demucs-mlx

Split any song into its individual stems — vocals, drums, bass, and other instruments — directly on your Mac.

demucs-mlx is a fast, native Apple Silicon port of Meta's Demucs music source separation model, built on MLX. No PyTorch required.

Features

• ~73x realtime on Apple Silicon — 2.6x faster than Demucs with PyTorch MPS
• Bit-exact parity with upstream Demucs stems (within floating-point tolerance)
• Custom fused Metal kernels (GroupNorm+GELU, GroupNorm+GLU, OLA)
• Metal-free fallbacks for non-Apple platforms (Linux)
• No PyTorch required at inference time
• Automatic resampling — input files at any sample rate are resampled to the model rate
• Audio I/O via mlx-audio-io
• STFT/iSTFT via mlx-spectro

Requirements

• Python >= 3.10
• macOS with Apple Silicon (recommended) or Linux with MLX
• MLX 0.31.2 paired with mlx-audio-io 1.3.11; the native audio package does not yet support MLX 0.32

Install

pip install demucs-mlx

On first run, demucs-mlx loads cached MLX weights if available. If the optional mlx-weights package is installed locally, demucs-mlx uses its shared cache. Otherwise it uses its built-in cache and conversion fallback.

To bootstrap a missing model with the public package, install the conversion extra:

pip install 'demucs-mlx[convert]'

If you are developing with mlx-weights installed, you can also pre-convert through its shared cache:

mlx-weights convert demucs/htdemucs

Once weights are cached, the convert extra is no longer needed for inference.

CLI usage

demucs-mlx /path/to/audio.wav

Options:

-n, --name          Model name (default: htdemucs)
-o, --out           Output directory (default: separated)
--shifts            Number of random shifts (default: 1)
--seed              Optional RNG seed for reproducible shifts (default: none)
--overlap           Overlap ratio (default: 0.25)
-b, --batch-size    Batch size (default: 2)
--write-workers     Concurrent writer threads (default: 1)
--list-models       List available models
-v, --verbose       Verbose logging

Python usage

from demucs_mlx import Separator

separator = Separator()
origin, stems = separator.separate_audio_file("song.wav")

# stems is a dict: {"drums": array, "bass": array, "other": array, "vocals": array}
for name, audio in stems.items():
    print(f"{name}: {audio.shape}")

To keep outputs as MLX arrays (avoids GPU-to-CPU copy):

origin, stems = separator.separate_audio_file("song.wav", return_mx=True)

For reproducible shift sampling (while keeping shifts=1 behavior), pass a seed:

separator = Separator(model="htdemucs", shifts=1, seed=0)
origin, stems = separator.separate_audio_file("song.wav")

What changed in 1.4.5

• Fixed audio prefetch on MLX 0.31.2 by materializing decoded arrays on the producer thread before queue handoff.
• Reduced the default inference batch size from 8 to 2 to avoid memory thrashing on 16–36 GB Macs; explicit -b values are unchanged.
• Pinned MLX 0.31.2 and mlx-audio-io 1.3.11 as a compatible native runtime pair. MLX 0.32 support will follow a matching mlx-audio-io release.

What changed in 1.4.4

• Fixed multi-segment split=True overlap-add on MLX 0.31.2. Long inputs no longer produce high-amplitude reconstruction spikes.
• Added regression coverage for split-mode overlap-add and an optional model-level reproduction for issue #1.

What changed in 1.4.3

• resample_mx() now uses direct mac.resample() instead of writing/reading a temp file — eliminates an unnecessary MLX→numpy→disk→MLX round-trip.
• Bumped minimum mlx-audio-io to >=1.3.9 (auto-selects best resampling quality).

What changed in 1.4.2

• Audio loading now stays as native MLX arrays end-to-end (no numpy round-trip).
• Automatic resampling via mlx-audio-io — input files no longer need to match the model sample rate.
• Uses soxr_vhq resampling quality when available, with automatic fallback.
• Bumped minimum dependencies: mlx>=0.31.0, mlx-audio-io>=1.3.8, mlx-spectro>=0.2.4.

What changed in 1.4.0

• Fixed shifted-inference TensorChunk propagation so chunk length/offset is handled correctly in all paths.
• Added optional deterministic RNG control (seed) for Python API and CLI.
• Default behavior is unchanged: shifts=1 remains stochastic unless seed is provided.

Performance

Benchmarked on a 3:15 stereo track (44.1 kHz, 16-bit) using htdemucs with default settings:

Apple M4 Max, 128 GB. All runs use htdemucs with default settings and a single warm-up pass before timing.

Models

MLX model cache

Pre-converted MLX weights are cached under ~/.cache/demucs-mlx by default. When the optional mlx-weights package is installed, demucs-mlx uses its shared ~/.cache/mlx-weights/demucs-mlx cache instead. Delete the active cache directory to force re-conversion.

Documentation

• API reference: docs/api.md
• Development workflow: docs/development.md
• Platform notes: docs/platform.md

License

MIT. Based on Demucs by Meta Research. See LICENSE for details.