_length
+        x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))]
+    return x
+
+
+class STFT(torch.nn.Module):
+    """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft"""
+    def __init__(self, filter_length=800, hop_length=200, win_length=800,
+                 window='hann'):
+        super(STFT, self).__init__()
+        self.filter_length = filter_length
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.window = window
+        self.forward_transform = None
+        scale = self.filter_length / self.hop_length
+        fourier_basis = np.fft.fft(np.eye(self.filter_length))
+
+        cutoff = int((self.filter_length / 2 + 1))
+        fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]),
+                                   np.imag(fourier_basis[:cutoff, :])])
+
+        forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
+        inverse_basis = torch.FloatTensor(
+            np.linalg.pinv(scale * fourier_basis).T[:, None, :])
+
+        if window is not None:
+            assert(filter_length >= win_length)
+            # get window and zero center pad it to filter_length
+            fft_window = get_window(window, win_length, fftbins=True)
+            fft_window = pad_center(fft_window, size=filter_length)
+            fft_window = torch.from_numpy(fft_window).float()
+
+            # window the bases
+            forward_basis *= fft_window
+            inverse_basis *= fft_window
+
+        self.register_buffer('forward_basis', forward_basis.float())
+        self.register_buffer('inverse_basis', inverse_basis.float())
+
+    def transform(self, input_data):
+        num_batches = input_data.size(0)
+        num_samples = input_data.size(1)
+
+        self.num_samples = num_samples
+
+        # similar to librosa, reflect-pad the input
+        input_data = input_data.view(num_batches, 1, num_samples)
+        input_data = F.pad(
+            input_data.unsqueeze(1),
+            (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0),
+            mode='reflect')
+        input_data = input_data.squeeze(1)
+
+        forward_transform = F.conv1d(
+            input_data,
+            Variable(self.forward_basis, requires_grad=False),
+            stride=self.hop_length,
+            padding=0)
+
+        cutoff = int((self.filter_length / 2) + 1)
+        real_part = forward_transform[:, :cutoff, :]
+        imag_part = forward_transform[:, cutoff:, :]
+
+        magnitude = torch.sqrt(real_part**2 + imag_part**2)
+        phase = torch.autograd.Variable(
+            torch.atan2(imag_part.data, real_part.data))
+
+        return magnitude, phase
+
+    def inverse(self, magnitude, phase):
+        recombine_magnitude_phase = torch.cat(
+            [magnitude*torch.cos(phase), magnitude*torch.sin(phase)], dim=1)
+
+        inverse_transform = F.conv_transpose1d(
+            recombine_magnitude_phase,
+            Variable(self.inverse_basis, requires_grad=False),
+            stride=self.hop_length,
+            padding=0)
+
+        if self.window is not None:
+            window_sum = window_sumsquare(
+                self.window, magnitude.size(-1), hop_length=self.hop_length,
+                win_length=self.win_length, n_fft=self.filter_length,
+                dtype=np.float32)
+            # remove modulation effects
+            approx_nonzero_indices = torch.from_numpy(
+                np.where(window_sum > tiny(window_sum))[0])
+            window_sum = torch.autograd.Variable(
+                torch.from_numpy(window_sum), requires_grad=False)
+            window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum
+            inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices]
+
+            # scale by hop ratio
+            inverse_transform *= float(self.filter_length) / self.hop_length
+
+        inverse_transform = inverse_transform[:, :, int(self.filter_length/2):]
+        inverse_transform = inverse_transform[:, :, :-int(self.filter_length/2):]
+
+        return inverse_transform
+
+    def forward(self, input_data):
+        self.magnitude, self.phase = self.transform(input_data)
+        reconstruction = self.inverse(self.magnitude, self.phase)
+        return reconstruction
\ No newline at end of file
diff --git a/tortoise/tortoise/utils/text.py b/tortoise/tortoise/utils/text.py
new file mode 100644
index 0000000..e28c867
--- /dev/null
+++ b/tortoise/tortoise/utils/text.py
@@ -0,0 +1,132 @@
+import re
+
+
+def split_and_recombine_text(text, desired_length=200, max_length=300):
+    """Split text it into chunks of a desired length trying to keep sentences intact."""
+    # normalize text, remove redundant whitespace and convert non-ascii quotes to ascii
+    text = re.sub(r'\n\n+', '\n', text)
+    text = re.sub(r'\s+', ' ', text)
+    text = re.sub(r'[“”]', '"', text)
+
+    rv = []
+    in_quote = False
+    current = ""
+    split_pos = []
+    pos = -1
+    end_pos = len(text) - 1
+
+    def seek(delta):
+        nonlocal pos, in_quote, current
+        is_neg = delta < 0
+        for _ in range(abs(delta)):
+            if is_neg:
+                pos -= 1
+                current = current[:-1]
+            else:
+                pos += 1
+                current += text[pos]
+            if text[pos] == '"':
+                in_quote = not in_quote
+        return text[pos]
+
+    def peek(delta):
+        p = pos + delta
+        return text[p] if p < end_pos and p >= 0 else ""
+
+    def commit():
+        nonlocal rv, current, split_pos
+        rv.append(current)
+        current = ""
+        split_pos = []
+
+    while pos < end_pos:
+        c = seek(1)
+        # do we need to force a split?
