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

Run (Docker Hub)

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

For CPU: https://hub.docker.com/r/onerahmet/openai-whisper-asr-webservice

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

For GPU: https://hub.docker.com/r/onerahmet/openai-whisper-asr-webservice-gpu

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

# Interactive Swagger API documentation is available at http://localhost:9000/docs

Available ASR_MODELs are tiny, base, small, medium and large

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==1.2.0

Install packages:

poetry install

Starting the Webservice:

poetry run whisper_asr

Quick start

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

There are four endpoints available:

/asr
/get-srt
/get-vtt
/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).

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)

Subtitle generating services /get-srt and /get-vtt

These two POST endpoints have the same interface as /asr but they return a subtitle file (either srt or vtt).

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

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
langauge_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 9000:9000 -e ASR_MODEL=base whisper-asr-webservice

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

TODO

Detailed README file
Unit tests
Hosted Swagger documentation with descriptions
Recognize from path
Batch recognition from given path/folder
Live recognition support with HLS
gRPC support