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stems-demucs

swift-vocal-isolation
music-source-separation
stems
demucs
core-ai
aimodel
apple-silicon
macos
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metadata 
license: mit
tags:
  - music-source-separation
  - stems
  - demucs
  - core-ai
  - aimodel
  - apple-silicon
  - macos
library_name: swift-vocal-isolation

Hybrid Transformer Demucs for Core AI (.aimodel)

Apple Core AI conversion of HTDemucs (htdemucs) — Simon Rouard, Francisco Massa, Alexandre Défossez, Hybrid Transformers for Music Source Separation, ICASSP 2023 — from github.com/facebookresearch/demucs (MIT, 42M parameters). Four stems: drums, bass, other, vocals.

stems-htdemucs-float32.aimodel (168 MB) holds the network between the complex spectrogram and the mask at the model's 7.8-second training segment. Inputs: the normalised mix [1, 2, 343980], its complex-as-channels spectrogram [1, 4, 2048, 336] and the four per-segment statistics the model normalises by. Outputs: the spectral stems [1, 16, 2048, 336] and the time-branch stems [1, 8, 343980]. Core AI has no STFT and no variance op, so those run in the host — in swift-vocal-isolation (MIT), together with upstream's chunking, overlap-add weights, centred padding and global normalisation, line for line. Nothing was re-authored, retrained or pruned. The stems CLI exposes it as --engine demucs.

Faithfulness

Held to upstream's Python with shifts=0 (random shifts make upstream itself non-deterministic):

  • The exported network is asserted equal to model(mix) before export.
  • One training segment through Core AI on the GPU: 136–147 dB PSNR against upstream's output.
  • Every one of 63 chunks of a 6-minute mix: worst stem 110 dB.
  • Whole clips end to end (10 s and 6 min): 127–148 dB on every stem.
  • One measured wrinkle in the runtime, not the model: the GPU returned a slightly wrong segment about 2% of the time (77–105 dB), never the same one twice. A correct run is bit-for-bit repeatable, so the host runs every segment twice and settles a mismatch with a third run.

Use 

hf download arraypress/stems-demucs --local-dir models
stems model install models/stems-htdemucs-float32.aimodel
stems song.wav --engine demucs      # song-drums.wav, -bass, -other, -vocals

Requirements: macOS 27, Apple silicon. Reproduce with uv run Tools/export_demucs.py in the library repo (fetches the checkpoint through the demucs package). htdemucs_ft (a bag of four) and htdemucs_6s (adds guitar and piano) export the same way but are not verified here.

Licence and citation

MIT, as upstream. Please cite:

S. Rouard, F. Massa, A. Défossez. "Hybrid Transformers for Music Source Separation." ICASSP 2023.