+        if len(current) >= max_length:
+            if len(split_pos) > 0 and len(current) > (desired_length / 2):
+                # we have at least one sentence and we are over half the desired length, seek back to the last split
+                d = pos - split_pos[-1]
+                seek(-d)
+            else:
+                # no full sentences, seek back until we are not in the middle of a word and split there
+                while c not in '!?.\n ' and pos > 0 and len(current) > desired_length:
+                    c = seek(-1)
+            commit()
+        # check for sentence boundaries
+        elif not in_quote and (c in '!?\n' or (c == '.' and peek(1) in '\n ')):
+            # seek forward if we have consecutive boundary markers but still within the max length
+            while pos < len(text) - 1 and len(current) < max_length and peek(1) in '!?.':
+                c = seek(1)
+            split_pos.append(pos)
+            if len(current) >= desired_length:
+                commit()
+        # treat end of quote as a boundary if its followed by a space or newline
+        elif in_quote and peek(1) == '"' and peek(2) in '\n ':
+            seek(2)
+            split_pos.append(pos)
+    rv.append(current)
+
+    # clean up, remove lines with only whitespace or punctuation
+    rv = [s.strip() for s in rv]
+    rv = [s for s in rv if len(s) > 0 and not re.match(r'^[\s\.,;:!?]*$', s)]
+
+    return rv
+
+
+if __name__ == '__main__':
+    import os
+    import unittest
+
+    class Test(unittest.TestCase):
+        def test_split_and_recombine_text(self):
+            text = """
+            This is a sample sentence.
+            This is another sample sentence.
+            This is a longer sample sentence that should force a split inthemiddlebutinotinthislongword.
+            "Don't split my quote... please"
+            """
+            self.assertEqual(split_and_recombine_text(text, desired_length=20, max_length=40),
+                             ['This is a sample sentence.',
+                              'This is another sample sentence.',
+                              'This is a longer sample sentence that',
+                              'should force a split',
+                              'inthemiddlebutinotinthislongword.',
+                              '"Don\'t split my quote... please"'])
+
+        def test_split_and_recombine_text_2(self):
+            text = """
+            When you are really angry sometimes you use consecutive exclamation marks!!!!!! Is this a good thing to do?!?!?!
+            I don't know but we should handle this situation..........................
+            """
+            self.assertEqual(split_and_recombine_text(text, desired_length=30, max_length=50),
+                             ['When you are really angry sometimes you use',
+                              'consecutive exclamation marks!!!!!!',
+                              'Is this a good thing to do?!?!?!',
+                              'I don\'t know but we should handle this situation.'])
+
+        def test_split_and_recombine_text_3(self):
+            text_src = os.path.join(os.path.dirname(__file__), '../data/riding_hood.txt')
+            with open(text_src, 'r') as f:
+                text = f.read()
+            self.assertEqual(
+                split_and_recombine_text(text),
+                [
+                    'Once upon a time there lived in a certain village a little country girl, the prettiest creature who was ever seen. Her mother was excessively fond of her; and her grandmother doted on her still more. This good woman had a little red riding hood made for her.',
+                    'It suited the girl so extremely well that everybody called her Little Red Riding Hood. One day her mother, having made some cakes, said to her, "Go, my dear, and see how your grandmother is doing, for I hear she has been very ill. Take her a cake, and this little pot of butter."',
+                    'Little Red Riding Hood set out immediately to go to her grandmother, who lived in another village. As she was going through the wood, she met with a wolf, who had a very great mind to eat her up, but he dared not, because of some woodcutters working nearby in the forest.',
+                    'He asked her where she was going. The poor child, who did not know that it was dangerous to stay and talk to a wolf, said to him, "I am going to see my grandmother and carry her a cake and a little pot of butter from my mother." "Does she live far off?" said the wolf "Oh I say,"',
+                    'answered Little Red Riding Hood; "it is beyond that mill you see there, at the first house in the village." "Well," said the wolf, "and I\'ll go and see her too. I\'ll go this way and go you that, and we shall see who will be there first."',
+                    'The wolf ran as fast as he could, taking the shortest path, and the little girl took a roundabout way, entertaining herself by gathering nuts, running after butterflies, and gathering bouquets of little flowers.',
+                    'It was not long before the wolf arrived at the old woman\'s house. He knocked at the door: tap, tap. "Who\'s there?" "Your grandchild, Little Red Riding Hood," replied the wolf, counterfeiting her voice; "who has brought you a cake and a little pot of butter sent you by mother."',
+                    'The good grandmother, who was in bed, because she was somewhat ill, cried out, "Pull the bobbin, and the latch will go up."',
