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README.md

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+---
+license: cc-by-nc-4.0
+base_model: MuScriptor/muscriptor-small
+tags:
+  - music
+  - transcription
+  - audio-to-midi
+  - onnx
+library_name: onnx
+---
+
+# MuScriptor small — ONNX conversion
+
+An ONNX Runtime conversion of [`MuScriptor/muscriptor-small`](https://huggingface.co/MuScriptor/muscriptor-small),
+the 103M-parameter multi-instrument music transcription model by **Kyutai ×
+Mirelo × IRCAM**. The weights are theirs and unmodified apart from the
+conversion; all credit for the model belongs to them.
+
+- Original model and paper: <https://github.com/muscriptor/muscriptor>, [arXiv:2607.08168](https://arxiv.org/abs/2607.08168)
+- **Licence: CC BY-NC 4.0** — non-commercial use only, inherited from the
+  original weights.
+
+It exists so that [Lepton Hum](https://midi.engineering/lepton-hum/), a
+desktop practice tool for musicians, can run the model through ONNX Runtime
+without shipping PyTorch. Anyone else running ONNX Runtime can use it the
+same way, under the same non-commercial terms.
+
+## Files
+
+| file | what it is |
+|---|---|
+| `muscriptor-small-cond.onnx` | conditioner: audio → prefix embeddings |
+| `muscriptor-small-lm.onnx` | the decoder-only transformer, KV cache in and out |
+
+### `muscriptor-small-cond.onnx`
+
+| | |
+|---|---|
+| `audio` | `f32 [1, 80000]` — one 5-second chunk at 16 kHz, zero-padded |
+| `instrument` | `i64 [1, K]` — instrument class ids (group + 2; `1` = unconditional) |
+| `dataset` | `i64 [1, 1]` — always `1`, the null class |
+| → `prefix` | `f32 [1, 501 + 1 + K, 768]` |
+
+The spectrogram is **inside this graph** on purpose. The checkpoint carries
+its own STFT window and mel filterbank as buffers, and they are stored
+quantized — about 2.4e-4 away from a freshly computed periodic Hann window
+and htk filterbank. That rounding raises the window's sidelobe floor by
+roughly 70 dB, and since the model reads log-magnitude features, bins that
+the textbook formulas put at the `log(1e-6)` floor sit near −4 for this
+model. Recomputing the front end therefore feeds it a spectrum it was not
+trained on, so the conversion keeps the checkpoint's own.
+
+### `muscriptor-small-lm.onnx`
+
+| | |
+|---|---|
+| `tokens` | `i64 [1, S]` |
+| `prefix` | `f32 [1, P, 768]` — the conditioner's output; `P = 0` after the first step |
+| `past_k`, `past_v` | `f32 [14, 1, T_past, 12, 64]` |
+| → `logits` | `f32 [1, 1393]` — last position only |
+| → `present_k`, `present_v` | the cache, grown by this step |
+
+Positions are not an input: everything in the cache precedes the call, so
+`T_past` is where the new tokens start. The graph is branch-free, so one
+exported model serves both the prefill and single-token decoding.
+
+Greedy decoding, MT3 token vocabulary (1393 tokens), 5-second chunks with a
+tie prologue carrying notes across chunk boundaries — see the original
+repository for the decoding rules.
+
+## Fidelity
+
+Checked against the reference PyTorch implementation on the same 3.7-minute
+mix, both given identical 16 kHz audio: **drums 1103 notes vs 1103 (exact),
+bass 522 vs 514, guitar 1730 vs 1845**. Greedy decoding turns the ~1e-5
+logit differences between ONNX Runtime and PyTorch into an occasional
+flipped argmax that then cascades, so agreement within a few percent per
+instrument is what parity looks like here.
+
+Layer-level checks from the conversion script: conditioner max|Δ| 7.7e-3
+(the STFT's numerics), prefill logits 2.7e-5, three decode steps ≤ 5.4e-5.
+
+## Reproducing
+
+`scripts/export_muscriptor_onnx.py` in the Lepton Hum repository. It fetches
+the original gated weights (accept the licence on the model page first),
+exports both graphs, and asserts parity against the reference modules before
+writing anything.

muscriptor-small-cond.onnx

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muscriptor-small-lm.onnx

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