Automatic Music Transcription (AMT) converts an audio recording into symbolic notes, usually MIDI. Single-instrument transcription already works reasonably well. However, transcribing a full multi-instrument mix stays difficult. Kyutai and Mirelo team now release MuScriptor to close that gap. It is an open-weight model trained on real, multi-instrument recordings across many genres.
This article explains how MuScriptor works, what the benchmarks show, and how to run it.
What is MuScriptor?
At its core, MuScriptor is a decoder-only Transformer for music transcription. First, it reads a mel-spectrogram of a short audio segment. Then it autoregressively predicts MIDI-like tokens for pitch, timing, and instrument. In effect, transcription becomes a language-modeling task, following the MT3 tokenization scheme.
The release ships three weight variants on Hugging Face. Their sizes are small (103M), medium (307M, default), and large (1.4B). The inference code uses the MIT license. The weights use CC BY-NC 4.0, so commercial use is restricted.
How the Three-Stage Pipeline Works
MuScriptor’s main idea is data, not architecture. Accordingly, training moves through three stages, and each builds on the last.
Pre-training uses D<sub>Synth</sub>, roughly 1.45M MIDI files. An on-the-fly pipeline synthesizes them during training. Augmentations include pitch shifting, tempo changes, velocity adjustment, and instrument randomization. Over 250 soundfonts plus random detuning yield near-infinite audio realizations.
Fine-tuning uses D<sub>Real</sub>, an internal set of 170,000 recordings. Together they total more than 11,000 hours with aligned note annotations. Most alignments come from audio-symbolic synchronization using interpolation and dynamic time warping. Poor pairs are filtered by warping distance and a maximum time-dilation factor.
Reinforcement learning post-training uses D<sub>RL</sub>, 300 manually verified tracks. The team applies a GRPO-like method combining REINFORCE with group-relative advantage normalization. The reward sums three F-scores: onset, frame, and offset. As a result, the model learns to favor cleaner transcriptions.