| --- |
| license: cc-by-nc-4.0 |
| base_model: m-a-p/SheetSage2 |
| tags: [coreml, music-transcription] |
| --- |
| |
| # SheetSage2 on Core ML |
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| [SheetSage2](https://huggingface.co/m-a-p/SheetSage2) (revision `398b22834dac7dd05e09b9c4e40a39fc479ec502`) and its parent encoder |
| [MERT-v2-FullSong](https://huggingface.co/m-a-p/MERT-v2-FullSong), converted to Core ML for |
| [slurper](https://github.com/TrevorS/slurper) by `scripts/convert_sheetsage2.py`. The weights keep their |
| CC BY-NC 4.0 license: non-commercial use only, with attribution to the SheetSage2 and MERT-v2 authors. |
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| - `encoder_fp32.mlpackage`: `samples[1,7200000]` (300 s of 24 kHz mono, zero padded) `-> cross[12,8,7500,64]`, |
| the log-mel frontend, MERT-v2 with SheetSage2's adapters merged, the layer mix and projection, and each |
| decoder layer's cross-attention keys (2i) and values (2i + 1). float32. |
| - `decoder_fp16.mlpackage`: one step of the 6-layer BART decoder, `token[1,1] + position[1] + mask[1,1,1,L] |
| -> logits[1,31678]`, with self- and cross-attention caches as states. `mask` is zeros of length position + 1. |
| - `golden_audio.f32`, `golden_tokens.json`: 30 s of "Swansong" by Josh Woodward (CC BY 4.0) at 24 kHz mono, and |
| the tokens PyTorch decodes from it, which these models reproduce exactly. |
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| Please cite the SheetSage2 technical report (Jiang et al., 2026) and MERT (Li et al., ICLR 2024). |
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