diff --git a/.coverage b/.coverage new file mode 100644 index 0000000..87b1aed Binary files /dev/null and b/.coverage differ diff --git a/.darglint b/.darglint new file mode 100644 index 0000000..72ccc6c --- /dev/null +++ b/.darglint @@ -0,0 +1,2 @@ +[darglint] +strictness = long diff --git a/.dockerignore b/.dockerignore index d6d95cf..604f0f2 100644 --- a/.dockerignore +++ b/.dockerignore @@ -1 +1 @@ -models \ No newline at end of file +models diff --git a/.env b/.env index 535064f..603ce01 100644 --- a/.env +++ b/.env @@ -6,4 +6,5 @@ DEBUG=True BATCH_SIZE=4 MAX_WAIT=0.1 WHISPER_MODEL="large-v2" -EMBEDDING_MODEL="speechbrain/spkrec-ecapa-voxceleb" +EMBEDDINGS_MODEL="speechbrain/spkrec-ecapa-voxceleb" +COMPUTE_TYPE="int8_float16" diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000..f587e22 --- /dev/null +++ b/.flake8 @@ -0,0 +1,9 @@ +[flake8] +select = B,B9,C,D,DAR,E,F,N,RST,S,W +ignore = D212,E203,E501,RST201,RST203,RST301,W503,S106 +max-line-length = 119 +max-complexity = 10 +docstring-convention = google +per-file-ignores = tests/*:S101 +rst-roles = class,const,func,meth,mod,ref +rst-directives = deprecated diff --git a/.github/workflows/constraints.txt b/.github/workflows/constraints.txt new file mode 100644 index 0000000..2bd70ae --- /dev/null +++ b/.github/workflows/constraints.txt @@ -0,0 +1,5 @@ +pip==23.0 +nox==2022.11.21 +nox-poetry==1.0.2 +poetry==1.4.1 +virtualenv==20.18.0 diff --git a/.github/workflows/quality.yml b/.github/workflows/quality.yml new file mode 100644 index 0000000..d82f473 --- /dev/null +++ b/.github/workflows/quality.yml @@ -0,0 +1,162 @@ +name: Quality Checks + +on: + pull_request: + branches: + - main + +jobs: + quality: + name: ${{ matrix.session }} ${{ matrix.python }} / ${{ matrix.os }} + runs-on: ${{ matrix.os }} + strategy: + fail-fast: true + matrix: + include: + - { python: "3.10", os: "ubuntu-latest", session: "pre-commit" } + env: + NOXSESSION: ${{ matrix.session }} + FORCE_COLOR: "1" + PRE_COMMIT_COLOR: "always" + steps: + - name: Check out the repository + uses: actions/checkout@v3 + + - name: Set up Python ${{ matrix.python }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python }} + + - name: Upgrade pip + run: | + pip install --constraint=.github/workflows/constraints.txt pip + pip --version + - name: Upgrade pip in virtual environments + shell: python + run: | + import os + import pip + with open(os.environ["GITHUB_ENV"], mode="a") as io: + print(f"VIRTUALENV_PIP={pip.__version__}", file=io) + - name: Install Poetry + run: | + pipx install --pip-args=--constraint=.github/workflows/constraints.txt poetry + poetry --version + - name: Install Nox + run: | + pipx install --pip-args=--constraint=.github/workflows/constraints.txt nox + pipx inject --pip-args=--constraint=.github/workflows/constraints.txt nox nox-poetry + nox --version + - name: Compute pre-commit cache key + if: matrix.session == 'pre-commit' + id: pre-commit-cache + shell: python + run: | + import hashlib + import sys + python = "py{}.{}".format(*sys.version_info[:2]) + payload = sys.version.encode() + sys.executable.encode() + digest = hashlib.sha256(payload).hexdigest() + result = "${{ runner.os }}-{}-{}-pre-commit".format(python, digest[:8]) + print("::set-output name=result::{}".format(result)) + - name: Restore pre-commit cache + uses: actions/cache@v3 + if: matrix.session == 'pre-commit' + with: + path: ~/.cache/pre-commit + key: ${{ steps.pre-commit-cache.outputs.result }}-${{ hashFiles('.pre-commit-config.yaml') }} + restore-keys: | + ${{ steps.pre-commit-cache.outputs.result }}- + - name: Run Nox + run: | + nox --python=${{ matrix.python }} + # tests: + # name: ${{ matrix.session }} ${{ matrix.python }} / ${{ matrix.os }} + # runs-on: ${{ matrix.os }} + # needs: [quality] + # strategy: + # fail-fast: true + # matrix: + # include: + # - { python: "3.10", os: "ubuntu-latest", session: "tests" } + # env: + # NOXSESSION: ${{ matrix.session }} + # FORCE_COLOR: "1" + # PRE_COMMIT_COLOR: "always" + # WORDCAB_API_KEY: ${{ secrets.WORDCAB_API_KEY }} + + # steps: + # - name: Check out the repository + # uses: actions/checkout@v3 + + # - name: Set up Python ${{ matrix.python }} + # uses: actions/setup-python@v4 + # with: + # python-version: ${{ matrix.python }} + + # - name: Upgrade pip + # run: | + # pip install --constraint=.github/workflows/constraints.txt pip + # pip --version + # - name: Upgrade pip in virtual environments + # shell: python + # run: | + # import os + # import pip + # with open(os.environ["GITHUB_ENV"], mode="a") as io: + # print(f"VIRTUALENV_PIP={pip.__version__}", file=io) + # - name: Install Poetry + # run: | + # pipx install --pip-args=--constraint=.github/workflows/constraints.txt poetry + # poetry --version + # - name: Install Nox + # run: | + # pipx install --pip-args=--constraint=.github/workflows/constraints.txt nox + # pipx inject --pip-args=--constraint=.github/workflows/constraints.txt nox nox-poetry + # nox --version + # - name: Run Nox + # run: | + # nox --python=${{ matrix.python }} + # - name: Upload coverage data + # if: always() && matrix.session == 'tests' + # uses: "actions/upload-artifact@v3" + # with: + # name: coverage-data + # path: ".coverage.