music3lab

Music3Lab

Open research toolkit extending MiniMax-Music3 with arbitrary-audio encoding, continuation, inpainting, prepend generation, prompt-free generation, and automated objective evaluation.

Music3Lab is a reproducible, evidence-first toolkit built around the released MiniMax-Music3 weights. It does not modify or redistribute those weights. Every capability below was gated on preregistered objective metrics, and the failures are published alongside the successes — they are the more useful part of the research.

Honesty note. This is a research toolkit, not a finished product. Several headline goals (native WAV→token encoding, true arbitrary-audio history-aware continuation/inpainting/prepend, direct long-form reference conditioning) were attempted and did not pass their gates. Those negative results, their code, configs, and exact metrics are all here on purpose.

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What it does

music3lab status --json is the authoritative machine-readable capability matrix. Full write-ups are in reports/ and reports/FINAL_RESULTS.md.

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What it is NOT

• ❌ It does not include MiniMax-Music3 weights. You download those yourself.
• ❌ It does not redistribute any audio — not LAION/YouTube audio, not the author's own songs. Only dataset metadata (IDs, hashes, splits) is included.
• ❌ It is not a native audio tokenizer for Music3. That does not exist in the public release (see FINDINGS.md).

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Quickstart

# 1. Environment (Python 3.10; exact pins in requirements.lock)
python -m venv .venv && . .venv/bin/activate      # Windows: .venv\Scripts\activate
pip install -e .                                  # core (CPU inspection/eval)
pip install -e ".[capture]"                       # + diffusers/transformers for generation (GPU)

# 2. Get the base model yourself (NOT bundled). See REPRODUCING.md.
hf download MiniMaxAI/MiniMax-Music3 --local-dir ./models/minimax-music3

# 3. Inspect the released checkpoint (CPU, no GPU, no weights modified)
python inspect_dav.py ./models/minimax-music3/dav.pth --json --sha256

# 4. Machine-readable capability matrix
music3lab status --json

Full setup, model download, one-command demo, and benchmark commands: REPRODUCING.md.

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Repository layout

music3lab/
├── src/music3lab/        # the installable package (tested; layout preserved)
│   ├── codec/            # encoders, external fine-tune, native-state experiments
│   ├── editing/          # continuation / inpaint / prepend / append
│   ├── autonomous/       # champion/challenger promotion + rollback
│   ├── inversion*.py     # latent inversion (research mode)
│   ├── eval.py           # objective evaluation
│   └── ...               # baseline capture, checkpoint audit, pipeline, release
├── configs/              # frozen experiment/training configs (34)
├── scripts/              # runnable training / experiment / benchmark scripts (38)
│   └── data/             # LAION downloader (laion_ingest.py, laion_freeze_interim.py)
├── tests/                # focused + adversarial suites (58)
├── reports/              # per-capability write-ups + FINAL_RESULTS.md
├── data/laion_disco/     # dataset METADATA only (IDs, hashes, splits) — no audio
├── checkpoints/          # POINTERS to trained adapters (no weights) — see README there
├── examples/             # how to reproduce demo outputs (no bundled audio)
├── requirements.lock     # exact pinned environment
├── LICENSE  NOTICE  THIRD_PARTY.md  MODEL_CARD.md  DATA.md  TRAINING.md  REPRODUCING.md

Training. Every trainable component ships its training script + config + tests. See TRAINING.md for the full table and how to push the open problems (native tokenization, arbitrary-audio editing).

Note on structure. The conceptual grouping (encoder / continuation / inpaint / prepend / eval) is preserved thematically via the codec/ and editing/ subpackages and this map, rather than by physically splitting src/ — that keeps the 48-test suite green and the package importable for a reproducible v0.1.0. A physical refactor into top-level encoder/continuation/... packages is a possible later, separately-tested change.

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The core finding

The released dav.pth is a continuous DAV analysis encoder + Gaussian posterior heads + flow model + waveform decoder. It contains no RVQ/VQ quantizer, no acoustic codebooks, and no generator/62000_generator tensors. The string 62000_generator is only the root folder name inside the PyTorch ZIP archive — not a model component. Music3's eight-stream token space therefore cannot be produced from an arbitrary WAV with the released weights. Everything Music3Lab does works either in the continuous Flow-latent space or from captured generation state. Details and reproduction: FINDINGS.md.

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Trained checkpoints & data

• Adapters/encoders are released separately (Hugging Face) — see checkpoints/README.md and MODEL_CARD.md. ⚠️ They are derivatives of MiniMax-Music3 and may be governed by its license; confirm before redistributing.
• Dataset: only LAION-DISCO-12M metadata + a downloader are shipped. No audio. See DATA.md.

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License

Original Music3Lab code: Apache-2.0 (LICENSE, NOTICE). Third-party components and their separate licenses: THIRD_PARTY.md.