Vendor Spotify Basic Pitch ICASSP 2022 ONNX for MAESTRO

README.md

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- music
+- midi
+- audio-to-midi
+- polyphonic-transcription
+- basic-pitch
+- onnx
+---
+
+# Basic Pitch (ONNX) Mirror
+
+Vendored copy of Spotify's [Basic Pitch](https://github.com/spotify/basic-pitch)
+ICASSP 2022 polyphonic transcription model in ONNX format, re-hosted for use
+in the [MAESTRO AI Workstation](https://github.com/AEmotionStudio).
+
+## What this model does
+
+**Audio → MIDI polyphonic transcription** for any pitched instrument:
+guitar, bass, vocals, synth, piano. Lightweight (~230 KB) and fast.
+
+## Why ONNX (not the pip package)?
+
+The official `basic-pitch` PyPI package depends on `tensorflow<2.15.1`,
+which has no Python 3.14 wheels and would conflict with the MAESTRO
+backend's torch installation. Spotify ships the same model as a small
+ONNX export, which we serve here and run via `onnxruntime` — same model,
+no TensorFlow dependency chain.
+
+## Architecture
+
+CNN spectrogram encoder + multi-head pitch/onset/note prediction. See the
+[ICASSP 2022 paper](https://arxiv.org/abs/2203.09893) for details.
+
+## License
+
+**Apache-2.0** — commercial-use OK.
+
+## Usage in MAESTRO
+
+Loaded by `backend/ai/models/basic_pitch.py` via `onnxruntime`; surfaced
+in the AI Workstation's `TranscribePanel` under General / Drums / Vocals
+mode tabs.
+
+## Citation
+
+```
+@inproceedings{2022_BittnerBRME_LightweightNoteTranscription_ICASSP,
+  title={A lightweight instrument-agnostic model for polyphonic note transcription and multipitch estimation},
+  author={Bittner, Rachel M. and Bosch, Juan Jos{\'e} and Rubinstein, David and Meseguer-Brocal, Gabriel and Ewert, Sebastian},
+  booktitle={ICASSP 2022},
+  year={2022}
+}
+```

nmp.onnx

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