From f9f1837df729c35d17d967b4e974b940e2520653 Mon Sep 17 00:00:00 2001 From: chainyo Date: Fri, 13 Oct 2023 14:27:23 +0000 Subject: [PATCH 1/3] add notebook for comparison --- .../comparison_torch_faster_whisper.ipynb | 11699 ++++++++++++++++ 1 file changed, 11699 insertions(+) create mode 100644 notebooks/comparison_torch_faster_whisper.ipynb diff --git a/notebooks/comparison_torch_faster_whisper.ipynb b/notebooks/comparison_torch_faster_whisper.ipynb new file mode 100644 index 0000000..8574bf4 --- /dev/null +++ b/notebooks/comparison_torch_faster_whisper.ipynb @@ -0,0 +1,11699 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NkxZJIOVU5Ft", + "outputId": "7e79aa30-bd2d-4990-e87c-aff7dac608dc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "faster-whisper 0.7.1 requires tokenizers==0.13.*, but you have tokenizers 0.14.1 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "!pip install -q --upgrade torch torchvision torchaudio\n", + "!pip install -q git+https://github.com/huggingface/transformers\n", + "!pip install -q accelerate optimum bitsandbytes\n", + "!pip install -q ipython-autotime" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GoWs8boSf6CC", + "outputId": "d13cb996-56cf-4503-a14d-e200513cda3e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 286 µs (started: 2023-10-13 13:44:34 +00:00)\n" + ] + } + ], + "source": [ + "%load_ext autotime" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-1b4AMKScNLl", + "outputId": "4c2a938b-d1e1-4fc9-ba62-be1fa82bad47" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2023-10-13 13:06:41-- https://huggingface.co/datasets/reach-vb/random-audios/resolve/main/4469669.mp3\n", + "Resolving huggingface.co (huggingface.co)... 18.172.134.24, 18.172.134.4, 18.172.134.88, ...\n", + "Connecting to huggingface.co (huggingface.co)|18.172.134.24|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://cdn-lfs.huggingface.co/repos/96/e4/96e4f69cd112b019dd764318570e47e5fe96de53d8c32a99d745e72d9086e355/09251982f6a864867d829525e510cdb53b421291d6629485d1f8ddc23e347512?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%274469669.mp3%3B+filename%3D%224469669.mp3%22%3B&response-content-type=audio%2Fmpeg&Expires=1697461601&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY5NzQ2MTYwMX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy85Ni9lNC85NmU0ZjY5Y2QxMTJiMDE5ZGQ3NjQzMTg1NzBlNDdlNWZlOTZkZTUzZDhjMzJhOTlkNzQ1ZTcyZDkwODZlMzU1LzA5MjUxOTgyZjZhODY0ODY3ZDgyOTUyNWU1MTBjZGI1M2I0MjEyOTFkNjYyOTQ4NWQxZjhkZGMyM2UzNDc1MTI%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=iebEuvMD5QHR4jNJlSsyzGAdKJnY4o0YmLQiMHGcAFGM4Cb%7EH80gQBbNNmcNYYazrqS2zZZ1M1kc2%7EFlniV7lVgcR2eArG7TdA6kIkso%7EREJs%7EKQE44rxVaAtg8llsK3Cv7NAR-kF1dhpFX0shG7Dv9BC294x2AkvBSpB9BycdAuZeRSO7lPeGqXt%7ESoFfrIROeaq0g4m%7E4YNa6GGwE0Uy9uhi8ktWxgcEEjdXKCaav2zo1v9UT7EMy-In%7EyWkyC4erJOuH3lsUoTvO5bZ5rMSoMxhwtegebnikSJi7kQSx-yjICkCbkLsjPBK8Imce3b89fdCXEK%7E2rgLa-SIikZA__&Key-Pair-Id=KVTP0A1DKRTAX [following]\n", + "--2023-10-13 13:06:41-- https://cdn-lfs.huggingface.co/repos/96/e4/96e4f69cd112b019dd764318570e47e5fe96de53d8c32a99d745e72d9086e355/09251982f6a864867d829525e510cdb53b421291d6629485d1f8ddc23e347512?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%274469669.mp3%3B+filename%3D%224469669.mp3%22%3B&response-content-type=audio%2Fmpeg&Expires=1697461601&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY5NzQ2MTYwMX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy85Ni9lNC85NmU0ZjY5Y2QxMTJiMDE5ZGQ3NjQzMTg1NzBlNDdlNWZlOTZkZTUzZDhjMzJhOTlkNzQ1ZTcyZDkwODZlMzU1LzA5MjUxOTgyZjZhODY0ODY3ZDgyOTUyNWU1MTBjZGI1M2I0MjEyOTFkNjYyOTQ4NWQxZjhkZGMyM2UzNDc1MTI%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=iebEuvMD5QHR4jNJlSsyzGAdKJnY4o0YmLQiMHGcAFGM4Cb%7EH80gQBbNNmcNYYazrqS2zZZ1M1kc2%7EFlniV7lVgcR2eArG7TdA6kIkso%7EREJs%7EKQE44rxVaAtg8llsK3Cv7NAR-kF1dhpFX0shG7Dv9BC294x2AkvBSpB9BycdAuZeRSO7lPeGqXt%7ESoFfrIROeaq0g4m%7E4YNa6GGwE0Uy9uhi8ktWxgcEEjdXKCaav2zo1v9UT7EMy-In%7EyWkyC4erJOuH3lsUoTvO5bZ5rMSoMxhwtegebnikSJi7kQSx-yjICkCbkLsjPBK8Imce3b89fdCXEK%7E2rgLa-SIikZA__&Key-Pair-Id=KVTP0A1DKRTAX\n", + "Resolving cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)... 