*" + + # coverage: + # runs-on: ubuntu-latest + # needs: tests + # steps: + # - name: Check out the repository + # uses: actions/checkout@v3 + + # - name: Set up Python + # uses: actions/setup-python@v4 + # with: + # python-version: "3.10" + + # - name: Upgrade pip + # run: | + # pip install --constraint=.github/workflows/constraints.txt pip + # pip --version + # - name: Install Poetry + # run: | + # pipx install --pip-args=--constraint=.github/workflows/constraints.txt poetry + # poetry --version + # - name: Install Nox + # run: | + # pipx install --pip-args=--constraint=.github/workflows/constraints.txt nox + # pipx inject --pip-args=--constraint=.github/workflows/constraints.txt nox nox-poetry + # nox --version + # - name: Download coverage data + # uses: actions/download-artifact@v3 + # with: + # name: coverage-data + + # - name: Combine coverage data and display human readable report + # run: | + # nox --session=coverage + # - name: Create coverage report + # run: | + # nox --session=coverage -- xml diff --git a/.gitignore b/.gitignore index a2128a8..5259b23 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,15 @@ -__pycache__ +__pycache__/ *.mp3 *.wav +.mypy_cache/ +/.coverage +/.coverage.* +/.nox/ +/.python-version +/.pytype/ +/dist/ +/docs/_build/ +/src/*.egg-info/ +.DS_Store +test.ipynb +test.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..c95d78a --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,66 @@ +repos: + - repo: local + hooks: + - id: black + name: black + entry: black + language: system + types: [python] + require_serial: true + - id: check-added-large-files + name: Check for added large files + entry: check-added-large-files + language: system + - id: check-toml + name: Check Toml + entry: check-toml + language: system + types: [toml] + - id: check-yaml + name: Check Yaml + entry: check-yaml + language: system + types: [yaml] + - id: darglint + name: darglint + entry: darglint + language: system + types: [python] + stages: [manual] + - id: end-of-file-fixer + name: Fix End of Files + entry: end-of-file-fixer + language: system + types: [text] + stages: [commit, push, manual] + - id: flake8 + name: flake8 + entry: flake8 + language: system + types: [python] + require_serial: true + args: [--darglint-ignore-regex, .*] + - id: isort + name: isort + entry: isort + require_serial: true + language: system + types_or: [cython, pyi, python] + args: ["--filter-files"] + - id: pyupgrade + name: pyupgrade + description: Automatically upgrade syntax for newer versions. + entry: pyupgrade + language: system + types: [python] + args: [--py37-plus] + - id: trailing-whitespace + name: Trim Trailing Whitespace + entry: trailing-whitespace-fixer + language: system + types: [text] + stages: [commit, push, manual] + - repo: https://github.com/pre-commit/mirrors-prettier + rev: v2.6.0 + hooks: + - id: prettier diff --git a/Dockerfile b/Dockerfile index 77bed91..0434322 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,5 +1,6 @@ FROM nvidia/cuda:11.7.0-devel-ubuntu22.04 +COPY requirements.txt /requirements.txt RUN apt-get update && apt-get install -y \ git \ curl \ @@ -13,8 +14,6 @@ RUN add-apt-repository ppa:deadsnakes/ppa \ RUN python3.10 -m pip install -r requirements.txt RUN python3.10 -m pip install --upgrade torch==1.13.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117 -COPY requirements.txt /requirements.txt - COPY . /app WORKDIR /app diff --git a/README.md b/README.md index f662e00..2839ecc 100644 --- a/README.md +++ b/README.md @@ -1,28 +1,40 @@ -# Wordcab Transcribe +# Wordcab Transcribe 💬 FastAPI based API for transcribing audio files using [`faster-whisper`](https://github.com/guillaumekln/faster-whisper) and [`pyannote-audio`](https://github.com/pyannote/pyannote-audio) More details on this project on this [blog post](https://wordcab.github.io/wordcab-posts/blog/2023/03/31/wordcab-transcribe/). -## Requirements +## Key features + +- 🤗 Open-source: Our project is open-source and based on open-source libraries, allowing you to customize and extend it as needed. +- ⚡ Fast: The faster-whisper library and CTranslate2 make audio processing incredibly fast compared to other implementations. +- 🐳 Easy to deploy: You can deploy the project on your workstation or in the cloud using Docker. +- 🔥 Batch requests: You can transcribe multiple audio files at once because batch requests are implemented in the API. +- 💸 Cost-effective: As an open-source solution, you won't have to pay for costly ASR platforms. +- 🫶 Easy-to-use API: With just a few lines of code, you can use the API to transcribe audio files or even YouTube videos. -* Python 3.10 -* Docker -* NVIDIA GPU + NVIDIA Container Toolkit +## Requirements +- Python 3.10 +- Docker +- NVIDIA GPU + NVIDIA Container Toolkit ## Docker commands +Build the image. + ```bash docker build -t wordcab-transcribe:latest . +``` + +Run the container. + +```bash docker run -d --name wordcab-transcribe \ --gpus all \ - --ipc=host \ - --shm-size 64g \ - --ulimit memlock=1 \ - --ulimit stack=67108864 \ - -p 5001:5001 \ + --shm-size 1g \ --restart unless-stopped \ + -p 5001:5001 \ wordcab-transcribe:latest ``` @@ -52,3 +64,69 @@ files = {"file": open(filepath, "rb")} response = requests.post("http://localhost:5001/api/v1/audio", files=files) print(response.json()) ``` + +## Local testing + +Before launching the API, be sure to install torch and torchaudio on your machine. + +```bash +pip install --upgrade torch==1.13.