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).
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:
| Size | Parameters |
|---|---|
tiny |
39 M |
base |
74 M |
small |
244 M |
medium |
769 M |
large |
1550 M |
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.
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.