---
pipeline_tag: audio-to-audio
library_name: onnxruntime
tags:
  - stem-separation
  - open-unmix
  - umxl
  - onnx
  - on-device
  - android
license: mit
---

# Open-Unmix UMX-L — ONNX Stem Separation Models

4 source-specific ONNX models for stem separation, exported from [Open-Unmix UMX-L](https://github.com/sigsep/open-unmix-pytorch).

## Models

| File | Source | Size | Format |
|------|--------|------|--------|
| `umxl_vocals.onnx` | Vocals | ~108 MB | ONNX opset 17 |
| `umxl_drums.onnx` | Drums | ~108 MB | ONNX opset 17 |
| `umxl_bass.onnx` | Bass | ~108 MB | ONNX opset 17 |
| `umxl_other.onnx` | Other | ~108 MB | ONNX opset 17 |

## Architecture

Open-Unmix operates in the **spectrogram domain** (not waveform). Each model:
- **Input**: Magnitude spectrogram `(batch, 2, 2049, nb_frames)` — stereo, 2049 frequency bins
- **Output**: Masked spectrogram `(batch, 2, 2049, nb_frames)` — same shape
- **Internal**: Linear → BatchNorm → LSTM (3 layers) → Linear → Output scaling → ReLU masking

## Usage

STFT and ISTFT must be performed on-device:

1. **STFT**: `n_fft=4096`, `n_hop=1024`, sample rate `44100 Hz`
2. **Magnitude**: Compute `|STFT(audio)|` → `(1, 2, 2049, frames)`
3. **Model**: Run each source model on the magnitude spectrogram
4. **Reconstruct**: Multiply masked spectrogram with complex STFT phase
5. **ISTFT**: Invert back to waveform

## Config

`config.json` contains STFT parameters:
```json
{
  "n_fft": 4096,
  "n_hop": 1024,
  "nb_bins": 2049,
  "sample_rate": 44100,
  "sources": ["vocals", "drums", "bass", "other"],
  "model": "umxl"
}
```

## Verification

All models verified with ONNX Runtime (max absolute diff < 0.00001 vs PyTorch).

## For DJNed App

These models are used by the DJNed Android app for on-device stem separation.
Download is managed by the app's Model Manager.