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all-in-one-mlx

GPU-accelerated music structure analysis on Apple Silicon (MLX).

all-in-one-mlx is an Apple Silicon–optimized port of the original All‑In‑One Music Structure Analyzer (upstream: mir-aidj/all-in-one). It runs end‑to‑end inference locally using Apple MLX, with an integrated pipeline designed for real songs (including demixing + fast spectrograms).

Given one or more audio tracks, it produces:

  • Tempo (BPM)
  • Beat times
  • Downbeat times
  • Section boundaries
  • Section labels (intro / verse / chorus / bridge / outro, etc.)

Why this repo exists

The upstream project is a strong reference implementation, but macOS Apple Silicon users historically lacked a first‑class GPU‑accelerated inference path. This repository provides that acceleration via MLX, with an emphasis on:

  • High performance on M‑series GPUs
  • Practical CLI defaults for song inference (demix → spectrogram → model → outputs)
  • Faithful behavior to the upstream model + method

I’m releasing all-in-one-mlx alongside all-in-one-mps (PyTorch/MPS acceleration) so Apple Silicon users can choose the stack that fits their environment.


Performance

Benchmark on a single file — Apple M4 Max, 128 GB RAM, macOS 26.3:

Project Time vs upstream
mir-aidj/all-in-one 75.25s baseline
mir-aidj/all-in-one 24.63s ~3.1x faster (patched to use MPS)
all-in-one-mps 13.43s ~5.6x faster
all-in-one-mlx (this repo) 5.96s ~12.6x faster

One run, one file — results will vary by hardware and content.


Project Purpose
mir-aidj/all-in-one Original reference implementation and training code
all-in-one-mlx This repo: MLX inference + packaging for Apple Silicon
all-in-one-mps Companion repo: PyTorch/MPS inference for Apple Silicon

This repository began as a fork/port of the upstream project. The original method/model is described in:

  • Taejun Kim & Juhan Nam, All‑In‑One Metrical and Functional Structure Analysis with Neighborhood Attentions on Demixed Audio (arXiv:2307.16425)

If you use this in academic work, please cite the paper and the upstream repository.


Requirements

Component Requirement
Hardware Apple Silicon (M-series)
OS macOS 14+ (required by MLX wheels)
Python 3.10+

Need CUDA / Linux / Windows? Use the upstream project.


Installation

pip

pip install all-in-one-mlx
uv pip install all-in-one-mlx

Quickstart

Analyze one or more tracks:

allin1-mlx path/to/song.wav
# or multiple:
allin1-mlx path/to/a.wav path/to/b.wav

By default, results are written under:

  • ./struct (set with --out-dir)

Common options

  • Choose output directory:
allin1-mlx song.wav --out-dir ./struct
  • Save visualizations / sonifications:
allin1-mlx song.wav --visualize --viz-dir ./viz
allin1-mlx song.wav --sonify --sonif-dir ./sonif
  • Keep intermediate byproducts (demixed audio + spectrograms):
allin1-mlx song.wav --keep-byproducts
# demix files: ./demix (override with --demix-dir)
# specs:       ./spec  (override with --spec-dir)
  • Fast spectrogram backend (default) vs reference backend:
allin1-mlx song.wav --spec-backend mlx_fast   # default
allin1-mlx song.wav --spec-backend mlx        # reference path
  • Parity guard for mlx_fast (enabled by default):
allin1-mlx song.wav --spec-fast-guard
allin1-mlx song.wav --no-spec-fast-guard
allin1-mlx song.wav --spec-fast-guard-max-abs 1e-3 --spec-fast-guard-mean-abs 1e-4

When --spec-backend mlx_fast is used, the guard runs a one-time comparison against mlx. If the diff exceeds thresholds, the run automatically falls back to mlx for the remaining tracks.