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117 +``` + +Then, you can launch the API using the following command. + +```bash +poetry run uvicorn wordcab_transcribe.main:app --reload +``` + +## 🚀 Contributing + +### Getting started + +1. Clone the repo + +```bash +git clone +cd wordcab-ask +``` + +2. Install dependencies and start coding + +```bash +poetry install +poetry shell + +# install pre-commit hooks +nox --session=pre-commit -- install + +# open your IDE +code . +``` + +3. Run tests + +```bash +# run all tests +nox + +# run a specific session +nox --session=tests # run tests +nox --session=pre-commit # run pre-commit hooks + +# run a specific test +nox --session=tests -- -k test_something +``` + +### Working workflow + +1. Create an issue for the feature or bug you want to work on. +2. Create a branch using the left panel on GitHub. +3. `git fetch`and `git checkout` the branch. +4. Make changes and commit. +5. Push the branch to GitHub. +6. Create a pull request and ask for review. +7. Merge the pull request when it's approved and CI passes. +8. Delete the branch. +9. Update your local repo with `git fetch` and `git pull`. + +![GitHub Workflow](https://user-images.githubusercontent.com/6351798/48032310-63842400-e114-11e8-8db0-06dc0504dcb5.png) diff --git a/noxfile.py b/noxfile.py new file mode 100644 index 0000000..157aba5 --- /dev/null +++ b/noxfile.py @@ -0,0 +1,169 @@ +"""Nox sessions.""" +import os +import shlex +import sys +from pathlib import Path +from textwrap import dedent + +import nox + + +try: + from nox_poetry import Session, session +except ImportError: + message = f"""\ + Nox failed to import the 'nox-poetry' package. + Please install it using the following command: + {sys.executable} -m pip install nox-poetry""" + raise SystemExit(dedent(message)) from None + + +package = "wordcab" +python_versions = ["3.10"] +nox.needs_version = ">= 2021.6.6" +nox.options.sessions = ( + "pre-commit", + "tests", +) + + +def activate_virtualenv_in_precommit_hooks(session: Session) -> None: + """Activate virtualenv in hooks installed by pre-commit. + + This function patches git hooks installed by pre-commit to activate the + session's virtual environment. This allows pre-commit to locate hooks in + that environment when invoked from git. + + Args: + session: The Session object. + """ + assert session.bin is not None # noqa: S101 + + # Only patch hooks containing a reference to this session's bindir. Support + # quoting rules for Python and bash, but strip the outermost quotes so we + # can detect paths within the bindir, like /python. + bindirs = [ + bindir[1:-1] if bindir[0] in "'\"" else bindir + for bindir in (repr(session.bin), shlex.quote(session.bin)) + ] + + virtualenv = session.env.get("VIRTUAL_ENV") + if virtualenv is None: + return + + headers = { + # pre-commit < 2.16.0 + "python": f"""\ + import os + os.environ["VIRTUAL_ENV"] = {virtualenv!r} + os.environ["PATH"] = os.pathsep.join(( + {session.bin!r}, + os.environ.get("PATH", ""), + )) + """, + # pre-commit >= 2.16.0 + "bash": f"""\ + VIRTUAL_ENV={shlex.quote(virtualenv)} + PATH={shlex.quote(session.bin)}"{os.pathsep}$PATH" + """, + # pre-commit >= 2.17.0 on Windows forces sh shebang + "/bin/sh": f"""\ + VIRTUAL_ENV={shlex.quote(virtualenv)} + PATH={shlex.quote(session.bin)}"{os.pathsep}$PATH" + """, + } + + hookdir = Path(".git") / "hooks" + if not hookdir.is_dir(): + return + + for hook in hookdir.iterdir(): + if hook.name.endswith(".sample") or not hook.is_file(): + continue + + if not hook.read_bytes().startswith(b"#!"): + continue + + text = hook.read_text() + + if not any( + Path("A") == Path("a") and bindir.lower() in text.lower() or bindir in text + for bindir in bindirs + ): + continue + + lines = text.splitlines() + + for executable, header in headers.items(): + if executable in lines[0].lower(): + lines.insert(1, dedent(header)) + hook.write_text("\n".join(lines)) + break + + +@session(name="pre-commit", python=python_versions[0]) +def precommit(session: Session) -> None: + """Lint using pre-commit.""" + args = session.posargs or [ + "run", + "--all-files", + "--hook-stage=manual", + "--show-diff-on-failure", + ] + session.install( + "black", + "darglint", + "flake8", + "flake8-bandit", + "flake8-bugbear", + "flake8-docstrings", + "flake8-rst-docstrings", + "isort", + "pep8-naming", + "pre-commit", + "pre-commit-hooks", + "pyupgrade", + ) + session.run("pre-commit", *args) + if args and args[0] == "install": + activate_virtualenv_in_precommit_hooks(session) + + +@session(python=python_versions) +def tests(session: Session) -> None: + """Run the test suite.""" + session.install(".") + session.install( + "asynctest", + "coverage[toml]", + "httpx", + "pytest", + "pytest-asyncio", + "pytest-mock", + "pygments", + ) + try: + session.run( + "coverage", + "run", + "--parallel", + "-m", + "pytest", + *session.posargs, + ) + finally: + if session.interactive: + session.notify("coverage", posargs=[]) + + +@session(python=python_versions[0]) +def coverage(session: Session) -> None: + """Produce the coverage report.""" + args = session.posargs or ["report"] + + session.install("coverage[toml]") + + if not session.posargs and any(Path().glob(".coverage.