+                    'The wolf pulled the bobbin, and the door opened, and then he immediately fell upon the good woman and ate her up in a moment, for it been more than three days since he had eaten.',
+                    'He then shut the door and got into the grandmother\'s bed, expecting Little Red Riding Hood, who came some time afterwards and knocked at the door: tap, tap. "Who\'s there?"',
+                    'Little Red Riding Hood, hearing the big voice of the wolf, was at first afraid; but believing her grandmother had a cold and was hoarse, answered, "It is your grandchild Little Red Riding Hood, who has brought you a cake and a little pot of butter mother sends you."',
+                    'The wolf cried out to her, softening his voice as much as he could, "Pull the bobbin, and the latch will go up." Little Red Riding Hood pulled the bobbin, and the door opened.',
+                    'The wolf, seeing her come in, said to her, hiding himself under the bedclothes, "Put the cake and the little pot of butter upon the stool, and come get into bed with me." Little Red Riding Hood took off her clothes and got into bed.',
+                    'She was greatly amazed to see how her grandmother looked in her nightclothes, and said to her, "Grandmother, what big arms you have!" "All the better to hug you with, my dear." "Grandmother, what big legs you have!" "All the better to run with, my child." "Grandmother, what big ears you have!"',
+                    '"All the better to hear with, my child." "Grandmother, what big eyes you have!" "All the better to see with, my child." "Grandmother, what big teeth you have got!" "All the better to eat you up with." And, saying these words, this wicked wolf fell upon Little Red Riding Hood, and ate her all up.',
+                ]
+            )
+
+    unittest.main()
diff --git a/tortoise/tortoise/utils/tokenizer.py b/tortoise/tortoise/utils/tokenizer.py
new file mode 100644
index 0000000..922f23e
--- /dev/null
+++ b/tortoise/tortoise/utils/tokenizer.py
@@ -0,0 +1,194 @@
+import os
+import re
+
+import inflect
+import torch
+from tokenizers import Tokenizer
+
+
+# Regular expression matching whitespace:
+from unidecode import unidecode
+
+_whitespace_re = re.compile(r'\s+')
+
+
+# List of (regular expression, replacement) pairs for abbreviations:
+_abbreviations = [(re.compile('\\b%s\\.' % x[0], re.IGNORECASE), x[1]) for x in [
+  ('mrs', 'misess'),
+  ('mr', 'mister'),
+  ('dr', 'doctor'),
+  ('st', 'saint'),
+  ('co', 'company'),
+  ('jr', 'junior'),
+  ('maj', 'major'),
+  ('gen', 'general'),
+  ('drs', 'doctors'),
+  ('rev', 'reverend'),
+  ('lt', 'lieutenant'),
+  ('hon', 'honorable'),
+  ('sgt', 'sergeant'),
+  ('capt', 'captain'),
+  ('esq', 'esquire'),
+  ('ltd', 'limited'),
+  ('col', 'colonel'),
+  ('ft', 'fort'),
+]]
+
+
+def expand_abbreviations(text):
+  for regex, replacement in _abbreviations:
+    text = re.sub(regex, replacement, text)
+  return text
+
+
+_inflect = inflect.engine()
+_comma_number_re = re.compile(r'([0-9][0-9\,]+[0-9])')
+_decimal_number_re = re.compile(r'([0-9]+\.[0-9]+)')
+_pounds_re = re.compile(r'£([0-9\,]*[0-9]+)')
+_dollars_re = re.compile(r'\$([0-9\.\,]*[0-9]+)')
+_ordinal_re = re.compile(r'[0-9]+(st|nd|rd|th)')
+_number_re = re.compile(r'[0-9]+')
+
+
+def _remove_commas(m):
+  return m.group(1).replace(',', '')
+
+
+def _expand_decimal_point(m):
+  return m.group(1).replace('.', ' point ')
+
+
+def _expand_dollars(m):
+  match = m.group(1)
+  parts = match.split('.')
+  if len(parts) > 2:
+    return match + ' dollars'  # Unexpected format
+  dollars = int(parts[0]) if parts[0] else 0
+  cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
+  if dollars and cents:
+    dollar_unit = 'dollar' if dollars == 1 else 'dollars'
+    cent_unit = 'cent' if cents == 1 else 'cents'
+    return '%s %s, %s %s' % (dollars, dollar_unit, cents, cent_unit)
+  elif dollars:
+    dollar_unit = 'dollar' if dollars == 1 else 'dollars'
+    return '%s %s' % (dollars, dollar_unit)
+  elif cents:
+    cent_unit = 'cent' if cents == 1 else 'cents'
+    return '%s %s' % (cents, cent_unit)
+  else:
+    return 'zero dollars'
+
+
+def _expand_ordinal(m):
+  return _inflect.number_to_words(m.group(0))
+
+
+def _expand_number(m):
+  num = int(m.group(0))
+  if num > 1000 and num < 3000:
+    if num == 2000:
+      return 'two thousand'
+    elif num > 2000 and num < 2010:
+      return 'two thousand ' + _inflect.number_to_words(num % 100)
+    elif num % 100 == 0:
+      return _inflect.number_to_words(num // 100) + ' hundred'
+    else:
+      return _inflect.number_to_words(num, andword='', zero='oh', group=2).replace(', ', ' ')
+  else:
+    return _inflect.number_to_words(num, andword='')
+
+
+def normalize_numbers(text):
+  text = re.sub(_comma_number_re, _remove_commas, text)
+  text = re.sub(_pounds_re, r'\1 pounds', text)
+  text = re.sub(_dollars_re, _expand_dollars, text)
+  text = re.sub(_decimal_number_re, _expand_decimal_point, text)
+  text = re.sub(_ordinal_re, _expand_ordinal, text)
+  text = re.sub(_number_re, _expand_number, text)
+  return text
+
+
+def expand_numbers(text):
+  return normalize_numbers(text)
+
+
+def lowercase(text):
+  return text.lower()
+
+
+def collapse_whitespace(text):
+  return re.sub(_whitespace_re, ' ', text)
+
+
+def convert_to_ascii(text):
+  return unidecode(text)
+
+
+def basic_cleaners(text):
+  '''Basic pipeline that lowercases and collapses whitespace without transliteration.'''
