muscriptor-large

MuScriptor — large (≈1.3B) MuScriptor is an open-weight model for general-purpose, multi-instrument automatic music transcription (AMT): it converts a music recording (any genre, multiple simultaneous instruments) into a stream of notes played. This repository hosts the large variant (≈1.3B parameters) — the flagship, best-quality checkpoint. For a smaller footprint use muscriptor-medium (≈300M, good trade-off) or muscriptor-small (≈100M, fastest). Developed by Mirelo x kyutai 📄 Paper: MuScriptor: An Open Model for Multi-Instrument Music Transcription — Rouard, Krause, Roebel, Simon-Gabriel, Défossez (2026). 💻 Code: https://github.com/muscriptor/muscriptor 🔊 Audio samples: https://muscriptor.github.io Table of contents Quickstart Model description Model variants Intended uses & limitations Instrument conditioning Training Evaluation Citation License Quickstart Install the muscriptor package (it uses huggingface_hub to fetch weights automatically): pip install git+https://github.com/muscriptor/muscriptor.git TODO (PyPI release forthcoming: pip install muscriptor) Python from pathlib import Path from muscriptor import TranscriptionModel "large" resolves to hf://MuScriptor/muscriptor-large and downloads on first use. model = TranscriptionModel.load_model("large") Get a MIDI file directly: Path("out.mid").write_bytes(model.transcribe_to_midi("audio.wav")) Or stream note events as they are transcribed: for event in model.transcribe("audio.wav"): print(event) # NoteStartEvent / NoteEndEvent / ProgressEvent load_model accepts a size keyword ("small"/"medium"/"large"), a local .safetensors path, or an hf:// / https:// URL. Weights loaded by size keyword (or any hf:// URL) are cached in the standard Hugging Face cache (~/.cache/huggingface/hub, configurable via HF_HOME); weights fetched from a plain http(s):// URL are cached under ~/.cache/muscriptor/. Input audio can be WAV or any format libsndfile reads (mp3, flac, ogg, m4a, …); it is resampled to 16 kHz mono internally. C