*")): + session.run("coverage", "combine") + + session.run("coverage", *args) diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..9db2f18 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,4776 @@ +# This file is 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"dfc11398095af988a33d137dbf92e1e49f717092c9338db55e4ff7db81d506c5" diff --git a/pyproject.toml b/pyproject.toml index bb6c3aa..beea5f9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,14 +7,51 @@ readme = "README.md" [tool.poetry.dependencies] python = ">=3.10,<4.0" +aiofiles = ">=23.1.0" fastapi = ">=0.95.0" faster-whisper = ">=0.3.0" ffmpeg-python = ">=0.2.0" loguru = ">=0.6.0" -pyannote-audio = { version = ">=2.1.1", platform = "linux" } +pyannote-audio = { version = ">=2.1.1", platform = "linux" } # faced problems with pyannote.audio on Mac M1 pydantic = ">=1.10.7" python-dotenv = ">=1.0.0" python-multipart = ">=0.0.6" scikit-learn = ">=1.2.2" +shortuuid = ">=1.0.0" +torch = ">=1.9,<2.0" uvicorn = ">=0.21.1" yt-dlp = ">=2023.3.4" + +[tool.poetry.group.test.dependencies] +black = ">=23.1.0" +coverage = {extras = ["toml"], version = ">=7.2.1"} +darglint = ">=1.8.1" +flake8 = ">=6.0.0" +flake8-bandit = ">=4.1.1" +flake8-bugbear = ">=23.2.13" +flake8-docstrings = ">=1.7.0" +httpx = ">=0.23.3" +isort = ">=5.12.0" +pre-commit = ">=3.2.0" +pre-commit-hooks = ">=4.4.0" +pytest = ">=7.2.2" +pytest-asyncio = ">=0.20.3" +pytest-mock = ">=3.10.0" +pyupgrade = ">=3.3.1" + +[tool.isort] +profile = "black" +force_single_line = false +lines_after_imports = 2 + +[tool.coverage.run] +branch = true +source = ["wordcab_ask", "tests"] + +[tool.coverage.report] +show_missing = true +fail_under = 90 + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/tests/__init__.py b/tests/__init__.py index bb71c4e..21fcd47 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -11,4 +11,4 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""Tests for the Wordcab package.""" \ No newline at end of file +"""Tests for the Wordcab package.""" diff --git a/tests/test_important_files.py b/tests/test_important_files.py new file mode 100644 index 0000000..6ad3377 --- /dev/null +++ b/tests/test_important_files.py @@ -0,0 +1,38 @@ +# Copyright 2023 The Wordcab Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Test important files are present in the project.""" + +import pathlib + + +important_files = [ + ".darglint", + ".flake8", + ".github", + ".gitignore", + ".pre-commit-config.yaml", + "tests", + "wordcab_transcribe", + "poetry.lock", + "pyproject.toml", + "Dockerfile", + "LICENSE", + "README.md", +] + + +def test_important_files_present(): + """Test important files are present in the project.""" + for file in important_files: + assert pathlib.Path(file).exists() diff --git a/wordcab_transcribe/__init__.py b/wordcab_transcribe/__init__.py index 53efbef..29e8840 100644 --- a/wordcab_transcribe/__init__.py +++ b/wordcab_transcribe/__init__.py @@ -11,4 +11,4 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -"""Main module of the Wordcab ASR API.""" \ No newline at end of file +"""Main module of the Wordcab ASR API.""" diff --git a/wordcab_transcribe/config.py b/wordcab_transcribe/config.py index 5f7fd6c..1dd2549 100644 --- a/wordcab_transcribe/config.py +++ b/wordcab_transcribe/config.py @@ -14,22 +14,27 @@ """Configuration module of the Wordcab Transcribe.""" from os import getenv -from dotenv import load_dotenv +from dotenv import load_dotenv from pydantic import BaseSettings class Settings(BaseSettings): + """Configuration settings for the Wordcab Transcribe API.""" + + # Basic API settings project_name: str version: str description: str api_prefix: str debug: bool + # Batch request settings batch_size: int max_wait: float + # Model settings whisper_model: str - embedding_model: str - + embeddings_model: str + compute_type: str load_dotenv() @@ -43,5 +48,6 @@ class Settings(BaseSettings): batch_size=getenv("BATCH_SIZE"), max_wait=getenv("MAX_WAIT"), whisper_model=getenv("WHISPER_MODEL"), - embedding_model=getenv("EMBEDDING_MODEL"), -) \ No newline at end of file + embeddings_model=getenv("EMBEDDINGS_MODEL"), + compute_type=getenv("COMPUTE_TYPE"), +) diff --git a/wordcab_transcribe/main.py b/wordcab_transcribe/main.py index 071322a..558a69c 100644 --- a/wordcab_transcribe/main.py +++ b/wordcab_transcribe/main.py @@ -14,23 +14,24 @@ """Main API module of the Wordcab