---
license: cc-by-nc-4.0
library_name: mule-torch
tags:
  - audio
  - music
  - music-information-retrieval
  - embeddings
  - representation-learning
  - feature-extraction
  - pytorch
  - onnx
  - nfnet
pipeline_tag: feature-extraction
---

# MULE (PyTorch) — `matteospanio/mule`

Pretrained weights for an **unofficial PyTorch port** of **MULE** (Musicset
Unsupervised Large Embedding), the SF-NFNet-F0 music-audio representation model
from SiriusXM/Pandora:

> *Supervised and Unsupervised Learning of Audio Representations for Music
> Understanding*, M. C. McCallum, F. Korzeniowski, S. Oramas, F. Gouyon,
> A. F. Ehmann. ISMIR 2022. <https://arxiv.org/abs/2210.03799>

These weights were **converted, not re-trained** — transferred from the original
TensorFlow/Keras `model.keras` into the PyTorch implementation and verified to be
numerically equivalent (end-to-end clip-embedding cosine **0.9999999** vs the
original pipeline; ONNX backbone max-abs `< 1e-6`; 62.35 M params).

Library / code: **<https://github.com/matteospanio/mule-torch>**

> ⚠️ **Unofficial.** This is an independent community port from TensorFlow to
> PyTorch. It is **not affiliated with, endorsed by, or maintained by** SiriusXM,
> Pandora, or the original authors. All credit for the model goes to them.

## Files

| File | What |
|---|---|
| `model.safetensors` | Full model state dict (SF-NFNet-F0 backbone + mel filterbank buffer), ~267 MB. |
| `config.json` | Architecture + frontend + slicing constants (rebuilds `MuleConfig`). |
| `backbone.onnx` | Self-contained ONNX export of the backbone (`(N,1,96,300)` log-mel slice → `(N,1728)`), opset 17, dynamic batch. ~252 MB. |

## Usage

```bash
pip install mule-torch          # or: pip install git+https://github.com/matteospanio/mule-torch
```

```python
import torch
from mule_torch import MuleModel

# Downloads these weights from the Hub by default.
model = MuleModel.from_pretrained()                 # == from_pretrained(hf_repo="matteospanio/mule")
waveform = torch.randn(1, 16000 * 10)               # (B, T) mono @ 16 kHz, in [-1, 1]
emb = model(waveform)                               # (B, 1728)
```

### ONNX (backbone only)

The full waveform→embedding path includes a data-dependent number of 2-second
slices, so the ONNX export covers the **backbone** (one standardized 96×300
log-mel slice → 1728-d). Do the mel front-end + slicing in torch/host, then run
slices through `backbone.onnx`:

```python
import onnxruntime as ort, numpy as np
sess = ort.InferenceSession("backbone.onnx", providers=["CPUExecutionProvider"])
emb = sess.run(None, {"mel_slice": slices.astype(np.float32)})[0]   # (N, 1728)
```

## Input convention

16 kHz mono waveform in `[-1, 1]`. The model computes a 96-band log-mel
spectrogram, slices it into 96×300 windows every ~2 s, runs the backbone, and
mean-pools the per-slice 1728-d embeddings into one vector per clip.

> The original `AudioFile` reader scales PCM16 by `1/2^16`; conventional `[-1,1]`
> audio tracks the original closely but isn't bit-identical (the
> `log10(10000·x+1)` mel compression is non-linear).

## License

These weights are a derivative of the original MULE weights, released by
Pandora/SiriusXM under **CC BY-NC 4.0**, and inherit that **non-commercial**
license. The `mule-torch` source code is GPL-3.0-only. Please cite McCallum et
al. (2022).

```bibtex
@inproceedings{mccallum2022mule,
  title     = {Supervised and Unsupervised Learning of Audio Representations for Music Understanding},
  author    = {McCallum, Matthew C. and Korzeniowski, Filip and Oramas, Sergio and Gouyon, Fabien and Ehmann, Andreas F.},
  booktitle = {Proceedings of the 23rd International Society for Music Information Retrieval Conference (ISMIR)},
  year      = {2022},
  url       = {https://arxiv.org/abs/2210.03799}
}
```