MuScriptor: An Open Model for Multi-Instrument Music Transcription
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| Authors | Simon Rouard et al. |
| Year | 2026 |
| HF Upvotes | 20 |
| arXiv | 2607.08168 |
| Download | |
| HF Page | View on Hugging Face |
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.
Engineering Breakdown
The Problem
Existing methods for automatic music transcription are often limited to single-instrument recordings or fail on complex, real music mixes.
The Approach
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.
Key Results
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.
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Machine learning
- Deep learning
- Neural networks
- Model optimization
- AI systems
- Muscriptor
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