diff --git a/.dockerignore b/.dockerignore index bddad54..abf7413 100644 --- a/.dockerignore +++ b/.dockerignore @@ -14,5 +14,4 @@ __pycache__ s3-creds.env .vscode .env -.env.override -/data \ No newline at end of file +.env.override \ No newline at end of file diff --git a/.env b/.env index 356cd46..5448bbe 100644 --- a/.env +++ b/.env @@ -1,12 +1,10 @@ # passed to --input-uri (see main.py) -INPUT_URI=http://model-hosting.beng.nl/kaldi-nl-test.mp3 -# uncomment if you have downloaded s3://x-omg-daan-av/dane-asr-worker-sample-data.tar.gz -# INPUT_URI=http://fake-hosting.beng.nl/2101608150135908031__NOS_JOURNAAL_-WON01207359.mp4 +INPUT_URI=http://model-hosting.beng.nl/whisper-test.mp3 # passed to --output-uri (see main.py) OUTPUT_URI=s3://x-omg-daan-av/assets/2101608150135908031__NOS_JOURNAAL_-WON01207359/ -# make sure to mount this dir into the container (see docker-compose-dane-worker.yml) +# make sure to mount these dirs into the container (see docker-compose.yml) DATA_BASE_DIR=./data MODEL_BASE_DIR=./model diff --git a/.gitignore b/.gitignore index 47963f0..6644b44 100644 --- a/.gitignore +++ b/.gitignore @@ -2,11 +2,11 @@ !/data/README.md !/data/input/ /data/input/* -!/data/input/testsource__testcarrier.wav +!/data/input/whisper-test.mp3 !/data/output /data/output/* -!/data/output/testsource__testcarrier -!/data/output/testsource__testcarrier/* +!/data/output/whisper-test +!/data/output/whisper-test/* /model/* __pycache__ .pytest_cache diff --git a/Dockerfile b/Dockerfile index 4d21fdd..5de08d8 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,16 +1,16 @@ FROM nvidia/cuda:12.2.2-cudnn8-runtime-ubuntu22.04 -# Create dirs for: -# - Injecting config.yml: /root/.DANE -# - Mount point for input & output files: /mnt/dane-fs -# - Storing the source code: /src -# - Storing the model: /model -RUN mkdir /root/.DANE /mnt/dane-fs /src /data /model - +# Install ffmpeg RUN apt-get update && \ apt-get install -y python3-pip python3.11-dev python-is-python3 ffmpeg && \ rm -rf /var/lib/apt/lists/* +# Create dirs for: +# - Storing the source code: /src +# - Storing the input & output files: /data +# - Storing the model: /model +RUN mkdir /src /data /model + WORKDIR /src @@ -29,6 +29,6 @@ RUN poetry install --without dev --no-root && rm -rf $POETRY_CACHE_DIR COPY ./ /src # Write provenance info about software versions to file -RUN echo "dane-whisper-asr-worker;https://github.com/beeldengeluid/dane-whisper-asr-worker/commit/$(git rev-parse HEAD)" >> /software_provenance.txt +RUN echo "whisper-asr-worker;https://github.com/beeldengeluid/whisper-asr-worker/commit/$(git rev-parse HEAD)" >> /software_provenance.txt ENTRYPOINT ["./docker-entrypoint.sh"] \ No newline at end of file diff --git a/README.md b/README.md index 001d0b9..05a5a4d 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,72 @@ -# dane-whisper-asr-worker +# whisper-asr-worker + +ASR Worker that uses faster-whisper as the backend, to be used for transcribing AV material from B&G. + +This is still a WIP, so it is subject to change. + +There are 2 ways in which the whisper-asr-worker can be tested **(ON THE CPU)**: + +## 1. Docker CPU run (recommended) + +1. Check if Docker is installed +2. Make sure you have the `.env.override` file in your local repo folder +3. In `.env.override`, change `W_DEVICE` from `cuda` to `cpu` +4. Comment out the lines indicated in `docker-compose.yml` +5. Open your preferred terminal and navigate to the local repository folder +6. To build the image, execute the following command: +``` +docker build . -t whisper-asr-worker +``` +7. To run the worker, execute