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 (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(OUTDATED BUT STILL MIGHT BE RELEVANT) To run it using a GPU via Docker, check the instructions from the dane-example-worker.

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 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 MODEL_BASE_DIR to the path where the model files can be found.

The pre-trained Whisper model version can be adjusted in the .env file by editing the W_MODEL parameter. Possible options are:

We recommend version large-v2 as it performs better than large-v3 in our benchmarks.

You can also specify an S3/HTTP URI if you want to load your own (custom) model (by modifying the W_MODEL parameter).

Config

The parameters used to configure the application can be found under .env file. You will also need to create a .env.override file that contains secrets related to the S3 connection that should normally not be exposed in the .env file. The parameters that should be updated with valid values in the .env.override are:

• S3_ENDPOINT_URL
• AWS_ACCESS_KEY_ID
• AWS_SECRET_ACCESS_KEY