Originally from: iky1e/demucs-mlx
Float16 variant: mlx-community/demucs-mlx-fp16
Demucs — MLX
MLX-compatible weights for all 8 pretrained Demucs models, converted to safetensors format for inference on Apple Silicon.
Demucs is a music source separation model that splits audio into stems: drums, bass, other, vocals (and guitar, piano for 6-source models).
Models
| Model | What it is | Architecture | Sub-models | Sources | Weights | Tensors |
|---|---|---|---|---|---|---|
htdemucs | Default v4 model, best speed/quality balance | HTDemucs (v4) | 1 | 4 | 160 MB | 573 |
htdemucs_ft | Fine-tuned v4, best overall quality | HTDemucs (v4) | 4 (fine-tuned) | 4 | 641 MB | 2292 |
htdemucs_6s | 6-source v4 (adds guitar + piano stems) | HTDemucs (v4) | 1 | 6 | 105 MB | 565 |
hdemucs_mmi | v3 hybrid, trained on more data | HDemucs (v3) | 1 | 4 | 319 MB | 379 |
mdx | v3 bag-of-models ensemble | Demucs + HDemucs | 4 (bag) | 4 | 1.3 GB | 1298 |
mdx_extra | v3 ensemble trained on extra data | HDemucs | 4 (bag) | 4 | 1.2 GB | 1516 |
mdx_q | Quantized v3 ensemble (same quality, smaller) | Demucs + HDemucs | 4 (bag) | 4 | 1.3 GB | 1298 |
mdx_extra_q | Quantized v3 extra ensemble | HDemucs | 4 (bag) | 4 | 1.2 GB | 1516 |
All models output stereo audio at 44.1 kHz.
Origin
- Original model/repo: adefossez/demucs
- License: MIT (same as original Demucs)
- Conversion path: PyTorch checkpoints → safetensors + JSON config (direct, no intermediary)
- Swift MLX port: iky1e/demucs-mlx-swift
No fine-tuning or quantization was applied — these are direct conversions of the original pretrained weights.
Files
Each model consists of two files at the repo root:
{model_name}.safetensors— model weights (float32){model_name}_config.json— model class, architecture config, and bag-of-models metadata
Usage
Swift (demucs-mlx-swift)
Models are downloaded automatically from this repo. No manual setup required.
# Separate a song into stems
demucs-mlx-swift -n htdemucs song.wav
# Use a specific model
demucs-mlx-swift -n htdemucs_ft song.wav
# Two-stem mode (vocals + instrumental)
demucs-mlx-swift -n htdemucs --two-stems vocals song.wav
Or use the Swift API directly:
import DemucsMLX
let separator = try DemucsSeparator(modelName: "htdemucs")
let result = try separator.separate(fileAt: URL(fileURLWithPath: "song.wav"))
Python (demucs-mlx)
pip install demucs-mlx
demucs-mlx -n htdemucs song.wav
Converting from PyTorch
To reproduce the export directly from PyTorch Demucs checkpoints:
pip install demucs safetensors numpy
# Export all 8 models
python export_from_pytorch.py --out-dir ./output
# Export specific models
python export_from_pytorch.py --models htdemucs htdemucs_ft --out-dir ./output
The conversion script (export_from_pytorch.py) is available in the demucs-mlx-swift repo under scripts/.
Citation
@inproceedings{rouard2022hybrid,
title={Hybrid Transformers for Music Source Separation},
author={Rouard, Simon and Massa, Francisco and Defossez, Alexandre},
booktitle={ICASSP 23},
year={2023}
}
@inproceedings{defossez2021hybrid,
title={Hybrid Spectrogram and Waveform Source Separation},
author={Defossez, Alexandre},
booktitle={Proceedings of the ISMIR 2021 Workshop on Music Source Separation},
year={2021}
}