mlx-audio-separator

MLX-native stem separation for Apple Silicon Macs.

This project ports the inference paths from audio-separator (upstream repo: nomadkaraoke/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

# Install and use ZFTurbo Mel-Band-RoFormer Vocals v1
mlx-audio-separator song.mp3 -m mel-roformer-zfturbo-vocals-v1-mlx

# 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

ZFTurbo Vocals v1 downloads preconverted MLX weights from Hugging Face through the standard model catalog. It produces a vocal stem and derives the instrumental stem by subtracting vocals from the mixture. It runs on the built-in MelBand-RoFormer implementation. Its original checkpoint is model_vocals_mel_band_roformer_sdr_8.42.ckpt.

Demucs cache security and migration

Demucs conversion stores MLX weights as <model>.safetensors with a validated <model>_config.json sidecar. The loader verifies that the two files match before constructing a model. Legacy <model>_mlx.pkl caches are never deserialized. When automatic conversion is enabled they are left untouched and replaced by newly generated safe files from the official Demucs source.

To regenerate a cache explicitly:

pip install "mlx-audio-separator[convert]"
python -m mlx_audio_separator.demucs_mlx.mlx_convert htdemucs \
  --output-dir ~/.cache/mlx-audio-separator/demucs

Converted Demucs weights live in ~/.cache/mlx-audio-separator/demucs. MLX_AUDIO_SEPARATOR_DEMUCS_CACHE_DIR points that elsewhere. Caches left in the older ~/.cache/demucs-mlx by a release before 0.1.12 are still read, so upgrading does not cost a reconversion; nothing is written there any more, because the demucs-mlx package uses that directory for its own differently shaped cache.

Official Demucs downloads retain their filename hash checks and are loaded with PyTorch's restricted weight-only deserializer. This trusts the installed PyTorch, Demucs, NumPy, and optional DiffQ implementations plus the official model registry; arbitrary checkpoint globals and local pickle caches are not trusted.

Validation Snapshot

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

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):

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.

The embedded Demucs implementation also includes faster GroupNorm and phased decoders. A paired, loaded-model comparison measured 10–11% less time for two-shift htdemucs inference (the default before 0.1.19; it is now one shift, as upstream) on 30- and 60-second synthetic inputs; see Demucs tuning and measurements. For the fine-tuned bag, ask for one stem to run only its specialist model:

mlx-audio-separator song.mp3 -m htdemucs_ft.yaml --single_stem bass

Stable Runtime Tuning

The defaults are the recommended configuration. Every inference option ships at the value that measured best for both quality and speed, so you should not need to set anything to get good results. See docs/tuning.md for what those values are, the measurements behind them, and the levers that remain for benchmarking or exact-parity work.

Release-facing stable controls:

• --cache_clear_policy {aggressive,deferred} — defaults to deferred
• --write_workers <int> — defaults to 4

Both already ship at the value that measured fastest, so the example that used to live here (passing them explicitly) is no longer needed.

--speed_mode is deprecated and ignored. Its latency_safe* profiles now resolve to the defaults, and it will be removed in the next major version.

Basic benchmark command:

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

BS-Roformer-SW Performance (Opt-In)

For BS-Roformer-SW.ckpt, no extra flags are needed:

mlx-audio-separator song.mp3 \
  -m BS-Roformer-SW.ckpt \
  --output_format FLAC

What --speed_mode latency_safe_v3 used to enable -- deferred cache clearing plus async stem writes -- is now the default. Measured on a 195 s track to FLAC it runs ~6-17% faster than the old defaults with bit-identical output.

To avoid repeated checkpoint conversion overhead, pre-convert once to *.safetensors and exit:

mlx-audio-separator \
  -m BS-Roformer-SW.ckpt \
  --save_converted_safetensors \
  --preconvert_only

safetensors primarily improves model load/startup time. It is not expected to materially change per-file inference latency.

