demucs-onnx — exported graphs
Demucs music source separation, exported to ONNX so it can run without PyTorch. These are the weights for demucs-onnx, which runs them from Python, C++, Rust, Java, C# and JavaScript.
Try them in the browser: the demucs-onnx Space.
What is in here
Each model is a pair — <name>.onnx, the graph, and <name>.json, a sidecar naming
its stems, sample rate and STFT parameters. A bag (htdemucs_ft) is a manifest
listing four member graphs and the per-stem weights they are blended with.
| file | stems | notes |
|---|---|---|
htdemucs | drums, bass, other, vocals | the v4 default |
htdemucs_fp16 | same | half-precision weights |
htdemucs_int8 | same | quantised; much faster on a CPU |
htdemucs_6s | + guitar, piano | six-stem model |
htdemucs_ft | drums, bass, other, vocals | bag of four fine-tuned networks, best quality |
hdemucs_mmi | drums, bass, other, vocals | the v3 hybrid model |
Using them
pip install "git+https://github.com/HRSadeghi/demucs-onnx.git#subdirectory=python"
hf download HRSadeghi/demucs-onnx htdemucs.onnx htdemucs.json --local-dir models
demucs-onnx song.wav -m models/htdemucs -o separated
The graph is cut at the complex boundary: the STFT, the ISTFT and the overlap-add happen in host code, and only the conv encoder / transformer / conv decoder is in the ONNX graph. The project README explains why, and every language binding in the repository reads exactly these two files.
Licence
The runtime code is MIT. The weights are Meta's, and carry the terms of the Demucs licence — non-commercial (CC BY-NC 4.0) for the v3/v4 checkpoints. Check that before you put this in a product.