Transcribe.""" -import random import asyncio +from typing import Optional import aiofiles import shortuuid -from loguru import logger -from typing import Optional - +from fastapi import BackgroundTasks, FastAPI, File, UploadFile from fastapi import status as http_status from fastapi.responses import HTMLResponse -from fastapi import BackgroundTasks, FastAPI, File, UploadFile +from loguru import logger from wordcab_transcribe.config import settings from wordcab_transcribe.models import ASRResponse from wordcab_transcribe.service import ASRService -from wordcab_transcribe.utils import convert_file_to_wav, delete_file, download_file_from_youtube - +from wordcab_transcribe.utils import ( + convert_file_to_wav, + delete_file, + download_file_from_youtube, +) app = FastAPI( @@ -45,13 +46,14 @@ @app.on_event("startup") async def startup_event(): + """Startup event handler.""" logger.debug("Starting up...") asyncio.create_task(asr.runner()) @app.get("/", tags=["status"]) async def health_check(): - """Health check endpoint""" + """Health check endpoint.""" content = f""" @@ -64,7 +66,11 @@ async def health_check():

{settings.project_name}

Version: {settings.version}

{settings.description}

-

Want access? Contact us: info@wordcab.com

+

Want access? Contact us: + + info@wordcab.com + +

@@ -79,39 +85,38 @@ async def health_check(): f"{settings.api_prefix}/audio", tags=["inference"], response_model=ASRResponse, - status_code=http_status.HTTP_200_OK + status_code=http_status.HTTP_200_OK, ) async def inference_with_audio( background_tasks: BackgroundTasks, - file: UploadFile = File(...), + file: UploadFile = File(...), # noqa: B008 num_speakers: Optional[int] = 0, source_lang: Optional[str] = "en", timestamps: Optional[str] = "seconds", -): +) -> ASRResponse: """ - Inference endpoint. + Inference endpoint with audio file. Args: background_tasks (BackgroundTasks): Background tasks dependency. file (UploadFile): Audio file. num_speakers (int): Number of speakers to detect; defaults to 0, which - attempts to detect the number of speaker. + attempts to detect the number of speaker. source_lang (str): The language of the source file; defaults to "en". timestamps (str): The format of the transcript timestamps. Options are "seconds", "milliseconds", or "hms," which stands for hours, minutes, seconds. Defaults to "seconds". Returns: - ASRResponse: Response data. + ASRResponse: Response data as an ASRResponse object. Examples: - import requests filepath = "sample_1.mp3" with open(file, "rb") as f: files = {"file": (filepath, f)} - response = requests.post("url/api/v1/audio", files=files) - print(response.json()) + r = requests.post("url/api/v1/audio", files=files) + print(r.json()) """ extension = file.filename.split(".")[-1] filename = f"audio_{shortuuid.ShortUUID().random(length=32)}.{extension}" @@ -126,7 +131,9 @@ async def inference_with_audio( else: filepath = filename - utterances = await asr.process_input(filepath, num_speakers, source_lang, timestamps) + utterances = await asr.process_input( + filepath, num_speakers, source_lang, timestamps + ) utterances = [ { "text": str(utterance["text"]), @@ -146,7 +153,7 @@ async def inference_with_audio( f"{settings.api_prefix}/youtube", tags=["inference"], response_model=ASRResponse, - status_code=http_status.HTTP_200_OK + status_code=http_status.HTTP_200_OK, ) async def inference_with_youtube( background_tasks: BackgroundTasks, @@ -154,30 +161,35 @@ async def inference_with_youtube( num_speakers: Optional[int] = 0, source_lang: Optional[str] = "en", timestamps: Optional[str] = "seconds", -): +) -> ASRResponse: """ - Inference endpoint. + Inference endpoint with youtube url. Args: background_tasks (BackgroundTasks): Background tasks dependency. - url (str): Youtube URL. - num_speakers (int): Number of speakers in the audio file. Default: 0. + url (str): Youtube url. + num_speakers (int): Number of speakers to detect. Defaults to 0, which attempts to detect the number of speaker. + source_lang (str): The language of the source file. Defaults to "en". + timestamps (str): The format of the transcript timestamps. Options are "seconds", "milliseconds", or "hms," + which stands for hours, minutes, seconds. Defaults to "seconds". Returns: - ASRResponse: Response data. + ASRResponse: Response data as an ASRResponse object. Examples: - import requests url = "https://youtu.be/dQw4w9WgXcQ" r = requests.post(f"http://localhost:5001/api/v1/youtube?url={url}") + print(r.json()) """ num_speakers = num_speakers or 0 filename = f"yt_{shortuuid.ShortUUID().random(length=32)}" filepath = await download_file_from_youtube(url, filename) - utterances = await asr.process_input(filepath, num_speakers, source_lang, timestamps) + utterances = await asr.process_input( + filepath, num_speakers, source_lang, timestamps + ) utterances = [ { "text": str(utterance["text"]), @@ -191,9 +203,3 @@ async def inference_with_youtube( background_tasks.add_task(delete_file, filepath=filename) return ASRResponse(utterances=utterances) - - -if __name__ == "__main__": - import uvicorn - - uvicorn.run("main:app", host="0.0.0.0", port=7680, reload=True) diff --git a/wordcab_transcribe/models.py b/wordcab_transcribe/models.py index afaadfd..0f05c3c 100644 --- a/wordcab_transcribe/models.py +++ b/wordcab_transcribe/models.py @@ -18,10 +18,12 @@ class ASRRequest(BaseModel): """Request model for the ASR API.""" + url: str = None num_speakers: int = None class ASRResponse(BaseModel): """Response model for the ASR API.""" + utterances: list diff --git a/wordcab_transcribe/service.py b/wordcab_transcribe/service.py index 1a4079d..1031039 100644 --- a/wordcab_transcribe/service.py +++ b/wordcab_transcribe/service.py @@ -13,79 +13,39 @@ # limitations under the License. """Service module to handle AI model interactions.""" -import io -import math import asyncio import functools -from pathlib import Path +from typing import List import numpy as np -from loguru import logger -from typing import List, Optional - -from wordcab_transcribe.config import settings -from wordcab_transcribe.utils import format_segments - import torch - -from sklearn.metrics import silhouette_score -from sklearn.cluster import AgglomerativeClustering - +from faster_whisper import WhisperModel +from loguru import logger from pyannote.audio import Audio -from pyannote.core import Segment from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding +from pyannote.core import Segment +from sklearn.cluster import AgglomerativeClustering +from sklearn.metrics import silhouette_score -from faster_whisper import WhisperModel - +from wordcab_transcribe.config import settings +from wordcab_transcribe.utils import convert_seconds_to_hms, format_segments class ASRService: - def __init__( - self, - model_size: str = "large-v2", - whisper_dir: str = "models/whisper_model", - compute_type: str = "int8_float16", - embeddings_model: str = "speechbrain/spkrec-ecapa-voxceleb", - ) -> None: - """ - ASR Service class to handle AI model interactions. + """ASR Service class to handle AI model interactions.""" - Args: - model_size (str, optional): Model size to use. Defaults to "large-v2". - whisper_dir (str, optional): If mounting the Whisper model, this directory will be used to load the model. - embeddings_model (str, optional): Speaker embeddings model to use. - Defaults to "speechbrain/spkrec-ecapa-voxceleb". - """ + def __init__(self) -> None: + """Initialize the ASRService class.""" self.device = "cuda" if torch.cuda.is_available() else "cpu" - self.model_size = model_size - self.whisper_dir = whisper_dir - self.compute_type = compute_type - self.embeddings_model = embeddings_model + self.whisper_model = settings.whisper_model + self.compute_type = settings.compute_type + self.embeddings_model = settings.embeddings_model - if Path(self.whisper_dir).exists(): - try: - self.model = WhisperModel( - self.whisper_dir, - device=self.device, - compute_type=self.compute_type, - model_dir=whisper_dir - ) - except: - logger.error("Failed to load Whisper model from directory. Downloading model...") - self.model = WhisperModel( - self.model_size, - device=self.device, - compute_type=self.compute_type - ) - else: - self.model = WhisperModel( - self.model_size, - device=self.device, - compute_type=self.compute_type - ) - self.embedding_model = PretrainedSpeakerEmbedding( - self.embeddings_model, - device=self.device + self.model = WhisperModel( + self.whisper_model, device=self.device, compute_type=self.compute_type + ) + self.embedding_model = PretrainedSpeakerEmbedding( + self.embeddings_model, device=self.device ) # Multi requests support @@ -94,9 +54,12 @@ def __init__( self.needs_processing = None self.needs_processing_timer = None - self.max_batch_size = settings.batch_size # Max number of requests to process at once - self.max_wait = settings.max_wait # Max time to wait for more requests before processing - + self.max_batch_size = ( + settings.batch_size + ) # Max number of requests to process at once + self.max_wait = ( + settings.max_wait + ) # Max time to wait for more requests before processing def schedule_processing_if_needed(self) -> None: """Method to schedule processing if needed.""" @@ -107,19 +70,22 @@ def schedule_processing_if_needed(self) -> None: self.queue[0]["time"] + self.max_wait, self.needs_processing.set ) - - async def process_input(self, filepath: str, num_speakers: int, source_lang: str, timestamps: str) -> List[dict]: + async def process_input( + self, filepath: str, num_speakers: int, source_lang: str, timestamps: str + ) -> List[dict]: """ Process the input request and return the result. Args: filepath (str): Path to the audio file. num_speakers (int): Number of speakers to detect. + source_lang (str): Source language of the audio file. + timestamps (str): Timestamps unit to use. Returns: List[dict]: List of speaker segments. """ - our_task = { + one_task = { "done_event": asyncio.Event(), "input": filepath, "num_speakers": num_speakers, @@ -128,13 +94,12 @@ async def process_input(self, filepath: str, num_speakers: int, source_lang: str "time": asyncio.get_event_loop().time(), } async with self.queue_lock: - self.queue.append(our_task) + self.queue.append(one_task) self.schedule_processing_if_needed() - await our_task["done_event"].wait() - - return our_task["result"] + await one_task["done_event"].wait() + return one_task["result"] async def runner(self) -> None: """Process the input requests in the queue.""" @@ -150,25 +115,31 @@ async def runner(self) -> None: async with self.queue_lock: if self.queue: - longest_wait = asyncio.get_event_loop().time() - self.queue[0]["time"] + longest_wait = ( + asyncio.get_event_loop().time() - self.queue[0]["time"] + ) + logger.debug(f"Longest wait: {longest_wait}") else: longest_wait = None - file_batch = self.queue[:self.max_batch_size] - del self.queue[:len(file_batch)] + file_batch = self.queue[: self.max_batch_size] + del self.queue[: len(file_batch)] self.schedule_processing_if_needed() try: - batch = [ - (task["input"], task["num_speakers"], task["source_lang"], task["timestamps"]) - for task in file_batch - ] results = [] - for input_file, num_speakers, source_lang, timestamps in batch: + for task in file_batch: res = await asyncio.get_event_loop().run_in_executor( - None, functools.partial(self.inference, input_file, num_speakers, source_lang, timestamps) + None, + functools.partial( + self.inference, + task["input"], + task["num_speakers"], + task["source_lang"], + task["timestamps"], + ), ) results.append(res) - for task, result in zip(file_batch, results): + for task, result in zip(file_batch, results, strict=True): task["result"] = result task["done_event"].set() del file_batch @@ -180,44 +151,40 @@ async def runner(self) -> None: task["result"] = e task["done_event"].set() - - def convert_seconds_to_hms(self, seconds): - hours, remainder = divmod(seconds, 3600) - minutes, seconds = divmod(remainder, 60) - milliseconds = math.floor((seconds % 1) * 1000) - output = f"{int(hours):02}:{int(minutes):02}:{int(seconds):02},{milliseconds:03}" - return output - - def inference( - self, - filepath: str, - num_speakers: int, - source_lang: str, - timestamps: str, + self, + filepath: str, + num_speakers: int, + source_lang: str, + timestamps: str, ) -> List[dict]: """ Inference method to process the audio file. Args: filepath (str): Path to the audio file. - + num_speakers (int): Number of speakers to detect. + source_lang (str): Source language of the audio file. + timestamps (str): Timestamps unit to use. Returns: List[dict]: List of diarized segments. """ - - segments, _ = self.model.transcribe(filepath, language=source_lang, beam_size=5, word_timestamps=True) + segments, _ = self.model.transcribe( + filepath, language=source_lang, beam_size=5, word_timestamps=True + ) segments = format_segments(list(segments)) duration = segments[-1]["end"] - diarized_segments = self.diarize(filepath, segments, duration, num_speakers, timestamps) + diarized_segments = self.diarize( + filepath, segments, duration, num_speakers, timestamps + ) return diarized_segments - def diarize( - self, audio_obj: str, + self, + filepath: str, segments: List[dict], duration: float, num_speakers: int, @@ -227,7 +194,7 @@ def diarize( Diarize the segments using pyannote. Args: - audio_obj (str): Path to the audio file. + filepath (str): Path to the audio file. segments (List[dict]): List of segments to diarize. duration (float): Duration of the audio file. num_speakers (int): Number of speakers; defaults to 0. @@ -239,25 +206,28 @@ def diarize( embeddings = np.zeros(shape=(len(segments), 192)) for i, segment in enumerate(segments): - embeddings[i] = self.segment_embedding(audio_obj, segment, duration) + embeddings[i] = self.segment_embedding(filepath, segment, duration) embeddings = np.nan_to_num(embeddings) num_speakers = num_speakers or 0 best_num_speakers = self._get_num_speakers(embeddings, num_speakers) - identified_segments = self._assign_speaker_label(segments, embeddings, best_num_speakers) + identified_segments = self._assign_speaker_label( + segments, embeddings, best_num_speakers + ) joined_segments = self.join_utterances(identified_segments, timestamps) return joined_segments - - def segment_embedding(self, audio_obj: io.BytesIO, segment: dict, duration: float) -> np.ndarray: + def segment_embedding( + self, filepath: str, segment: dict, duration: float + ) -> np.ndarray: """ Get the embedding of a segment. Args: - audio_obj (io.BytesIO): Audio file object. + filepath (str): Path to the audio file. segment (dict): Segment to get the embedding. duration (float): Duration of the audio file. @@ -270,10 +240,9 @@ def segment_embedding(self, audio_obj: io.BytesIO, segment: dict, duration: floa clip = Segment(start=start, end=end) audio = Audio() - waveform, _ = audio.crop(audio_obj, clip) - - return self.embedding_model(waveform[None]) + waveform, _ = audio.crop(filepath, clip) + return self.embedding_model(waveform[None]) def join_utterances(self, segments: List[dict], timestamps: str) -> List[dict]: """ @@ -281,6 +250,7 @@ def join_utterances(self, segments: List[dict], timestamps: str) -> List[dict]: Args: segments (List[dict]): List of segments. + timestamps (str): Format