18.154.185.27, 18.154.185.94, 18.154.185.64, ...\n", + "Connecting to cdn-lfs.huggingface.co (cdn-lfs.huggingface.co)|18.154.185.27|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 29628186 (28M) [audio/mpeg]\n", + "Saving to: ‘4469669.mp3’\n", + "\n", + "4469669.mp3 100%[===================>] 28.25M 35.6MB/s in 0.8s \n", + "\n", + "2023-10-13 13:06:43 (35.6 MB/s) - ‘4469669.mp3’ saved [29628186/29628186]\n", + "\n", + "time: 2.51 s (started: 2023-10-13 13:06:40 +00:00)\n" + ] + } + ], + "source": [ + "!wget https://huggingface.co/datasets/reach-vb/random-audios/resolve/main/4469669.mp3" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xwO3In02mE7z" + }, + "source": [ + "## BetterTransformer" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nnzyGx2YlkfY", + "outputId": "b2d4f077-b002-4bd0-e837-a348c90f23f1" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n", + "The BetterTransformer implementation does not support padding during training, as the fused kernels do not support attention masks. Beware that passing padded batched data during training may result in unexpected outputs. Please refer to https://huggingface.co/docs/optimum/bettertransformer/overview for more details.\n" + ] + } + ], + "source": [ + "import torch\n", + "from transformers import pipeline\n", + "\n", + "pipe = pipeline(\n", + " \"automatic-speech-recognition\",\n", + " \"openai/whisper-large-v2\",\n", + " torch_dtype=torch.float16,\n", + " device=\"cuda:0\"\n", + ")\n", + "\n", + "pipe.model = pipe.model.to_bettertransformer()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 142 + }, + "id": "qtggr0dVQS3o", + "outputId": "b2083cf3-9342-4b67-f8bc-608539c67e00" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 2min 37s (started: 2023-10-13 13:21:05 +00:00)\n" + ] + } + ], + "source": [ + "torch_out = pipe(\n", + " \"4469669.mp3\",\n", + " chunk_length_s=30,\n", + " batch_size=16,\n", + " return_timestamps=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 6.92 ms (started: 2023-10-13 13:42:35 +00:00)\n" + ] + } + ], + "source": [ + "with open(\"torch_output.txt\", \"w\") as f:\n", + " for s in torch_out[\"chunks\"]:\n", + " start, end = s[\"timestamp\"]\n", + " f.write(f\"[{start} - {end}] {s['text']}\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# This is failing with CUDA OOM\n", + "\n", + "# torch_out_with_config = pipe(\n", + "# \"4469669.mp3\",\n", + "# chunk_length_s=30,\n", + "# batch_size=16,\n", + "# return_timestamps=True,\n", + "# generate_kwargs=dict(\n", + "# temperature=0.0,\n", + "# num_beams=3,\n", + "# repetition_penalty=1.0,\n", + "# do_sample=False,\n", + "# ),\n", + "# )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Faster-Whisper" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 13.4 s (started: 2023-10-13 13:26:48 +00:00)\n" + ] + } + ], + "source": [ + "from faster_whisper import WhisperModel\n", + "\n", + "model = WhisperModel(\n", + " \"large-v2\", device=\"cuda\", device_index=0, compute_type=\"float16\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Estimating duration from bitrate, this may be inaccurate\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 5min 5s (started: 2023-10-13 13:28:01 +00:00)\n" + ] + } + ], + "source": [ + "segments, _ = model.transcribe(\n", + " \"4469669.mp3\",\n", + " language=\"en\",\n", + " beam_size=1,\n", + " repetition_penalty=1.0,\n", + " compression_ratio_threshold=2.4,\n", + " log_prob_threshold=-1.0,\n", + " no_speech_threshold=0.6,\n", + " condition_on_previous_text=True,\n", + " suppress_blank=False,\n", + " word_timestamps=False,\n", + ")\n", + "faster_whisper_out = list(segments)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "time: 5.15 ms (started: 2023-10-13 13:39:53 +00:00)\n" + ] + } + ], + "source": [ + "with open(\"faster_whisper_output.txt\", \"w\") as f:\n", + " for s in faster_whisper_out:\n", + " f.write(f\"[{s.start:.2f}, {s.end:.2f}] {s.text}\\n\")\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparison using NLTK" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Jaccard Similarity: 0.9084995663486557\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package punkt to /home/chainyo/nltk_data...\n", + "[nltk_data] Package punkt is already up-to-date!\n" + ] + } + ], + "source": [ + "import re\n", + "import string\n", + "\n", + "import nltk\n", + "nltk.download(\"punkt\")\n", + "\n", + "from nltk.tokenize import word_tokenize\n", + "\n", + "\n", + "# Function to remove brackets at the beginning of a line\n", + "def remove_brackets(text):\n", + " # Use a regular expression to match and remove brackets at the beginning of the line\n", + " cleaned_text = re.sub(r'^\\[[^\\]]*\\]\\s*', '', text)\n", + "\n", + " return cleaned_text\n", + "\n", + "\n", + "def preprocess_and_tokenize(text):\n", + " # Convert to lowercase and remove leading/trailing whitespace\n", + " text = text.lower().strip()\n", + "\n", + " # Remove punctuation using string.punctuation\n", + " translator = str.maketrans('', '', string.punctuation)\n", + " text = text.translate(translator)\n", + "\n", + " # Tokenize and remove punctuation\n", + " tokens = word_tokenize(text)\n", + " tokens = [token for token in tokens if token.isalnum()] # Keep alphanumeric tokens\n", + "\n", + " return set(tokens) # Convert to a set for Jaccard similarity\n", + "\n", + "# Transcriptions\n", + "with open(\"torch_output.txt\", \"r\") as f:\n", + " content = f.readlines()\n", + " clean_content = [remove_brackets(line) for line in content]\n", + " torch_out = \" \".join(clean_content)\n", + "\n", + "with open(\"faster_whisper_output.txt\", \"r\") as f:\n", + " content = f.readlines()\n", + " clean_content = [remove_brackets(line) for line in content]\n", + " faster_whisper_out = \" \".join(clean_content)\n", + "\n", + "# Preprocess and tokenize\n", + "tokens1 = preprocess_and_tokenize(torch_out)\n", + "tokens2 = preprocess_and_tokenize(faster_whisper_out)\n", + "\n", + "# Calculate Jaccard similarity\n", + "intersection = len(tokens1.intersection(tokens2))\n", + "union = len(tokens1.union(tokens2))\n", + "jaccard_similarity = intersection / union\n", + "\n", + "print(f\"Jaccard Similarity: {jaccard_similarity}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "authorship_tag": "ABX9TyOhBU5WuZJ+wCciS7VORq32", + "gpuType": "T4", + "include_colab_link": true, + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "00d97f03d55d46498c350995295e6936": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": 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e069d39..365abf8 100644 --- a/.env +++ b/.env @@ -31,7 +31,7 @@ DEBUG=True # docker run command as a volume. # e.g. WHISPER_MODEL="/app/models/custom" # docker cmd: -v /path/to/custom/model:/app/models/custom -WHISPER_MODEL="large-v2" +WHISPER_MODEL="openai/whisper-large-v2" # The compute_type parameter is used to control the precision of the model. You can choose between: # "int8", "int8_float16", "int8_bfloat16", "int16", "float_16", "bfloat16", "float32". # The default value is "float16". diff --git a/pyproject.toml b/pyproject.toml index ba0adfc..eb8d464 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,8 +27,10 @@ classifiers = [ "Programming Language :: Python :: Implementation :: PyPy", ] dependencies = [ + "accelerate", "aiohttp>=3.8.4", "aiofiles>=23.1.0", + "bitsandbytes", "ctranslate2>=3.18.0", "faster-whisper @ git+https://github.com/Wordcab/faster-whisper@master", "ffmpeg-python>=0.2.0", @@ -36,11 +38,13 @@ dependencies = [ "loguru>=0.6.0", "numpy==1.23.1", "onnxruntime>=1.15.0", + "optimum", "pydantic>=1.10.9", "python-dotenv>=1.0.0", "tensorshare>=0.1.1", "torch>=2.0.0", "torchaudio>=2.0.1", + "transformers @ git+https://github.com/huggingface/transformers", "wget>=3.2.0", "yt-dlp>=2023.3.4", ] diff --git a/src/wordcab_transcribe/models.py b/src/wordcab_transcribe/models.py index 8203a04..07bc728 100644 --- a/src/wordcab_transcribe/models.py +++ b/src/wordcab_transcribe/models.py @@ -22,7 +22,6 @@ from enum import Enum from typing import List, Literal, NamedTuple, Optional, Union -from faster_whisper.transcribe import Segment from pydantic import BaseModel, HttpUrl, field_validator from tensorshare import TensorShare @@ -529,10 +528,36 @@ class MultiChannelTranscriptionOutput(BaseModel): segments: List[MultiChannelSegment] +class Chunk(BaseModel): + """Whisper transcription chunk.""" + + start: float + end: float + text: str + words: None + + class TranscriptionOutput(BaseModel): """Transcription output model for the API.""" - segments: List[Segment] + segments: List[Chunk] + + @classmethod + def get_segments_from_outputs(cls, outputs: List[dict]) -> "TranscriptionOutput": + """Create a TranscriptionOutput object from a list of outputs.""" + chunks = [] + for out in outputs: + start, end = out["timestamp"] + chunks.append( + Chunk( + start=start, + end=end, + text=out["text"], + words=None, + ) + ) + + return cls(segments=chunks) class TranscribeRequest(BaseModel): diff --git a/src/wordcab_transcribe/services/transcribe_service.py b/src/wordcab_transcribe/services/transcribe_service.py index 4a0fa9b..b642c7a 100644 --- a/src/wordcab_transcribe/services/transcribe_service.py +++ b/src/wordcab_transcribe/services/transcribe_service.py @@ -22,9 +22,9 @@ from typing import Iterable, List, NamedTuple, Optional, Union import torch -from faster_whisper import WhisperModel from loguru import logger from tensorshare import Backend, TensorShare +from transformers import pipeline from wordcab_transcribe.models import ( MultiChannelSegment, @@ -34,13 +34,6 @@ ) -class FasterWhisperModel(NamedTuple): - """Faster Whisper Model.""" - - model: WhisperModel - lang: str - - class TranscribeService: """Transcribe Service for audio files.""" @@ -75,12 +68,16 @@ def __init__( self.compute_type = compute_type self.model_path = model_path - self.model = WhisperModel( + self.pipe = pipeline( + "automatic-speech-recognition", self.model_path, - device=self.device, - device_index=device_index, - compute_type=self.compute_type, + torch_dtype=torch.float16, + device=f"{self.device}:{device_index[0]}", ) + self.pipe.model = self.pipe.model.to_bettertransformer() + + self.last_lang = "en" + self.pipe.tokenizer.set_prefix_tokens(language=self.last_lang) self.extra_lang = extra_languages self.extra_lang_models = extra_languages_model_paths @@ -173,16 +170,16 @@ def __call__( # lang=source_lang, # ) - if ( - vocab is not None - and isinstance(vocab, list) - and len(vocab) > 0 - and vocab[0].strip() - ): - words = ", ".join(vocab) - prompt = f"Vocab: {words.strip()}" - else: - prompt = None + # if ( + # vocab is not None + # and isinstance(vocab, list) + # and len(vocab) > 0 + # and vocab[0].strip() + # ): + # words = ", ".join(vocab) + # prompt = f"Vocab: {words.strip()}" + # else: + # prompt = None if not isinstance(audio, list): if isinstance(audio, torch.Tensor): @@ -191,201 +188,171 @@ def __call__( ts = audio.to_tensors(backend=Backend.NUMPY) audio = ts["audio"] - segments, _ = self.model.transcribe( + if source_lang != self.last_lang: + self.pipe.tokenizer.set_prefix_tokens(language=source_lang) + + pipe_outputs = self.pipe( audio, - language=source_lang, - initial_prompt=prompt, - repetition_penalty=repetition_penalty, - compression_ratio_threshold=compression_ratio_threshold, - log_prob_threshold=log_prob_threshold, - no_speech_threshold=no_speech_threshold, - condition_on_previous_text=condition_on_previous_text, - suppress_blank=suppress_blank, - word_timestamps=word_timestamps, - vad_filter=internal_vad, - vad_parameters={ - "threshold": 0.5, - "min_speech_duration_ms": 250, - "min_silence_duration_ms": 100, - "speech_pad_ms": 30, - "window_size_samples": 512, - }, + chunk_length_s=30, + batch_size=16, + return_timestamps=True, ) - segments = list(segments) - if not segments: - logger.warning( - "Empty transcription result. Trying with vad_filter=True." - ) - segments, _ = self.model.transcribe( - audio, - language=source_lang, - initial_prompt=prompt, - repetition_penalty=repetition_penalty, - compression_ratio_threshold=compression_ratio_threshold, - log_prob_threshold=log_prob_threshold, - no_speech_threshold=no_speech_threshold, - condition_on_previous_text=condition_on_previous_text, - suppress_blank=False, - word_timestamps=True, - vad_filter=False if internal_vad else True, - ) - - _outputs = [segment._asdict() for segment in segments] - outputs = TranscriptionOutput(segments=_outputs) + outputs = TranscriptionOutput.get_segments_from_outputs(pipe_outputs["chunks"]) else: - outputs = [] - for audio_index, audio_file in enumerate(audio): - outputs.append( - self.multi_channel( - audio_file, - source_lang=source_lang, - speaker_id=audio_index, - suppress_blank=suppress_blank, - word_timestamps=word_timestamps, - internal_vad=internal_vad, - repetition_penalty=repetition_penalty, - compression_ratio_threshold=compression_ratio_threshold, - log_prob_threshold=log_prob_threshold, - no_speech_threshold=no_speech_threshold, - prompt=prompt, - ) - ) + pass + # outputs = [] + # for audio_index, audio_file in enumerate(audio): + # outputs.append( + # self.multi_channel( + # audio_file, + # source_lang=source_lang, + # speaker_id=audio_index, + # suppress_blank=suppress_blank, + # word_timestamps=word_timestamps, + # internal_vad=internal_vad, + # repetition_penalty=repetition_penalty, + # compression_ratio_threshold=compression_ratio_threshold, + # log_prob_threshold=log_prob_threshold, + # no_speech_threshold=no_speech_threshold, + # prompt=prompt, + # ) + # ) return outputs - async def async_live_transcribe( - self, - audio: torch.Tensor, - source_lang: str, - model_index: int, - ) -> Iterable[dict]: - """Async generator for live transcriptions. - - This method wraps the live_transcribe method to make it async. - - Args: - audio (torch.Tensor): Audio tensor. - source_lang (str): Language of the audio file. - model_index (int): Index of the model to use. - - Yields: - Iterable[dict]: Iterable of transcribed segments. - """ - for result in self.live_transcribe(audio, source_lang, model_index): - yield result - - def live_transcribe( - self, - audio: torch.Tensor, - source_lang: str, - model_index: int, - ) -> Iterable[dict]: - """ - Transcribe audio from a WebSocket connection. - - Args: - audio (torch.Tensor): Audio tensor. - source_lang (str): Language of the audio file. - model_index (int): Index of the model to use. - - Yields: - Iterable[dict]: Iterable of transcribed segments. - """ - segments, _ = self.model.transcribe( - audio.numpy(), - language=source_lang, - suppress_blank=True, - word_timestamps=False, - ) - - for segment in segments: - yield segment._asdict() - - def multi_channel( - self, - audio: Union[str, torch.Tensor, TensorShare], - source_lang: str, - speaker_id: int, - suppress_blank: bool = False, - word_timestamps: bool = True, - internal_vad: bool = True, - repetition_penalty: float = 1.0, - compression_ratio_threshold: float = 2.4, - log_prob_threshold: float = -1.0, - no_speech_threshold: float = 0.6, - condition_on_previous_text: bool = False, - prompt: Optional[str] = None, - ) -> MultiChannelTranscriptionOutput: - """ - Transcribe an audio file