+  text = lowercase(text)
+  text = collapse_whitespace(text)
+  return text
+
+
+def transliteration_cleaners(text):
+  '''Pipeline for non-English text that transliterate to ASCII.'''
+  text = convert_to_ascii(text)
+  text = lowercase(text)
+  text = collapse_whitespace(text)
+  return text
+
+
+def english_cleaners(text):
+  '''Pipeline for English text, including number and abbreviation expansion.'''
+  text = convert_to_ascii(text)
+  text = lowercase(text)
+  text = expand_numbers(text)
+  text = expand_abbreviations(text)
+  text = collapse_whitespace(text)
+  text = text.replace('"', '')
+  return text
+
+
+def lev_distance(s1, s2):
+  if len(s1) > len(s2):
+    s1, s2 = s2, s1
+
+  distances = range(len(s1) + 1)
+  for i2, c2 in enumerate(s2):
+    distances_ = [i2 + 1]
+    for i1, c1 in enumerate(s1):
+      if c1 == c2:
+        distances_.append(distances[i1])
+      else:
+        distances_.append(1 + min((distances[i1], distances[i1 + 1], distances_[-1])))
+    distances = distances_
+  return distances[-1]
+
+
+DEFAULT_VOCAB_FILE = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../data/tokenizer.json')
+
+
+class VoiceBpeTokenizer:
+    def __init__(self, vocab_file=None, use_basic_cleaners=False):
+        self.tokenizer = Tokenizer.from_file(
+          DEFAULT_VOCAB_FILE if vocab_file is None else vocab_file
+        )
+        if use_basic_cleaners:
+            self.preprocess_text = basic_cleaners
+        else:
+            self.preprocess_text = english_cleaners
+
+    def encode(self, txt):
+        txt = self.preprocess_text(txt)
+        txt = txt.replace(' ', '[SPACE]')
+        return self.tokenizer.encode(txt).ids
+
+    def decode(self, seq):
+        if isinstance(seq, torch.Tensor):
+            seq = seq.cpu().numpy()
+        txt = self.tokenizer.decode(seq, skip_special_tokens=False).replace(' ', '')
+        txt = txt.replace('[SPACE]', ' ')
+        txt = txt.replace('[STOP]', '')
+        txt = txt.replace('[UNK]', '')
+        return txt
diff --git a/tortoise/tortoise/utils/typical_sampling.py b/tortoise/tortoise/utils/typical_sampling.py
new file mode 100644
index 0000000..ff6bf48
--- /dev/null
+++ b/tortoise/tortoise/utils/typical_sampling.py
@@ -0,0 +1,33 @@
+import torch
+from transformers import LogitsWarper
+
+
+class TypicalLogitsWarper(LogitsWarper):
+    def __init__(self, mass: float = 0.9, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
+        self.filter_value = filter_value
+        self.mass = mass
+        self.min_tokens_to_keep = min_tokens_to_keep
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+        # calculate entropy
+        normalized = torch.nn.functional.log_softmax(scores, dim=-1)
+        p = torch.exp(normalized)
+        ent = -(normalized * p).nansum(-1, keepdim=True)
+
+        # shift and sort
+        shifted_scores = torch.abs((-normalized) - ent)
+        sorted_scores, sorted_indices = torch.sort(shifted_scores, descending=False)
+        sorted_logits = scores.gather(-1, sorted_indices)
+        cumulative_probs = sorted_logits.softmax(dim=-1).cumsum(dim=-1)
+
+        # Remove tokens with cumulative mass above the threshold
+        last_ind = (cumulative_probs < self.mass).sum(dim=1)
+        last_ind[last_ind < 0] = 0
+        sorted_indices_to_remove = sorted_scores > sorted_scores.gather(1, last_ind.view(-1, 1))
+        if self.min_tokens_to_keep > 1:
+            # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
+            sorted_indices_to_remove[..., : self.min_tokens_to_keep] = 0
+        indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+
+        scores = scores.masked_fill(indices_to_remove, self.filter_value)
+        return scores
\ No newline at end of file
diff --git a/tortoise/tortoise/utils/wav2vec_alignment.py b/tortoise/tortoise/utils/wav2vec_alignment.py
new file mode 100644
index 0000000..adc39e3
--- /dev/null
+++ b/tortoise/tortoise/utils/wav2vec_alignment.py
@@ -0,0 +1,150 @@
+import re
+
+import torch
+import torchaudio
+from transformers import Wav2Vec2ForCTC, Wav2Vec2FeatureExtractor, Wav2Vec2CTCTokenizer, Wav2Vec2Processor
+
+from tortoise.utils.audio import load_audio
+
+
+def max_alignment(s1, s2, skip_character='~', record=None):
+    """
+    A clever function that aligns s1 to s2 as best it can. Wherever a character from s1 is not found in s2, a '~' is
+    used to replace that character.