  • One-time spectrogram correctness check (reports max/mean diff):
allin1-mlx song.wav --spec-check
  • Overwrite specific stages (demix,spec,json,viz,sonify) or everything:
allin1-mlx song.wav --overwrite all
allin1-mlx song.wav --overwrite demix,spec,json
  • Timing / performance instrumentation:
allin1-mlx song.wav --timings-path timings.jsonl
allin1-mlx song.wav --timings-path timings.jsonl --timings-viz-path timings.png

MLX inference controls

  • Select model (pretrained name):
allin1-mlx song.wav --model harmonix-all
  • Batch size:
allin1-mlx song.wav --mlx-batch-size 1
  • mx.compile for model forward (enabled by default):
allin1-mlx song.wav --no-mlx-compile
  • In-memory pipeline for demix + spectrograms (enabled by default):
allin1-mlx song.wav --no-mlx-in-memory
  • Ensemble inference parallelism (enabled by default):
allin1-mlx song.wav --no-ensemble-parallel
  • Disable multiprocessing (debug / determinism / constrained envs):
allin1-mlx song.wav --no-multiprocess

Outputs

The CLI writes analysis artifacts under --out-dir (default ./struct). Exact filenames may vary by model/pipeline version, but outputs include tempo, beats, downbeats, and segment boundaries/labels in machine-readable form.

Optional outputs:

Artifact Enable with
Visualizations --visualize and --viz-dir
Sonifications --sonify and --sonif-dir
Frame-level activations --activ and optionally --array-dir
Frame-level embeddings --embed and optionally --array-dir
JSONL timings --timings-path

Activation and embedding arrays are saved beside the JSON results by default. Use --array-dir to keep these much larger files in a separate directory:

allin1-mlx song.wav --activ --embed \
  --out-dir ./struct --array-dir ./arrays

Demix reproducibility

Demucs applies a random time shift before separation by default. Supply a seed to reproduce that shift selection across runs:

allin1-mlx song.wav --demix-seed 1234

The seed only applies when Demucs actually runs. External stems and reusable cached stems bypass it; use --overwrite demix,spec,json when replacing a disk-backed cached analysis with a seeded run. Seeding Demucs does not guarantee bitwise-stable end-to-end results because later MLX inference can still vary.

Model weights

Item Behavior
Source The optional mlx-weights integration is preferred when installed; otherwise verified checkpoints are downloaded on demand
Packaging Release wheels/sdists do not bundle model weights
Standalone cache ~/.cache/all-in-one-mlx/weights
Custom paths Use --mlx-weights-dir or explicit --mlx-weights-path and --mlx-config-path

Standalone downloads are pinned to an immutable source revision and checked against the published SHA-256 digest before use. Cached files are rechecked, and a damaged entry is discarded and downloaded again. Install mlx-weights separately if you want to use its shared conversion cache instead.

Known limitations

  • Artifact naming uses input basename/stem for intermediate and output files.
  • If multiple inputs share the same basename (for example a/song.mp3 and b/song.wav), artifacts may overwrite each other or be reused unexpectedly.
  • Workaround: process those files separately or rename files so basenames are unique.

Beat parity workflow

Use the reproducible parity harness to compare upstream CPU, all-in-one-mps, and all-in-one-mlx:

python scripts/compare_beat_parity.py \
  "/path/to/song.wav" \
  --upstream-python /path/to/all-in-one/.venv/bin/python \
  --mps-python /path/to/all-in-one-mps/.venv/bin/python \
  --mlx-python /path/to/all-in-one-mlx/.venv/bin/python \
  --mlx-weights-dir ~/.cache/all-in-one-mlx/weights \
  --output /tmp/allin1_beat_parity.json

The report includes beat/downbeat counts and timing delta summaries (median/mean signed, p90/max abs).


License

This project retains the upstream license (MIT). See LICENSE.


Issues

Please include:

Include Example
macOS version + Apple Silicon model macOS 26.3, M4 Max
Python + MLX versions Python 3.12.7, mlx x.y
Exact command and logs/traceback Full allin1-mlx ... command + stack trace

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