stemstudio-htdemucs-onnx

htdemucs — ONNX graphs for onnxruntime-web (WebGPU)

Hybrid Transformer Demucs, exported to ONNX and prepared for in-browser inference with onnxruntime-web on the WebGPU backend. Used by stemstudio.jp for local, upload-free stem separation.

Derived from facebookresearch/demucs (MIT). See LICENSE — the upstream notice travels with these files.

Files

Two things that are easy to get wrong

1. The .gzbin extension is deliberate. These are raw gzip streams stored as ordinary files, and the browser inflates them with DecompressionStream('gzip'). They are not named .gz, because some hosts and dev servers see that extension and add content-encoding: gzip, at which point the transport decompresses them and the application decompresses them again — which fails with incorrect header check. Detect gzip by the leading magic bytes (1f 8b), not by header or extension.

The compression is in the file rather than in the transport because model hosts generally serve .onnx uncompressed regardless of Accept-Encoding.

2. Build order matters: optimize before fp16, not after. Running the graph optimizer on an fp16 graph overflows folded constants to inf and every output becomes NaN. Worse, nan > tolerance is false, so a naive comparison reports "max diff 0.000e+00 / OK" on a completely broken model. The order used here is: fp32 export → graph optimization → fp16 weights → inverse STFT folded into the graph.

Why the optimized graph is WebGPU-only

Measured on one 7.8 s segment, same model, same host:

On WebGPU, session build gets ~11× faster. On wasm, heap grows 2.7× and inference gets slower. "Ship the optimized one and everyone gets faster" is false — pick per execution provider.

Training data

The upstream weights were trained on MUSDB18-HQ and additional sources. The MIT license covers the code and these derived graphs; the dataset carries its own terms, which are worth checking for your own use.