| title | MuScriptor: An Open Model for Multi-Instrument Music Transcription |
|---|---|
| authors | Simon Rouard, Michael Krause, Axel Roebel, Carl-Johann Simon-Gabriel, Alexandre Défossez |
| date | 2026-07-09 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.08168 |
| affiliation | Kyutai |
| upvotes | 14 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Existing methods for automatic music transcription are often limited to single-instrument recordings or fail on complex, real music mixes. Although previous work utilizes synthetic training data, the resulting models generalize poorly, leading to largely unusable transcription output in realistic, multi-instrument settings. In this work, we analyze the effectiveness of synthetic data for pre-training while combining it with fine-tuning on real music audio and post-training using reinforcement learning. We further introduce conditioning on instrument presence to customize transcriptions. Finally, we release MuScriptor, an open-weight multi-instrument music transcription model that works on real-world music recordings from across a diverse range of musical genres. |
cite
@misc{rouard2026muscriptor,
title={MuScriptor: An Open Model for Multi-Instrument Music Transcription},
author={Simon Rouard and Michael Krause and Axel Roebel and Carl-Johann Simon-Gabriel and Alexandre Défossez},
year={2026},
eprint={2607.08168},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.08168}
}