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:
@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}
}