mlx-audio-separator 0.1.1

MLX-native audio stem separation for Apple Silicon

mlx-audio-separator

MLX-native stem separation for Apple Silicon Macs.

This project ports the inference paths from python-audio-separator to MLX so separation runs on Apple Silicon without requiring PyTorch or ONNX Runtime at inference time. Core runtime components are powered by mlx-audio-io (audio I/O) and mlx-spectro (spectral transforms).

Requirements

• macOS 13+ (Ventura or later)
• Apple Silicon (M1/M2/M3/M4)
• Python 3.10+

Installation

pip install mlx-audio-separator

If you need first-run conversion from upstream checkpoints (.ckpt/.onnx/Demucs weights), install conversion extras:

pip install "mlx-audio-separator[convert]"

Quick Start

CLI

# Separate with default model
mlx-audio-separator song.mp3

# Use a specific model
mlx-audio-separator song.mp3 -m htdemucs_ft.yaml

# List supported models
mlx-audio-separator --list_models

Python

from mlx_audio_separator import Separator

sep = Separator()
sep.load_model()
outputs = sep.separate("song.mp3")
print(outputs)

Supported Architectures

• Roformer (BS-Roformer and MelBand-Roformer families)
• MDXC (including MDX23C-style checkpoints)
• MDX
• VR
• Demucs

Validation Snapshot

Release validation snapshot (2026-02-24 to 2026-02-26):

Check	Result
Full-catalog benchmark gate	163/163 models ok (0 failures)
Unit tests	167 passed, 1 skipped
MLX vs audio-separator parity smoke	4/4 models passed (rel L2 <= 5e-2)

Scope: Apple Silicon (M4 mini), MUSDB18-HQ test subset, release gate + parity smoke model set.

Detailed evidence and provenance: docs/release-validation.md.

Performance Snapshot

MLX vs audio-separator (ABBA, 12-song MUSDB18-HQ test subset, M4 mini):

Model	MLX speedup vs PAS
htdemucs_ft.yaml	1.40x
model_bs_roformer_ep_317_sdr_12.9755.ckpt	2.16x
mel_band_roformer_instrumental_instv7n_gabox.ckpt	2.50x
UVR-MDX-NET-Inst_HQ_3.onnx	1.53x

Median speedup across the 4-model overlap set: 1.847x.

These numbers are scoped to the benchmark settings above and are not universal guarantees for all machines, models, or audio inputs.

Stable Runtime Tuning

Release-facing stable controls:

• --speed_mode {default,latency_safe,latency_safe_v2}
• --cache_clear_policy {aggressive,deferred}
• --write_workers <int>

Example:

mlx-audio-separator song.mp3 \
  --speed_mode latency_safe \
  --cache_clear_policy deferred \
  --write_workers 2

Basic benchmark command:

mlx-audio-separator \
  --benchmark song.mp3 \
  --benchmark_warmup 1 \
  --benchmark_repeats 3 \
  --benchmark_profile

Documentation

Document	Description
docs/release-validation.md	Release evidence snapshot
docs/release-first.md	Release execution playbook
docs/reproducibility.md	Reproducibility guide
docs/wave4-opt-in.md	Wave 4 opt-in/experimental roadmap
CHANGELOG.md	Changelog
THIRD_PARTY_NOTICES.md	Third-party attribution and license notices

License

This project is MIT licensed.

Acknowledgments

mlx-audio-separator is derived from python-audio-separator (MIT) by beveradb and the nomadkaraoke community. Substantial portions of the architecture, model loading, and separation logic are adapted from that project. If you find this package useful, please also star and support the upstream project.

The models used by this project were trained by the Ultimate Vocal Remover community, primarily @Anjok07 and @aufr33. See THIRD_PARTY_NOTICES.md for full attribution and license details.

Additional references:

• mlx-audio-io
• mlx-spectro
• BS-Roformer
• Demucs (Meta Research)
• Ultimate Vocal Remover
• python-audio-separator