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Float16 MLX-compatible weights for all 8 pretrained Demucs models, converted to safetensors format for inference on Apple Silicon.
Source du modèle
Description de la source
Float16 MLX-compatible weights for all 8 pretrained Demucs models, converted to safetensors format for inference on Apple Silicon.
This is the float16 variant of iky1e/demucs-mlx — same models, half the file size, identical output quality. Recommended for Apple Silicon where memory is constrained (iOS, smaller Macs).
Sources
1 sourceVérifié 22 août
Artefacts du modèle
8 artefactsExtraits de sources
2 extraitsDemucs is a music source separation model that splits audio into stems: drums, bass, other, vocals (and guitar, piano for 6-source models).
| Model | What it is | Architecture | Sub-models | Sources | Weights (fp16) | Weights (fp32) |
|---|---|---|---|---|---|---|
htdemucs | Default v4 model, best speed/quality balance | HTDemucs (v4) | 1 | 4 | 80 MB | 160 MB |
htdemucs_ft | Fine-tuned v4, best overall quality | HTDemucs (v4) | 4 (fine-tuned) | 4 | 321 MB | 641 MB |
htdemucs_6s | 6-source v4 (adds guitar + piano stems) | HTDemucs (v4) | 1 | 6 | 52 MB | 105 MB |
hdemucs_mmi | v3 hybrid, trained on more data | HDemucs (v3) | 1 | 4 |
All models output stereo audio at 44.1 kHz.
Output quality is identical — max sample difference is 3.1e-5 (one int16 LSB), correlation > 0.999999999. MLX on Apple Silicon upcasts float16 weights to float32 for computation, so the math is the same.
| Metric | float32 (iky1e/demucs-mlx) | float16 (this repo) |
|---|---|---|
| htdemucs file size | 160 MB | 80 MB |
| htdemucs RSS (peak memory) | 1311 MB | 1210 MB |
| htdemucs speed (M1 Pro) | 7.1s | 7.9s |
| Output quality | reference | identical |
Each model consists of two files at the repo root:
{model_name}.safetensors — model weights (float16){model_name}_config.json — model class, architecture config, and bag-of-models metadataPoint the model directory or repo to this float16 variant:
# Use float16 models from local directory
demucs-mlx-swift -n htdemucs --model-dir /path/to/demucs-mlx-fp16 song.wav
# Or set the HF repo environment variable
export DEMUCS_MLX_SWIFT_MODEL_REPO=iky1e/demucs-mlx-fp16
demucs-mlx-swift -n htdemucs 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"))
To reproduce the export directly from PyTorch Demucs checkpoints:
pip install demucs safetensors numpy
# Export all 8 models as float16 (default)
python export_from_pytorch.py --out-dir ./output
# Export as float32
python export_from_pytorch.py --out-dir ./output --dtype float32
The conversion script (export_from_pytorch.py) is available in the demucs-mlx-swift repo under scripts/.
@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}
}
htdemucs_ft.safetensors
safetensors · 321 MB · SHA-256 6eb655cf7290…5a89 · Hugging Face
--- license: mit library_name: mlx tags: - mlx - audio - music-source-separation - source-separation - demucs - htdemucs - hdemucs - apple-silicon - float16 base_model: adefossez/demucs pipeline_tag: audio-to-audio --- # Demucs — MLX (float16) Float16 MLX-compatible weights for all 8 pretrained [Demucs](https://github.com/adefossez/demucs) models, converted to `safetensors` format for inference on Apple Silicon. This is the **float16 variant** of [iky1e/demucs-mlx](https://huggingface.co/iky1e/demucs-mlx) — same models, half the file size, identical output quality. Recommended for Apple Silicon where memory is constrained (iOS, smaller Macs). 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 (fp16) | Weights (fp32) | |-------|-----------|-------------|-----------|---------|----------------|----------------| | `htdemucs` | Default v4 model, best speed/quality balance | HTDemucs (v4) | 1 | 4 | 80 MB | 160 MB | | `htdemucs_ft` | Fine-tuned v4, best overall quality | HTDemucs (v4) | 4 (fine-tuned) | 4 | 321 MB | 641 MB | | `htdemucs_6s` | 6-source v4 (adds guitar + piano stems) | HTDemucs (v4) | 1 | 6 | 52 MB | 105 MB | | `hdemucs_mmi` | v3 hybrid, trained on more data | HDemucs (v3) | 1 | 4 | 160 MB | 319 MB | | `mdx` | v3 bag-of-models ensemble | Demucs + HDemucs | 4 (bag) | 4 | 659 MB | 1.3 GB | | `mdx_extra` | v3 ensemble trained on extra data | HDemucs | 4 (bag) | 4 | 638 MB | 1.2 GB | | `mdx_q` | Quantized v3 ensemble (same quality, smaller) | Demucs + HDemucs | 4 (bag) | 4 | 659 MB | 1.3 GB | | `mdx_extra_q` | Quantized v3 extra ensemble | HDemucs | 4 (bag) | 4 | 638 MB | 1.2 GB | All models output stereo audio at 44.1 kHz. ## Float16 vs Float32 Output quality is **identical** — max sample difference is 3.1e-5 (one int16 LSB), correlation > 0.999999999. MLX on Apple Silicon upcasts float16 weights to float32 for computation, so the math is the same. | Metric | float32 ([iky1e/demucs-mlx](https://huggingface.co/iky1e/demucs-mlx)) | float16 (this repo) | |--------|---------|---------| | htdemucs file size | 160 MB | **80 MB** | | htdemucs RSS (peak memory) | 1311 MB | **1210 MB** | | htdemucs speed (M1 Pro) | 7.1s | 7.9s | | Output quality | reference | iden...
Source context: 0 downloads · 0 likes · Pipeline audio-to-audio · Library mlx · Repo iky1e/demucs-mlx-fp16
| 160 MB |
| 319 MB |
mdx | v3 bag-of-models ensemble | Demucs + HDemucs | 4 (bag) | 4 | 659 MB | 1.3 GB |
mdx_extra | v3 ensemble trained on extra data | HDemucs | 4 (bag) | 4 | 638 MB | 1.2 GB |
mdx_q | Quantized v3 ensemble (same quality, smaller) | Demucs + HDemucs | 4 (bag) | 4 | 659 MB | 1.3 GB |
mdx_extra_q | Quantized v3 extra ensemble | HDemucs | 4 (bag) | 4 | 638 MB | 1.2 GB |