the following command: +``` +docker compose up +``` + +## 2. Local CPU run + +All commands should be run within WSL if on Windows or within your terminal if on Linux. + +1. Follow the steps [here](https://github.com/beeldengeluid/dane-example-worker/wiki/Setting-up-a-new-worker) (under "Adding `pyproject.toml` and generating a `poetry.lock` based on it") to install Poetry and the dependencies required to run the worker +2. Make sure you have the `.env.override` file in your local repo folder +3. In `.env.override`, change `W_DEVICE` from `cuda` to `cpu` +4. Install `ffmpeg`. You can run this command, for example: +``` +apt-get -y update && apt-get -y upgrade && apt-get install -y --no-install-recommends ffmpeg +``` +5. Navigate to `scripts`, then execute the following command: +``` +./run.sh +``` + +## Running the worker using a CUDA-compatible GPU + +To run the worker with a CUDA-compatible GPU instead of the CPU, either: +- skip steps 3 & 4 from "Docker CPU run" +- skip step 3 from "Local run" + +**(OUTDATED BUT STILL MIGHT BE RELEVANT)** To run it using a GPU via Docker, check [the instructions from the dane-example-worker](https://github.com/beeldengeluid/dane-example-worker/wiki/Containerization#running-the-container-locally-using-cuda-compatible-gpu). + +Make sure to replace `dane-example-worker` in the `docker run` command with `dane-whisper-asr-worker`. + +## Expected run + +The expected run of this worker (whose pipeline is defined in `asr.py`) should + +1. download the input file if it isn't downloaded already in `/data/input/` via `download.py` + +2. download the model if not present via `model_download.py` + +3. run `transcode.py` if the input file is a video to convert it to audio format (though there are plans to remove this and instead use the [audio-extraction-worker](https://github.com/beeldengeluid/audio-extraction-worker/) to extract the audio) + +4. run `whisper.py` to transcribe the audio and save it in `/data/output/` if a transcription doesn't already exist +5. convert Whisper's output to DAAN index format using `daan_transcript.py` +6. (optional) transfer the output to an S3 bucket. ## Model options -If you prefer to use your own model that is stored locally, make sure to set `BASE_MOUNT_MODEL` to the path where the model files can be found. A model found locally will take precedence over downloading it from Huggingface or S3 (so, no matter how `WHISPER_ASR_SETTINGS.MODEL` is set, it will ignore it if a model is present locally). +If you prefer to use your own model that is stored locally, make sure to set `MODEL_BASE_DIR` to the path where the model files can be found. A model found locally will take precedence over downloading it from Huggingface or S3 (so, no matter how `W_MODEL` is set, it will ignore it if a model is present locally). -The pre-trained Whisper model version can be adjusted in the `config.yml` file by editing the `MODEL` parameter within `WHISPER_ASR_SETTINGS`. Possible options are: +The pre-trained Whisper model version can be adjusted in the `.env` file by editing the `W_MODEL` parameter. Possible options are: |Size|Parameters| |---|---| @@ -16,12 +78,6 @@ The pre-trained Whisper model version can be adjusted in the `config.yml` file b |`large-v2`|1550 M| |`large-v3`|1550 M| -We recommend version `large-v2` as it performs better than `large-v3` in our benchmarks. - -You can also specify an S3 URI if you have your own custom model available via S3. - -## Running via Docker using a CUDA compatible GPU - -To run it using a GPU via Docker, check [the instructions from the