Instructions to use starkdmi/USS_MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use starkdmi/USS_MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir USS_MLX starkdmi/USS_MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
license: apache-2.0
base_model: RSNuts/Universal_Source_Separation
tags:
- mlx
- audio
- audio-to-audio
- source-separation
USS ResUNet30 (MLX)
Universal source separation — extracts a chosen sound class from a mixture. Converted to MLX from the ByteDance USS code and the published RSNuts/Universal_Source_Separation checkpoint.
Files
| file | size |
|---|---|
resunet30_fp32.safetensors |
102 MiB |
resunet30_fp16.safetensors |
51 MiB |
Precision
| precision | size | speed | peak memory | quality vs fp32 |
|---|---|---|---|---|
| fp32 | 102 MiB | 27.2x | 1203 MiB | reference |
| fp16 | 51 MiB | 25.8x | 1259 MiB | 57.3 dB |
Bold marks the best value in each column. Quality is fp16 against this model's own fp32 output — the cost of the precision change, not a score for the model.
fp16 halves the download. On the machine below it was also slightly slower and used slightly more memory than fp32, since the forward pass dominates the footprint rather than the weights.
Measured on an Apple M1 Pro (16 GB), 30 s of audio, three timed runs per configuration; speed is realtime factor. These are single-machine figures — expect different absolute numbers on other hardware, and treat the ordering as indicative rather than guaranteed.
Conditioning
USS is conditioned on a 527-dimensional vector over the
AudioSet classes, which selects what the
separator extracts. It is an input rather than a model weight and is not shipped
here: each vector is a normalised multi-hot indicator over a class group — zero
everywhere, 1/n at each of the n chosen indices — so a class list fully
determines it.
Class indices follow AudioSet's class_labels_indices.csv, published by Google
with the dataset under CC BY 4.0.