A tool to export OpenAI Whisper speech recognition models to ONNX.
The core model file (model.py) has been isolated from the original Whisper codebase. Other files are not included or needed.
Taking some of the code in whisper-openvino as a starting point, the model's key-value structure has been modified to be passed as an input or output, removing the need for hooks.
The TextDecoder, ResidualAttentionBlock and MultiHeadAttention classes have also been further modified to directly output the cross-attention weights, without any hooks.
The exported ONNX models are primarily intended to be used with Echogarden, which has its own implementation of the higher-level Whisper API, and is written in TypeScript. The code doesn't include a way to use the exported models from Python. However, since it is closely related to the code on whisper-openvino, which adapts the higher-level Python API to use it, it should be possible to make it work with it, with some modifications.
You can download pre-exported models for all sizes, except large, large-v1, large-v2, large-v3 from the releases section of the whisper-onnx-models repository.
Ensure you have torch and onnx Python libraries installed.
Copy the official Whisper model files (.pt) to the pytorch-models subdirectory.
To get the models you can use the official Whisper CLI, which would auto-download a model as needed. On Windows, the downloaded models should be stored at %userprofile%\.cache\whisper.
Alternatively, you may find direct download URLs in the original whisper source code.
Run:
python export-whisper-onnx.py [whisper-model-name]
For example:
python export-whisper-onnx.py tiny
The exported encoder and decoder ONNX models would be located at:
onnx-models/tiny/encoder.onnx
onnx-models/tiny/decoder.onnx
MIT