Validation command (latency + deterministic equivalence):

uv run --with torch python scripts/perf/compare_latency.py \
  --corpus-file /tmp/corpus_one.txt \
  --baseline-config scripts/perf/configs/bs_roformer_sw_default_baseline.json \
  --candidate-config scripts/perf/configs/bs_roformer_sw_latency_safe_v3_candidate.json \
  --model-file-dir ~/.cache/mlx-audio-separator/models \
  --allow-speed-mode-mismatch \
  --target-improvement-demucs-mdxc 10.0 \
  --equivalence-check \
  --equivalence-threshold-rel-l2 1e-6 \
  --output-json /tmp/bs_roformer_sw_latency_safe_v3_compare.json \
  --output-markdown /tmp/bs_roformer_sw_latency_safe_v3_compare.md

BS-Roformer-SW Optimization Program (Opt-In Tracks)

As of 0.1.8, what latency_safe_v3 promoted -- deferred cache clearing and async stem writes -- is the default for every model, and --speed_mode is deprecated and ignored. Measure candidates against the defaults. All experimental tracks below remain parked pending new evidence.

Candidate configs for staged exploration live under scripts/perf/configs/:

• bs_roformer_sw_cand_grouped_bandmask.json
• bs_roformer_sw_cand_fused_ola.json
• bs_roformer_sw_cand_stream_pipeline.json
• bs_roformer_sw_cand_compile_fullgraph.json
• bs_roformer_sw_cand_flac_fastwrite.json

Corpus manifest templates:

• Quick gate (3 files): scripts/perf/corpora/bs_roformer_sw_quick.txt
• Full gate (12 files): scripts/perf/corpora/bs_roformer_sw_full.txt

Quick-gate example:

uv run --with torch python scripts/perf/compare_latency.py \
  --corpus-file scripts/perf/corpora/bs_roformer_sw_quick.txt \
  --baseline-config scripts/perf/configs/bs_roformer_sw_latency_safe_v3_baseline.json \
  --candidate-config scripts/perf/configs/bs_roformer_sw_cand_grouped_bandmask.json \
  --model-file-dir ~/.cache/mlx-audio-separator/models \
  --target-improvement-demucs-mdxc 3.0 \
  --equivalence-check \
  --equivalence-threshold-rel-l2 1e-6 \
  --equivalence-max-files 1 \
  --output-json /tmp/bs_roformer_sw_quick_gate.json \
  --output-markdown /tmp/bs_roformer_sw_quick_gate.md

Full-gate example:

uv run --with torch python scripts/perf/compare_latency.py \
  --corpus-file scripts/perf/corpora/bs_roformer_sw_full.txt \
  --baseline-config scripts/perf/configs/bs_roformer_sw_latency_safe_v3_baseline.json \
  --candidate-config scripts/perf/configs/bs_roformer_sw_cand_grouped_bandmask.json \
  --model-file-dir ~/.cache/mlx-audio-separator/models \
  --target-improvement-demucs-mdxc 5.0 \
  --equivalence-check \
  --equivalence-threshold-rel-l2 1e-6 \
  --equivalence-max-files 0 \
  --output-json /tmp/bs_roformer_sw_full_gate.json \
  --output-markdown /tmp/bs_roformer_sw_full_gate.md

Troubleshooting

Symbol not found: __ZN3mlx4core... or MLX version mismatch: the mlx-audio-io native binary was built against ... when importing. Your mlx-audio-io is 1.3.22 or older, whose extension was compiled against one MLX version. From 1.3.23 it does not link MLX at all, so upgrading fixes it for every MLX from 0.32.0 on:

pip install -U "mlx-audio-io>=1.3.23"

Documentation

License

This project is MIT licensed.

Acknowledgments

mlx-audio-separator is derived from audio-separator (upstream repo: nomadkaraoke/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
• audio-separator / nomadkaraoke/python-audio-separator