+
+    Finally got to use my DP skills!
+    """
+    if record is None:
+        record = {}
+    assert skip_character not in s1, f"Found the skip character {skip_character} in the provided string, {s1}"
+    if len(s1) == 0:
+        return ''
+    if len(s2) == 0:
+        return skip_character * len(s1)
+    if s1 == s2:
+        return s1
+    if s1[0] == s2[0]:
+        return s1[0] + max_alignment(s1[1:], s2[1:], skip_character, record)
+
+    take_s1_key = (len(s1), len(s2) - 1)
+    if take_s1_key in record:
+        take_s1, take_s1_score = record[take_s1_key]
+    else:
+        take_s1 = max_alignment(s1, s2[1:], skip_character, record)
+        take_s1_score = len(take_s1.replace(skip_character, ''))
+        record[take_s1_key] = (take_s1, take_s1_score)
+
+    take_s2_key = (len(s1) - 1, len(s2))
+    if take_s2_key in record:
+        take_s2, take_s2_score = record[take_s2_key]
+    else:
+        take_s2 = max_alignment(s1[1:], s2, skip_character, record)
+        take_s2_score = len(take_s2.replace(skip_character, ''))
+        record[take_s2_key] = (take_s2, take_s2_score)
+
+    return take_s1 if take_s1_score > take_s2_score else skip_character + take_s2
+
+
+class Wav2VecAlignment:
+    """
+    Uses wav2vec2 to perform audio<->text alignment.
+    """
+    def __init__(self, device='cuda' if not torch.backends.mps.is_available() else 'mps'):
+        self.model = Wav2Vec2ForCTC.from_pretrained("jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli").cpu()
+        self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"facebook/wav2vec2-large-960h")
+        self.tokenizer = Wav2Vec2CTCTokenizer.from_pretrained('jbetker/tacotron-symbols')
+        self.device = device
+
+    def align(self, audio, expected_text, audio_sample_rate=24000):
+        orig_len = audio.shape[-1]
+
+        with torch.no_grad():
+            self.model = self.model.to(self.device)
+            audio = audio.to(self.device)
+            audio = torchaudio.functional.resample(audio, audio_sample_rate, 16000)
+            clip_norm = (audio - audio.mean()) / torch.sqrt(audio.var() + 1e-7)
+            logits = self.model(clip_norm).logits
+            self.model = self.model.cpu()
+
+        logits = logits[0]
+        pred_string = self.tokenizer.decode(logits.argmax(-1).tolist())
+
+        fixed_expectation = max_alignment(expected_text.lower(), pred_string)
+        w2v_compression = orig_len // logits.shape[0]
+        expected_tokens = self.tokenizer.encode(fixed_expectation)
+        expected_chars = list(fixed_expectation)
+        if len(expected_tokens) == 1:
+            return [0]  # The alignment is simple; there is only one token.
+        expected_tokens.pop(0)  # The first token is a given.
+        expected_chars.pop(0)
+
+        alignments = [0]
+        def pop_till_you_win():
+            if len(expected_tokens) == 0:
+                return None
+            popped = expected_tokens.pop(0)
+            popped_char = expected_chars.pop(0)
+            while popped_char == '~':
+                alignments.append(-1)
+                if len(expected_tokens) == 0:
+                    return None
+                popped = expected_tokens.pop(0)
+                popped_char = expected_chars.pop(0)
+            return popped
+
+        next_expected_token = pop_till_you_win()
+        for i, logit in enumerate(logits):
+            top = logit.argmax()
+            if next_expected_token == top:
+                alignments.append(i * w2v_compression)
+                if len(expected_tokens) > 0:
+                    next_expected_token = pop_till_you_win()
+                else:
+                    break
+
+        pop_till_you_win()
+        if not (len(expected_tokens) == 0 and len(alignments) == len(expected_text)):
+            torch.save([audio, expected_text], 'alignment_debug.pth')
+            assert False, "Something went wrong with the alignment algorithm. I've dumped a file, 'alignment_debug.pth' to" \
+                          "your current working directory. Please report this along with the file so it can get fixed."
+
+        # Now fix up alignments. Anything with -1 should be interpolated.
+        alignments.append(orig_len)  # This'll get removed but makes the algorithm below more readable.
+        for i in range(len(alignments)):
+            if alignments[i] == -1:
+                for j in range(i+1, len(alignments)):
+                    if alignments[j] != -1:
+                        next_found_token = j
+                        break
+                for j in range(i, next_found_token):
+                    gap = alignments[next_found_token] - alignments[i-1]
+                    alignments[j] = (j-i+1) * gap // (next_found_token-i+1) + alignments[i-1]
+
+        return alignments[:-1]
+
+    def redact(self, audio, expected_text, audio_sample_rate=24000):
+        if '[' not in expected_text:
+            return audio
+        splitted = expected_text.split('[')
+        fully_split = [splitted[0]]
+        for spl in splitted[1:]:
+            assert ']' in spl, 'Every "[" character must be paired with a "]" with no nesting.'