dane-example-worker](https://github.com/beeldengeluid/dane-example-worker/wiki/Containerization#running-the-container-locally-using-cuda-compatible-gpu). +We recommend version `large-v2` as it performs better than `large-v3` in our [benchmarks](https://opensource-spraakherkenning-nl.github.io/ASR_NL_results/). -Make sure to replace `dane-example-worker` in the `docker run` command with `dane-whisper-asr-worker`. \ No newline at end of file +You can also specify an S3 URI if you have your own custom model available via S3 (by modifying the `W_MODEL` parameter). \ No newline at end of file diff --git a/simple_asr.py b/asr.py similarity index 94% rename from simple_asr.py rename to asr.py index 3cc483d..24c3f9c 100644 --- a/simple_asr.py +++ b/asr.py @@ -25,7 +25,7 @@ def run(input_uri: str, output_uri: str) -> bool: asset_id, extension = get_asset_info(input_path) output_path = asr_output_dir(input_path) - # 2. Check if the input file is suitable for processing any further + # 2. check if the input file is suitable for processing any further transcoded_file_path = try_transcode(input_path, asset_id, extension) if not transcoded_file_path: logger.error("The transcode failed to yield a valid file to continue with") @@ -57,7 +57,7 @@ def run(input_uri: str, output_uri: str) -> bool: return True -# if (S3) output_uri is supplied transfers data to S3 location +# if (S3) output_uri is supplied transfers data to (S3) location def transfer_asr_output(output_path: str, asset_id: str) -> bool: logger.info(f"Transferring {output_path} to S3 (asset={asset_id})") if any( @@ -95,7 +95,7 @@ def asr_already_done(output_dir): return os.path.exists(os.path.join(output_dir, WHISPER_JSON_FILE)) -# check if there is a daan-transcript.json +# check if there is a daan-es-transcript.json def daan_transcript_already_done(output_dir): daan_transcript = os.path.join(output_dir, DAAN_JSON_FILE) logger.info(f"Checking existence of {daan_transcript}") diff --git a/base_util.py b/base_util.py index 6e89196..4ffd5fe 100644 --- a/base_util.py +++ b/base_util.py @@ -31,7 +31,7 @@ def extension_to_mime_type(extension: str) -> str: ".wav": "audio/wav", } - return mime_dict.get(extension, "application/octet-stream") + return mime_dict.get(extension, "unknown") # used by asr.py and transcode.py diff --git a/config.py b/config.py index 9af301d..143667c 100644 --- a/config.py +++ b/config.py @@ -17,14 +17,14 @@ def assert_int(param: str) -> int: def assert_tuple(param: str) -> str: - value = os.environ.get(param, "(0.0, 0.2, 0.4, 0.6, 0.8, 1.0)") + value = os.environ.get(param, "(0.0,0.2,0.4,0.6,0.8,1.0)") try: tuple(eval(value)) return value except ValueError: assert ( False - ), f"Please enter a valid tuple, e.g. (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), for {param}, not |{value}|" + ), f"Please enter a valid tuple, e.g. (0.0,0.2,0.4,0.6,0.8,1.0), for {param}, not |{value}|" # main input & output params diff --git a/daan_transcript.py b/daan_transcript.py index 388060f..6c75526 100644 --- a/daan_transcript.py +++ b/daan_transcript.py @@ -18,7 +18,7 @@ class ParsedResult(TypedDict): carrierId: str -# asr_output_dir e.g mount/asr-output/1272-128104-0000 +# asr_output_dir e.g /data/output/whisper-test/ def generate_daan_transcript(asr_output_dir: str) -> bool: logger.info(f"Generating transcript from: {asr_output_dir}") whisper_transcript = load_whisper_transcript(asr_output_dir) @@ -29,7 +29,7 @@ def generate_daan_transcript(asr_output_dir: str) -> bool: transcript = parse_whisper_transcript(whisper_transcript) try: - # write transcript.json + # write daan-es-transcript.json with