This is an API service that receives audio file paths via the endpoint POST /asr.
For the server specification (request structure and response behavior) see the OpenAPI specificaiton in /docs.
For any other documentation refer to WhisperX readme.
Current release (v1.0.0) supports following whisper models:
WhisperX service now available on Docker Hub. You can find the latest version of this repository on docker hub for GPU.
Docker Hub: https://hub.docker.com/r/chinaboard/whisperx-service
For GPU:
docker pull chinaboard/whisperx-service:latest
docker run -d --gpus all -p 9000:9000 -e ASR_MODEL=large chinaboard/whisperx-service:latest# Interactive Swagger API documentation is available at http://localhost:9000/docs
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.
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
# Build Image
docker build -t whisperx-service .
# Run Container
docker run -d --gpus all -p 9000:9000 whisperx-service
# or
docker run -d --gpus all -p 9000:9000 -e ASR_MODEL=base whisperx-serviceThe 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 /tmp/whisper:/root/.cache/whisper whisperx-service