of timestamps to use. Returns: List[dict]: List of joined segments with speaker labels. @@ -296,12 +266,12 @@ def join_utterances(self, segments: List[dict], timestamps: str) -> List[dict]: current_utterance["text"] = text.strip() utterance_list.append(current_utterance) text = "" - + current_utterance = { "start": segment["start"], - "speaker": segment["speaker"] + "speaker": segment["speaker"], } - + text += segment["text"] + " " if current_utterance: @@ -311,8 +281,8 @@ def join_utterances(self, segments: List[dict], timestamps: str) -> List[dict]: for utterance in utterance_list: if timestamps == "hms": - utterance["start"] = self.convert_seconds_to_hms(utterance["start"]) - utterance["end"] = self.convert_seconds_to_hms(utterance["end"]) + utterance["start"] = convert_seconds_to_hms(utterance["start"]) + utterance["end"] = convert_seconds_to_hms(utterance["end"]) elif timestamps == "seconds": utterance["start"] = float(utterance["start"]) utterance["end"] = float(utterance["end"]) @@ -322,7 +292,6 @@ def join_utterances(self, segments: List[dict], timestamps: str) -> List[dict]: return utterance_list - def _get_num_speakers(self, embeddings: np.ndarray, num_speakers: int) -> int: """ Get the number of speakers in the audio file. @@ -339,16 +308,19 @@ def _get_num_speakers(self, embeddings: np.ndarray, num_speakers: int) -> int: for i in range(2, 11): clustering = AgglomerativeClustering(i).fit(embeddings) - score = silhouette_score(embeddings, clustering.labels_, metric="euclidean") + score = silhouette_score( + embeddings, clustering.labels_, metric="euclidean" + ) score_num_speakers[i] = score - best_num_speakers = max(score_num_speakers, key=lambda x: score_num_speakers[x]) + best_num_speakers = max( + score_num_speakers, key=lambda x: score_num_speakers[x] + ) else: best_num_speakers = num_speakers return best_num_speakers - def _assign_speaker_label( self, segments: List[dict], embeddings: np.ndarray, best_num_speakers: int ) -> List[int]: @@ -371,5 +343,5 @@ def _assign_speaker_label( labels = clustering.labels_ for i in range(len(segments)): segments[i]["speaker"] = labels[i] + 1 - + return segments diff --git a/wordcab_transcribe/utils.py b/wordcab_transcribe/utils.py index 2974422..7c13150 100644 --- a/wordcab_transcribe/utils.py +++ b/wordcab_transcribe/utils.py @@ -14,9 +14,10 @@ """Utils module of the Wordcab Transcribe.""" import asyncio -import subprocess +import math +import subprocess # noqa: S404 from pathlib import Path -from typing import List +from typing import List, Optional from yt_dlp import YoutubeDL @@ -31,12 +32,33 @@ async def run_subprocess(command: List[str]) -> tuple: Returns: tuple: Tuple with the return code, stdout and stderr. """ - process = await asyncio.create_subprocess_exec(*command, stdout=subprocess.PIPE, stderr=subprocess.PIPE) + process = await asyncio.create_subprocess_exec( + *command, stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) stdout, stderr = await process.communicate() return process.returncode, stdout, stderr +def convert_seconds_to_hms(seconds: float) -> str: + """ + Convert seconds to hours, minutes and seconds. + + Args: + seconds (float): Seconds to convert. + + Returns: + str: Hours, minutes and seconds. + """ + hours, remainder = divmod(seconds, 3600) + minutes, seconds = divmod(remainder, 60) + milliseconds = math.floor((seconds % 1) * 1000) + + output = f"{int(hours):02}:{int(minutes):02}:{int(seconds):02},{milliseconds:03}" + + return output + + async def convert_file_to_wav(filepath: str) -> str: """ Convert a file to wav format using ffmpeg. @@ -44,6 +66,10 @@ async def convert_file_to_wav(filepath: str) -> str: Args: filepath (str): Path to the file to convert. + Raises: + FileNotFoundError: If the file does not exist. + Exception: If there is an error converting the file. + Returns: str: Path to the converted file. """ @@ -55,10 +81,21 @@ async def convert_file_to_wav(filepath: str) -> str: new_filepath = filepath.with_suffix(".wav") cmd = [ - "ffmpeg", "-i", str(filepath), "-vn", "-acodec", "pcm_s16le", "-ar", "16000", "-ac", "1", "-y", str(new_filepath) + "ffmpeg", + "-i", + str(filepath), + "-vn", + "-acodec", + "pcm_s16le", + "-ar", + "16000", + "-ac", + "1", + "-y", + str(new_filepath), ] result = await run_subprocess(cmd) - + if result[0] != 0: raise Exception(f"Error converting file {filepath} to wav format: {result[2]}") @@ -102,7 +139,11 @@ def delete_file(filepath: str) -> None: filepath.unlink() -def format_segments(segments: list, use_dict: bool = False, include_words: bool = False) -> list: +def format_segments( + segments: list, + use_dict: Optional[bool] = False, + include_words: Optional[bool] = False, +) -> list: """ Format the segments to a list of dicts with start, end and text keys. @@ -135,8 +176,8 @@ def format_segments(segments: list, use_dict: bool = False, include_words: bool "start": word.start, "end": word.end, "word": word.word.strip(), - "probability": word.probability - } + "probability": word.probability, + } for word in segment.words ] segment_dict["words"] = words