open( os.path.join(asr_output_dir, DAAN_JSON_FILE), "w+", encoding="utf-8" ) as f: diff --git a/data/input/testsource__testcarrier.wav b/data/input/testsource__testcarrier.wav deleted file mode 100644 index a9819c5..0000000 Binary files a/data/input/testsource__testcarrier.wav and /dev/null differ diff --git a/data/input/whisper-test.mp3 b/data/input/whisper-test.mp3 new file mode 100644 index 0000000..e8eac20 Binary files /dev/null and b/data/input/whisper-test.mp3 differ diff --git a/data/output/testsource__testcarrier/daan-es-transcript.json b/data/output/testsource__testcarrier/daan-es-transcript.json deleted file mode 100644 index 1af0ba5..0000000 --- a/data/output/testsource__testcarrier/daan-es-transcript.json +++ /dev/null @@ -1,7336 +0,0 @@ -{ - "segments": [ - { - "id": 1, - "seek": 2700, - "start": 16.44, - "end": 18.02, - "text": "Dit is jullie nieuwsdag?", - "tokens": [ - 50364, - 25270, - 307, - 29633, - 26829, - 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image: whisper-worker:latest + image: whisper-asr-worker:latest volumes: - ./model:/model - ./data:/data - container_name: whisper-worker + container_name: whisper-asr-worker # args: ["--output-uri"] env_file: - .env.override @@ -12,9 +12,9 @@ services: options: max-size: 20m restart: no - # Uncomment below to use the GPU/CUDA + # Comment below to use the CPU instead of the GPU/CUDA deploy: resources: reservations: - devices: + devices: - capabilities: [gpu] \ No newline at end of file diff --git a/download.py b/download.py index 46679ca..ebbef59 100644 --- a/download.py +++ b/download.py @@ -17,8 +17,8 @@ @dataclass class DownloadResult: file_path: str # target_file_path, # TODO harmonize with dane-download-worker - download_time: float = -1 # time (secs) taken to receive data after request - mime_type: str = "unknown" # download_data.get("mime_type", "unknown"), + mime_type: str # download_data.get("mime_type", "unknown"), + download_time: float = -1 # time (ms) taken to receive data after request content_length: int = -1 # download_data.get("content_length", -1), @@ -34,7 +34,6 @@ def http_download(url: str) -> Optional[DownloadResult]: logger.info(f"Checking if {url} was already downloaded") fn = os.path.basename(urlparse(url).path) input_file = os.path.join(input_file_dir, fn) - # TODO rethink how to return mime_type _, extension = get_asset_info(input_file) mime_type = extension_to_mime_type(extension) @@ -52,11 +51,10 @@ def http_download(url: str) -> Optional[DownloadResult]: file.close() download_time = (time.time() - start_time) * 1000 # time in ms return DownloadResult( - input_file, download_time, mime_type # TODO add content_length + input_file, mime_type, download_time # TODO add content_length ) -# e.g. s3://dane-asset-staging-gb/assets/2101608170158176431__NOS_JOURNAAL_-WON01513227.mp4 def s3_download(s3_uri: str) -> Optional[DownloadResult]: logger.info(f"Checking if {s3_uri} was already downloaded") @@ -88,7 +86,7 @@ def s3_download(s3_uri: str) -> Optional[DownloadResult]: if not success: logger.error("Failed to download input data from S3") return None - download_time = time.time() - start_time + download_time = (time.time() - start_time) * 1000 # time in ms return DownloadResult( - input_file, download_time, mime_type # TODO add content_length + input_file, mime_type, download_time # TODO add content_length ) diff --git a/main.py b/main.py index 1754cd6..ca31499 100644 --- a/main.py +++ b/main.py @@ -3,7 +3,7 @@ from base_util import LOG_FORMAT from config import input_uri, output_uri -import simple_asr +import asr # initialises the root