using the faster-whisper original pipeline. - - Args: - audio (Union[str, torch.Tensor, TensorShare]): Audio file path or loaded audio. - source_lang (str): Language of the audio file. - speaker_id (int): Speaker ID used in the diarization. - suppress_blank (bool): - Whether to suppress blank at the beginning of the sampling. - word_timestamps (bool): - Whether to return word timestamps. - internal_vad (bool): - Whether to use faster-whisper's VAD or not. - repetition_penalty (float): - Repetition penalty to use during generation beamed search. - compression_ratio_threshold (float): - If the gzip compression ratio is above this value, treat as failed. - log_prob_threshold (float): - If the average log probability over sampled tokens is below this value, treat as failed. - no_speech_threshold (float): - If the no_speech probability is higher than this value AND the average log probability - over sampled tokens is below `log_prob_threshold`, consider the segment as silent. - condition_on_previous_text (bool): - If True, the previous output of the model is provided as a prompt for the next window; - disabling may make the text inconsistent across windows, but the model becomes less prone - to getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. - prompt (Optional[str]): Initial prompt to use for the generation. - - Returns: - MultiChannelTranscriptionOutput: Multi-channel transcription segments in a list. - """ - if isinstance(audio, torch.Tensor): - _audio = audio.numpy() - elif isinstance(audio, TensorShare): - ts = audio.to_tensors(backend=Backend.NUMPY) - _audio = ts["audio"] - - final_segments = [] - - segments, _ = self.model.transcribe( - _audio, - language=source_lang, - initial_prompt=prompt, - repetition_penalty=repetition_penalty, - compression_ratio_threshold=compression_ratio_threshold, - log_prob_threshold=log_prob_threshold, - no_speech_threshold=no_speech_threshold, - condition_on_previous_text=condition_on_previous_text, - suppress_blank=suppress_blank, - word_timestamps=word_timestamps, - vad_filter=internal_vad, - vad_parameters={ - "threshold": 0.5, - "min_speech_duration_ms": 250, - "min_silence_duration_ms": 100, - "speech_pad_ms": 30, - "window_size_samples": 512, - }, - ) - - for segment in segments: - _segment = MultiChannelSegment( - start=segment.start, - end=segment.end, - text=segment.text, - words=[Word(**word._asdict()) for word in segment.words], - speaker=speaker_id, - ) - final_segments.append(_segment) - - return MultiChannelTranscriptionOutput(segments=final_segments) + # async def async_live_transcribe( + # self, + # audio: torch.Tensor, + # source_lang: str, + # model_index: int, + # ) -> Iterable[dict]: + # """Async generator for live transcriptions. + + # This method wraps the live_transcribe method to make it async. + + # Args: + # audio (torch.Tensor): Audio tensor. + # source_lang (str): Language of the audio file. + # model_index (int): Index of the model to use. + + # Yields: + # Iterable[dict]: Iterable of transcribed segments. + # """ + # for result in self.live_transcribe(audio, source_lang, model_index): + # yield result + + # def live_transcribe( + # self, + # audio: torch.Tensor, + # source_lang: str, + # model_index: int, + # ) -> Iterable[dict]: + # """ + # Transcribe audio from a WebSocket connection. + + # Args: + # audio (torch.Tensor): Audio tensor. + # source_lang (str): Language of the audio file. + # model_index (int): Index of the model to use. + + # Yields: + # Iterable[dict]: Iterable of transcribed segments. + # """ + # segments, _ = self.model.transcribe( + # audio.numpy(), + # language=source_lang, + # suppress_blank=True, + # word_timestamps=False, + # ) + + # for segment in segments: + # yield segment._asdict() + + # def multi_channel( + # self, + # audio: Union[str, torch.Tensor, TensorShare], + # source_lang: str, + # speaker_id: int, + # suppress_blank: bool = False, + # word_timestamps: bool = True, + # internal_vad: bool = True, + # repetition_penalty: float = 1.0, + # compression_ratio_threshold: float = 2.4, + # log_prob_threshold: float = -1.0, + # no_speech_threshold: float = 0.6, + # condition_on_previous_text: bool = False, + # prompt: Optional[str] = None, + # ) -> MultiChannelTranscriptionOutput: + # """ + # Transcribe an audio file using the faster-whisper original pipeline. + + # Args: + # audio (Union[str, torch.Tensor, TensorShare]): Audio file path or loaded audio. + # source_lang (str): Language of the audio file. + # speaker_id (int): Speaker ID used in the diarization. + # suppress_blank (bool): + # Whether to suppress blank at the beginning of the sampling. + # word_timestamps (bool): + # Whether to return word timestamps. + # internal_vad (bool): + # Whether to use faster-whisper's VAD or not. + # repetition_penalty (float): + # Repetition penalty to use during generation beamed search. + # compression_ratio_threshold (float): + # If the gzip compression ratio is above this value, treat as failed. + # log_prob_threshold (float): + # If the average log probability over sampled tokens is below this value, treat as failed. + # no_speech_threshold (float): + # If the no_speech probability is higher than this value AND the average log probability + # over sampled tokens is below `log_prob_threshold`, consider the segment as silent. + # condition_on_previous_text (bool): + # If True, the previous output of the model is provided as a prompt for the next window; + # disabling may make the text inconsistent across windows, but the model becomes less prone + # to getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. + # prompt (Optional[str]): Initial prompt to use for the generation. + + # Returns: + # MultiChannelTranscriptionOutput: Multi-channel transcription segments in a list. + # """ + # if isinstance(audio, torch.Tensor): + # _audio = audio.numpy() + # elif isinstance(audio, TensorShare): + # ts = audio.to_tensors(backend=Backend.NUMPY) + # _audio = ts["audio"] + + # final_segments = [] + + # segments, _ = self.model.transcribe( + # _audio, + # language=source_lang, + # initial_prompt=prompt, + # repetition_penalty=repetition_penalty, + # compression_ratio_threshold=compression_ratio_threshold, + # log_prob_threshold=log_prob_threshold, + # no_speech_threshold=no_speech_threshold, + # condition_on_previous_text=condition_on_previous_text, + # suppress_blank=suppress_blank, + # word_timestamps=word_timestamps, + # vad_filter=internal_vad, + # vad_parameters={ + # "threshold": 0.5, + # "min_speech_duration_ms": 250, + # "min_silence_duration_ms": 100, + # "speech_pad_ms": 30, + # "window_size_samples": 512, + # }, + # ) + + # for segment in segments: + # _segment = MultiChannelSegment( + # start=segment.start, + # end=segment.end, + # text=segment.text, + # words=[Word(**word._asdict()) for word in segment.words], + # speaker=speaker_id, + # ) + # final_segments.append(_segment) + + # return MultiChannelTranscriptionOutput(segments=final_segments) From 01cd08580337dd44a6881d36060f4e68acc0c344 Mon Sep 17 00:00:00 2001 From: chainyo Date: Fri, 13 Oct 2023 16:11:22 +0000 Subject: [PATCH 3/3] fix Chunk model --- src/wordcab_transcribe/models.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/wordcab_transcribe/models.py b/src/wordcab_transcribe/models.py index 07bc728..2cfb936 100644 --- a/src/wordcab_transcribe/models.py +++ b/src/wordcab_transcribe/models.py @@ -534,7 +534,7 @@ class Chunk(BaseModel): start: float end: float text: str - words: None + words: Union[None, list] class TranscriptionOutput(BaseModel): @@ -553,7 +553,7 @@ def get_segments_from_outputs(cls, outputs: List[dict]) -> "TranscriptionOutput" start=start, end=end, text=out["text"], - words=None, + words=[], ) )