+            fully_split.extend(spl.split(']'))
+
+        # At this point, fully_split is a list of strings, with every other string being something that should be redacted.
+        non_redacted_intervals = []
+        last_point = 0
+        for i in range(len(fully_split)):
+            if i % 2 == 0 and fully_split[i] != "": # Check for empty string fixes index error
+                end_interval = max(0, last_point + len(fully_split[i]) - 1)
+                non_redacted_intervals.append((last_point, end_interval))
+            last_point += len(fully_split[i])
+
+        bare_text = ''.join(fully_split)
+        alignments = self.align(audio, bare_text, audio_sample_rate)
+
+        output_audio = []
+        for nri in non_redacted_intervals:
+            start, stop = nri
+            output_audio.append(audio[:, alignments[start]:alignments[stop]])
+        return torch.cat(output_audio, dim=-1)
diff --git a/tortoise/tortoise/voices/angie/1.wav b/tortoise/tortoise/voices/angie/1.wav
new file mode 100644
index 0000000..6f8480a
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diff --git a/tortoise/tortoise/voices/angie/2.wav b/tortoise/tortoise/voices/angie/2.wav
new file mode 100644
index 0000000..f053ebd
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+<html><head><meta charset="UTF-8"><title>TorToiSe - These words were never spoken.</title></head>
+<body>
+<h1>Introduction 🐢 </h1>
+<p>TorToiSe is a text-to-speech program built in April 2022 by jbetker@. TorToiSe is open source, with trained model weights
+available at <a href="https://github.com/neonbjb/tortoise-tts">https://github.com/neonbjb/tortoise-tts</a></p>
+
+<p>This page demonstrates some of the results of TorToiSe.</p>
+
+<h1>Handpicked results 🐢 </h1>
+<p>Following are several particularly good results generated by the model.</p>
+
+<h2>Short-form</h2>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/atkins_mha.mp3" type="audio/mp3"></audio><br>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/atkins_omicron.mp3" type="audio/mp3"></audio><br>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/atkins_value.mp3" type="audio/mp3"></audio><br>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/daniel_craig_dumbledore.mp3" type="audio/mp3"></audio><br>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/daniel_craig_training_ethics.mp3" type="audio/mp3"></audio><br>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/dotrice_stop_for_death.mp3" type="audio/mp3"></audio><br>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/emma_stone_courage.mp3" type="audio/mp3"></audio><br>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/emma_stone_training_ethics.mp3" type="audio/mp3"></audio><br>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/halle_barry_dumbledore.mp3" type="audio/mp3"></audio><br>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/halle_barry_oar_to_oar.mp3" type="audio/mp3"></audio><br>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/henry_cavill_metallic_hydrogen.mp3" type="audio/mp3"></audio><br>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/kennard_road_not_taken.mp3" type="audio/mp3"></audio><br>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/morgan_freeman_metallic_hydrogen.mp3" type="audio/mp3"></audio><br>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/myself_gatsby.mp3" type="audio/mp3"></audio><br>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/patrick_stewart_omicron.mp3" type="audio/mp3"></audio><br>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/patrick_stewart_secret_of_life.mp3" type="audio/mp3"></audio><br>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/robert_deniro_review.mp3" type="audio/mp3"></audio><br>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorites/william_shatner_spacecraft_interview.mp3" type="audio/mp3"></audio><br>
+
+<h2>Long-form</h2>
+<audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/favorite_riding_hood.mp3" type="audio/mp3"></audio><br>
+
+<h1>Comparisons (with the LJSpeech voice): 🐢 </h1>
+<p>LJSpeech is a popular dataset used to train small-scale TTS models. TorToiSe is a multi-voice model, following is how
+it renders the LJSpeech voice with and without fine-tuning, compared with results for the same text from the popular Tacotron2
+model paired with the Waveglow vocoder.</p>
+<table><th>Tacotron2+Waveglow</th><th>TorToiSe</th><th>TorToiSe Finetuned</th><tr>
+    <td><audio controls="" style="width: 300px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/tacotron_comparison/2-tacotron2.mp3" type="audio/mp3"></audio><br>
+</td>
+    <td><audio controls="" style="width: 300px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/tacotron_comparison/2-tortoise.mp3" type="audio/mp3"></audio><br></td>
+    <td><audio controls="" style="width: 300px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/finetuned/lj/2.mp3" type="audio/mp3"></audio><br></td>
+</tr><tr><td><audio controls="" style="width: 300px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/tacotron_comparison/3-tacotron2.mp3" type="audio/mp3"></audio><br></td>
+    <td><audio controls="" style="width: 300px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/tacotron_comparison/3-tortoise.mp3" type="audio/mp3"></audio><br></td>
+
+    <td><audio controls="" style="width: 300px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/finetuned/lj/3.mp3" type="audio/mp3"></audio><br></td>
+</tr><tr><td><audio controls="" style="width: 300px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/tacotron_comparison/4-tacotron2.mp3" type="audio/mp3"></audio><br>
+</td><td><audio controls="" style="width: 300px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/tacotron_comparison/4-tortoise.mp3" type="audio/mp3"></audio><br></td>
+
+    <td><audio controls="" style="width: 300px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/finetuned/lj/4.mp3" type="audio/mp3"></audio><br></td>
+</tr></table>
+<p>NaturalVoice is a SOTA TTS engine developed by Microsoft Research Asia in May 2022. It features realistic prosody
+and end-to-end generation with no need for a vocoder. While not much has actually been released about this model other
+than five samples, those samples are quite good and I would consider this the most competitive TTS engine out there
+right now.</p>
+<table><th>Natural Voice</th><th>TorToiSe Finetuned</th>
+<tr><td><audio controls="" style="width: 300px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/naturalspeech_comparison/lax/naturalspeech.mp3" type="audio/mp3"></audio><br></td>
