Cocktail-Fork MRX (MLX)
Collection
MERL MRX ported to Apple MLX — 3-stem music/speech/sfx soundtrack separation. Numerically exact vs PyTorch. 4 variants. • 4 items • Updated
Cocktail-Fork-MRX — paper variant (MLX)
Apple MLX port of MERL's MRX (Multi-Resolution CrossNet) — separates a soundtrack mixture into music, speech, and sound effects (sfx).
This variant uses the paper_ checkpoint: trained with the scale-invariant
SNR loss, reproducing the results from the ICASSP 2022 paper. Use this for
benchmark comparability with the original publication.
Other variants: Cocktail-Fork-MRX (default, SNR loss) ·
-adapted-loudness ·
-adapted-eq (cinematic-tuned).
- Upstream: merlresearch/cocktail-fork-separation (MIT) · The Cocktail Fork Problem, ICASSP 2022.
- Parity: numerically exact vs PyTorch (full-forward max_abs ≈
2e-7).
Usage
pip install git+https://github.com/xocialize/cocktail-fork-mlx
cocktail-fork-mlx --audio-path soundtrack.wav --out-dir ./out \
--weights mlx-community/Cocktail-Fork-MRX-paper
~30.6M params, fp32 (122 MB), 44.1 kHz. MIT, © MERL for the original model/weights.
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Model size
30.6M params
Tensor type
F32
·
Hardware compatibility
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