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--- fork from https://github.com/ahmetoner/whisper-asr-webservice/

Whisper ASR Webservice

Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification. For more details: github.com/openai/whisper

Features

Current release (v1.1.0) supports following whisper models:

Usage

Whisper ASR Webservice now available on Docker Hub. You can find the latest version of this repository on docker hub for CPU and GPU.

Docker Hub: https://hub.docker.com/r/onerahmet/openai-whisper-asr-webservice

For CPU:

docker run -d -p 9000:9000 -e ASR_MODEL=base onerahmet/openai-whisper-asr-webservice:latest

For GPU:

docker run -d --gpus all -p 9000:9000 -e ASR_MODEL=base onerahmet/openai-whisper-asr-webservice:latest-gpu

For MacOS (CPU only):

GPU passthrough does not work on macOS due to fundamental design limitations of Docker. Docker actually runs containers within a LinuxVM on macOS. If you wish to run GPU-accelerated containers, I'm afraid Linux is your only option.

The :latest image tag provides both amd64 and arm64 architectures:

docker run -d -p 9000:9000 -e ASR_MODEL=base onerahmet/openai-whisper-asr-webservice:latest
# Interactive Swagger API documentation is available at http://localhost:9000/docs

Swagger UI

Available ASR_MODELs are tiny, base, small, medium, large, large-v1 and large-v2. Please note that large and large-v2 are the same model.

For English-only applications, the .en models tend to perform better, especially for the tiny.en and base.en models. We observed that the difference becomes less significant for the small.en and medium.en models.

Run (Development Environment)

Install poetry with following command:

pip3 install poetry

Install torch with following command:

# just for GPU:
pip3 install torch==1.13.0+cu117 -f https://download.pytorch.org/whl/torch

Install packages:

poetry install

Starting the Webservice:

poetry run gunicorn --bind 0.0.0.0:9000 --workers 1 --timeout 0 app.webservice:app -k uvicorn.workers.UvicornWorker

With docker compose:

For CPU:

docker-compose up --build

For GPU:

docker-compose up --build -f docker-compose.gpu.yml

Quick start

After running the docker image interactive Swagger API documentation is available at localhost:9000/docs

There are 2 endpoints available:

  • /asr (TXT, VTT, SRT, TSV, JSON)
  • /detect-language

Automatic Speech recognition service /asr

If you choose the transcribe task, transcribes the uploaded file. Both audio and video files are supported (as long as ffmpeg supports it).

Note that you can also upload video formats directly as long as they are supported by ffmpeg.

You can get TXT, VTT, SRT, TSV and JSON output as a file from /asr endpoint.

You can provide the language or it will be automatically recognized.

If you choose the translate task it will provide an English transcript no matter which language was spoken.

Returns a json with following fields:

  • text: Contains the full transcript
  • segments: Contains an entry per segment. Each entry provides time stamps, transcript, token ids and other metadata
  • language: Detected or provided language (as a language code)

Language detection service /detect-language

Detects the language spoken in the uploaded file. For longer files it only processes first 30 seconds.

Returns a json with following fields:

  • detected_language
  • language_code

Build

Build .whl package

poetry build

Configuring the Model

export ASR_MODEL=base

Docker Build

For CPU

# Build Image
docker build -t whisper-asr-webservice .

# Run Container
docker run -d -p 9000:9000 whisper-asr-webservice
# or
docker run -d -p 9001:9000 -e ASR_MODEL=base whisper-asr-webservice3

For GPU

# Build Image
docker build -f Dockerfile.gpu -t whisper-asr-webservice-gpu .

# Run Container
docker run -d --gpus all -p 9000:9000 whisper-asr-webservice-gpu
# or
docker run -d --gpus all -p 9000:9000 -e ASR_MODEL=base whisper-asr-webservice-gpu

Cache

The ASR model is downloaded each time you start the container, using the large model this can take some time. If you want to decrease the time it takes to start your container by skipping the download, you can store the cache directory (/root/.cache/whisper) to an persistent storage. Next time you start your container the ASR Model will be taken from the cache instead of being downloaded again.

Important this will prevent you from receiving any updates to the models.

docker run -d -p 9000:9000 -e ASR_MODEL=large -v //c/tmp/whisper:/root/.cache/whisper onerahmet/openai-whisper-asr-webservice:latest