+<td><audio controls="" style="width: 300px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/naturalspeech_comparison/lax/tortoise.mp3" type="audio/mp3"></audio><br></td>
+</tr><tr><td><audio controls="" style="width: 300px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/naturalspeech_comparison/maltby/naturalspeech.mp3" type="audio/mp3"></audio><br></td>
+<td><audio controls="" style="width: 300px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/naturalspeech_comparison/maltby/tortoise.mp3" type="audio/mp3"></audio><br></td>
+</tr><tr><td><audio controls="" style="width: 300px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/naturalspeech_comparison/fibers/naturalspeech.mp3" type="audio/mp3"></audio><br>
+</td><td><audio controls="" style="width: 300px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/naturalspeech_comparison/fibers/tortoise.mp3" type="audio/mp3"></audio><br></td>
+</tr></table>
+<p>It is important to note that it is not actually fair to compare any of these models: Tortoise is a multi-voice probabilistic
+model trained on millions of hours of speech with an exceptionally slow inference time. Tacotron and NaturalVoice are efficient,
+fast, single-voice models trained on 24 hours of speech. Unfortunately, there isn't much in the way of actually comparable
+research to Tortoise.</p>
+
+<h1>All Results 🐢</h1>
+<p>    Following are all the results from which the hand-picked results were drawn from. Also included is the reference
+    audio that the program is trying to mimic. This will give you a better sense of how TorToiSe really performs.</p>
+
+<h2>Short-form</h2>
+<table><th>text</th><th>angie</th><th>daniel</th><th>deniro</th><th>emma</th><th>freeman</th><th>geralt</th><th>halle</th><th>jlaw</th><th>lj</th><th>myself</th><th>pat</th><th>snakes</th><th>tom</th><th>train_atkins</th><th>train_dotrice</th><th>train_kennard</th><th>weaver</th><th>william</th>
+<tr><td>reference clip</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/angie/1.wav" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/daniel/1.wav" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/deniro/1.wav" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/emma/1.wav" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/freeman/1.wav" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/geralt/1.wav" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/halle/1.wav" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/jlaw/1.wav" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/lj/1.wav" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/myself/1.wav" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/pat/1.wav" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/snakes/1.wav" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/tom/1.wav" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/train_atkins/1.wav" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/train_dotrice/1.wav" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/train_kennard/1.wav" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/weaver/1.wav" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/tortoise/voices/william/1.wav" type="audio/mp3"></audio></td></tr>
+<tr><td>autoregressive_ml</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/autoregressive_ml/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>bengio_it_needs_to_know_what_is_bad</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/bengio_it_needs_to_know_what_is_bad/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>dickinson_stop_for_death</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/dickinson_stop_for_death/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>espn_basketball</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/espn_basketball/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>frost_oar_to_oar</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_oar_to_oar/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>frost_road_not_taken</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/frost_road_not_taken/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>gatsby_and_so_we_beat_on</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/gatsby_and_so_we_beat_on/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>harrypotter_differences_of_habit_and_language</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/harrypotter_differences_of_habit_and_language/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>i_am_a_language_model</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/i_am_a_language_model/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>melodie_kao</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/melodie_kao/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>nyt_covid</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/nyt_covid/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>real_courage_is_when_you_know_your_licked</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/real_courage_is_when_you_know_your_licked/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>rolling_stone_review</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/rolling_stone_review/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>spacecraft_interview</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/spacecraft_interview/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>tacotron2_sample1</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample1/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>tacotron2_sample2</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample2/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>tacotron2_sample3</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample3/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>tacotron2_sample4</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/tacotron2_sample4/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>watts_this_is_the_real_secret_of_life</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/watts_this_is_the_real_secret_of_life/william.mp3" type="audio/mp3"></audio></td></tr>