logger @@ -42,6 +42,6 @@ logger.info(f"Got the following CMD line arguments: {args}") if args.input_uri: - simple_asr.run(args.input_uri, args.output_uri) + asr.run(args.input_uri, args.output_uri) else: logger.error("Please supply the --input param (--output is optional)") diff --git a/output.json b/output.json deleted file mode 100644 index fd1bad3..0000000 --- a/output.json +++ /dev/null @@ -1,2974 +0,0 @@ -{ - "segments": [ - { - "id": 1, - "seek": 2508, - "start": 0.0, - "end": 4.74, - "text": "De Nederlandse Spoorwegen en Schiphol, toch twee gevoelige dossiers op het verkeersgebied,", - "tokens": [ - 50364, - 1346, - 31888, - 405, - 1738, - 28656, - 13683, - 465, - 2065, - 647, - 5449, - 11, - 22587, - 30660, - 1519, - 3080, - 338, - 3969, - 47831, - 4890, - 999, - 3639, - 1306, - 330, - 433, - 10848, - 1091, - 11, - 50620 - ], - "temperature": 0.0, - "avg_logprob": -0.23489932205853045, - "compression_ratio": 1.6159169550173011, - "no_speech_prob": 0.0225830078125, - "words": [ - { - "text": "De", - "start": 0.0, - "end": 0.78, - "confidence": 0.2237548828125 - }, - { - "text": "Nederlandse", - 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"no_speech_prob": 0.2186279296875, - "words": [ - { - "text": "en", - "start": 109.12, - "end": 109.6, - "confidence": 0.83154296875 - }, - { - "text": "uitgaven", - "start": 109.6, - "end": 110.3, - "confidence": 0.724609375 - }, - { - "text": "aan", - "start": 110.3, - "end": 110.52, - "confidence": 0.88916015625 - }, - { - "text": "ICT.", - "start": 110.52, - "end": 111.06, - "confidence": 0.890625 - } - ] - } - ] -} \ No newline at end of file diff --git a/python-docker-entrypoint.sh b/python-docker-entrypoint.sh deleted file mode 100644 index b3b838a..0000000 --- a/python-docker-entrypoint.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/bin/sh - -echo "Starting DANE Whisper ASR worker" - -python worker.py "$@" - -echo "The worker has finished" \ No newline at end of file diff --git a/python.Dockerfile b/python.Dockerfile deleted file mode 100644 index 81f3574..0000000 --- a/python.Dockerfile +++ /dev/null @@ -1,37 +0,0 @@ -FROM docker.io/python:3.10@sha256:c6b64ba9c0f03c41e10f1e6053ca2ecf2dbced44098f8d56ed579aa50e839889 AS req - -RUN python3 -m pip install pipx && \ - python3 -m pipx ensurepath - -RUN pipx install poetry==1.7.1 && \ - pipx inject poetry poetry-plugin-export && \ - pipx run poetry config warnings.export false - -COPY ./poetry.lock ./poetry.lock -COPY ./pyproject.toml ./pyproject.toml -RUN pipx run poetry export --format requirements.txt --output requirements.txt - -FROM docker.io/python:3.10@sha256:c6b64ba9c0f03c41e10f1e6053ca2ecf2dbced44098f8d56ed579aa50e839889 - -# Create dirs for: -# - Injecting config.yml: /root/.DANE -# - Mount point for input & output files: /data -# - Storing the source code: /src -RUN mkdir \ - /data \ - /mnt/dane-fs \ - /model \ - /root/.DANE \ - /src - -WORKDIR /src - -COPY --from=req ./requirements.txt requirements.txt -RUN pip install --no-cache-dir -r requirements.txt - -COPY ./ /src - -# Write provenance info about software versions to file -RUN echo "dane-whisper-asr-worker;https://github.com/beeldengeluid/dane-whisper-asr-worker/commit/$(git rev-parse HEAD)" >> /software_provenance.txt - -ENTRYPOINT ["./python-docker-entrypoint.sh"] diff --git a/scripts/check-project.sh b/scripts/check-project.sh index 022efde..c2f5596 100755 --- a/scripts/check-project.sh +++ b/scripts/check-project.sh @@ -10,13 +10,17 @@ poetry shell cd ../ # run tests (configured in pyproject.toml) +echo "Running pytest" pytest # check lint rules (configured