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edgetools/
umx-hq

Name: umx-hq
Availability: InStock

onnx

onnxruntime-web

source-separation

music-source-separation

open-unmix

audio-to-audio

mit

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Open-Unmix UMX-HQ (ONNX)

An ONNX export of the vocals stem of UMX-HQ, the Open-Unmix music source separation models trained on MUSDB18-HQ.


File	`vocals.onnx`
Size	17,820,856 bytes (17.0 MiB)
SHA-256	`3d05709ff7197bbd4a33aee759e4e82002acb491f41c94770227ab57507b6ccb`

Open-Unmix has one independent graph per stem (vocals / drums / bass / other); only vocals is published here. The other three can be exported from the same upstream weights with the same procedure — see the signal parameters below.

This is an export of the published UMX-HQ PyTorch weights (Zenodo record 3370489) — the spectrogram model only. The STFT, the mask application and the ISTFT are deliberately left outside the graph so a host can implement them in whatever environment it runs in; the parameters are given below.

Tensor contract

The graph maps a magnitude spectrogram to an estimated magnitude spectrogram for its stem. Read input/output names off the graph rather than hardcoding them.

Input — float32[1, 2, 2049, frames]: STFT magnitudes as (batch, channels, bins, frames) — batch 1, 2 channels (stereo), nb_bins = 2049.
Output — the same shape, the vocals estimate.

Every Open-Unmix stem graph takes the same mixture tensor, so a caller running more than one builds the spectrogram once and reuses it across sessions.

Signal parameters

These must match or the output is wrong:


Sample rate	44,100 Hz, stereo
FFT size	4096
Hop	1024
Window	Hann (periodic), centred (reflect-padded by `n_fft / 2`)
Bins	2049 (`n_fft / 2 + 1`)

To reconstruct audio, apply the estimated magnitudes as a ratio mask over the mixture's phase and inverse-STFT. That is Open-Unmix's niter=0 path — no Wiener/EM refinement — which is what this export is validated against.

Why UMX-HQ rather than UMX-L

UMX-HQ is the same architecture at half the LSTM width (hidden_size 512 vs 1024). It separates slightly less cleanly than UMX-L, and it is MIT-licensed, whereas the UMX-L weights are CC BY-NC-SA 4.0 (non-commercial).

An MIT licence on the model grants no rights over the audio you run through it. Separating a copyrighted recording still needs its own clearance.

Licence and credit

Weights are MIT, from sigsep/open-unmix-pytorch. Please cite:

bibtex

@article{stoter19,
  author  = {St\"oter, Fabian-Robert and Uhlich, Stefan and Liutkus, Antoine and Mitsufuji, Yuki},
  title   = {Open-Unmix - A Reference Implementation for Music Source Separation},
  journal = {Journal of Open Source Software},
  year    = 2019,
  doi     = {10.21105/joss.01667}
}

edgetools/umx-hq