+<tr><td>wilde_nowadays_people_know_the_price</td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/angie.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/daniel.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/deniro.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/emma.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/freeman.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/geralt.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/halle.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/jlaw.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/lj.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/myself.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/pat.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/snakes.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/tom.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/train_atkins.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/train_dotrice.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/train_kennard.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/weaver.mp3" type="audio/mp3"></audio></td><td><audio controls="" style="width: 150px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/various/wilde_nowadays_people_know_the_price/william.mp3" type="audio/mp3"></audio></td></tr></table>
+
+<h2>Long-form</h2>
+<b>Angelina:</b> <audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/riding_hood/angelina.mp3" type="audio/mp3"></audio><br>
+<b>Craig:</b><audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/riding_hood/craig.mp3" type="audio/mp3"></audio><br>
+<b>Deniro:</b><audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/riding_hood/deniro.mp3" type="audio/mp3"></audio><br>
+<b>Emma:</b><audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/riding_hood/emma.mp3" type="audio/mp3"></audio><br>
+<b>Freeman:</b><audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/riding_hood/freeman.mp3" type="audio/mp3"></audio><br>
+<b>Geralt:</b><audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/riding_hood/geralt.mp3" type="audio/mp3"></audio><br>
+<b>Halle:</b><audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/riding_hood/halle.mp3" type="audio/mp3"></audio><br>
+<b>Jlaw:</b><audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/riding_hood/jlaw.mp3" type="audio/mp3"></audio><br>
+<b>LJ:</b><audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/riding_hood/lj.mp3" type="audio/mp3"></audio><br>
+<b>Myself:</b><audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/riding_hood/myself.mp3" type="audio/mp3"></audio><br>
+<b>Pat:</b><audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/riding_hood/pat.mp3" type="audio/mp3"></audio><br>
+<b>Snakes:</b><audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/riding_hood/snakes.mp3" type="audio/mp3"></audio><br>
+<b>Tom:</b><audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/riding_hood/tom.mp3" type="audio/mp3"></audio><br>
+<b>Weaver:</b><audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/riding_hood/weaver.mp3" type="audio/mp3"></audio><br>
+<b>William:</b><audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/riding_hood/william.mp3" type="audio/mp3"></audio><br>
+
+<h1>Prompt Engineering 🐢</h1>
+<p>Tortoise is capable of "prompt-engineering" in that tone and prosody is affected by the emotions inflected in the words
+fed to the program. For example, prompting the model with "[I am so angry,] I went to the park and threw a ball" will
+result in it outputting "I went to the park and threw the ball" with an angry tone.</p>
+
+<p>Following are a few examples of different prompts. The effect is subtle, but is definitely there. Many voices are
+less effected by this.</p>
+
+<b>Angry:</b> <audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/prompting/angry.mp3" type="audio/mp3"></audio><br>
+<b>Sad:</b><audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/prompting/sad.mp3" type="audio/mp3"></audio><br>
+<b>Happy:</b><audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/prompting/happy.mp3" type="audio/mp3"></audio><br>
+<b>Scared:</b><audio controls="" style="width: 600px;"><source src="https://github.com/neonbjb/tortoise-tts/raw/main/examples/prompting/scared.mp3" type="audio/mp3"></audio><br>
+
+</body></html>
diff --git a/tortoise/voice_customization_guide.md b/tortoise/voice_customization_guide.md
new file mode 100644
index 0000000..5afe6fc
--- /dev/null
+++ b/tortoise/voice_customization_guide.md
@@ -0,0 +1,34 @@
+## Voice Customization Guide
+
+Tortoise was specifically trained to be a multi-speaker model. It accomplishes this by consulting reference clips.
+
+These reference clips are recordings of a speaker that you provide to guide speech generation. These clips are used to determine many properties of the output, such as the pitch and tone of the voice, speaking speed, and even speaking defects like a lisp or stuttering. The reference clip is also used to determine non-voice related aspects of the audio output like volume, background noise, recording quality and reverb.
+
+### Provided voices
+
+This repo comes with several pre-packaged voices. Voices prepended with "train_" came from the training set and perform
+far better than the others. If your goal is high quality speech, I recommend you pick one of them. If you want to see
+what Tortoise can do for zero-shot mimicking, take a look at the others.
+
+### Adding a new voice
+
+To add new voices to Tortoise, you will need to do the following:
+
+1. Gather audio clips of your speaker(s). Good sources are YouTube interviews (you can use youtube-dl to fetch the audio), audiobooks or podcasts. Guidelines for good clips are in the next section.
+2. Cut your clips into ~10 second segments. You want at least 3 clips. More is better, but I only experimented with up to 5 in my testing.
+3. Save the clips as a WAV file with floating point format and a 22,050 sample rate.
+4. Create a subdirectory in voices/
+5. Put your clips in that subdirectory.
+6. Run tortoise utilities with --voice=<your_subdirectory_name>.
+
+### Picking good reference clips
+
+As mentioned above, your reference clips have a profound impact on the output of Tortoise. Following are some tips for picking
+good clips:
+
+1. Avoid clips with background music, noise or reverb. These clips were removed from the training dataset. Tortoise is unlikely to do well with them.
+2. Avoid speeches. These generally have distortion caused by the amplification system.
+3. Avoid clips from phone calls.
+4. Avoid clips that have excessive stuttering, stammering or words like "uh" or "like" in them.
+5. Try to find clips that are spoken in such a way as you wish your output to sound like. For example, if you want to hear your target voice read an audiobook, try to find clips of them reading a book.
+6. The text being spoken in the clips does not matter, but diverse text does seem to perform better.