in .flake8) +echo "Running flake8" flake8 # check formatting (configured in pyproject.toml) +echo "Running black" black --check . # check type annotations (configured in pyproject.toml) +echo "Running mypy" mypy . diff --git a/scripts/intial-test-program-ids.txt b/scripts/intial-test-program-ids.txt deleted file mode 100644 index a69d8c3..0000000 --- a/scripts/intial-test-program-ids.txt +++ /dev/null @@ -1 +0,0 @@ -2102003230266006831;NOS_JOURNAAL_-WON02065023 diff --git a/scripts/transcribe-daan-carriers.sh b/scripts/transcribe-daan-carriers.sh deleted file mode 100755 index b247e46..0000000 --- a/scripts/transcribe-daan-carriers.sh +++ /dev/null @@ -1,45 +0,0 @@ -#!/bin/bash - -SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd ) -LOW_RES_THROTTLER="http://your-host/viz" -DEV_WORKSPACE_ROOT="/home/YOU/workspace" -DOCKER_IMAGE="917951871879.dkr.ecr.eu-west-1.amazonaws.com/kaldi_nl:v1.2" -INPUT_FILE=$1 -INPUT_DIR=$DEV_WORKSPACE_ROOT/dane-asr-worker/data - -if [ -z "$1" ] - then - echo "Please supply an input file" - exit 1 -fi - -while read LINE; do - PID=`echo $LINE | cut -d';' -f1` - CARRIER_ID=`echo $LINE | cut -d';' -f2` - echo "Program ID: $PID" - echo "Carrier ID: $CARRIER_ID" - FILE_ID="${PID}__${CARRIER_ID}" - echo "File ID: $FILE_ID" - - MP4="$INPUT_DIR/$FILE_ID.mp4" - MP3="$INPUT_DIR/$FILE_ID.mp3" - OUTPUT_DIR="$INPUT_DIR/output/$FILE_ID" - # only download if it's not there yet - if ! [ -f $MP4 ] ; then - echo "Downloading $CARRIER_ID"; - curl "${LOW_RES_THROTTLER}/${CARRIER_ID}" -o $MP4; - fi - # only transcode if it's not there yet - if ! [ -f $MP3 ] ; then - echo "Extracting audio from $MP4"; - # ffmpeg -nostdin -y -i $MP4 -ar 16000 -ac 1 -b:a 256k $MP3 -v quiet; - ffmpeg -y -i $MP4 $MP3; - echo "done with this one"; - fi - # only extract OUTPUT_DIR if it's not there yet - if ! [ -d $OUTPUT_DIR ] ; then - mkdir -p $OUTPUT_DIR - echo "Generating transcript from $MP3"; - docker run --rm -v $DEV_WORKSPACE_ROOT/dane-asr-worker/models:/models -v $INPUT_DIR:/data $DOCKER_IMAGE ./decode_OH.sh /data/$FILE_ID.mp3 /data/output/$FILE_ID - fi -done <$INPUT_FILE diff --git a/scripts/transcript-example.json b/scripts/transcript-example.json deleted file mode 100644 index 51a2915..0000000 --- a/scripts/transcript-example.json +++ /dev/null @@ -1,49 +0,0 @@ -{ - "wordTimes": [ - 1300, - 1630, - 1810, - 1970, - 2260, - 2400, - 2550, - 2925, - 3710, - 4010, - 4520, - 4920, - 5250, - 5340, - 5850, - 6180, - 6330, - 6450, - 6540, - 7500, - 7710, - 8430, - 8730, - 9600, - 9780, - 10380, - 10500, - 10590, - 11760, - 11910, - 12000, - 12060, - 12510, - 12630, - 12780, - 13350, - 13860, - 13980, - 14040, - 14520 - ], - "sequenceNr": 0, - "start": 0, - "fragmentId": "0001", - "words": "Negen uur joh boos met het nos journaal de fractieleiders rutte wilders en verhagen sturen aan op een minderheidskabinet van vvd en cda met gedoogsteun van de pvv dat is de uitkomst van hun informele gesprekken in de afgelopen week.", - "carrierId": "2010073021RA5-RCR1007301P" -} \ No newline at end of file diff --git a/whisper.py b/whisper.py index 5080508..e0c01b8 100644 --- a/whisper.py +++ b/whisper.py @@ -1,4 +1,4 @@ -# import ast +import ast import json import logging import os @@ -10,7 +10,7 @@ w_best_of, w_device, w_model, - # w_temperature, + w_temperature, w_vad, w_word_timestamps, ) @@ -40,7 +40,7 @@ def run_asr(input_path, output_dir) -> bool: vad_filter=w_vad, beam_size=w_beam_size, best_of=w_best_of, - # temperatures=ast.literal_eval(w_temperature), + temperature=ast.literal_eval(w_temperature), language="nl", word_timestamps=w_word_timestamps, )