README.md

metadata

title: MuScriptor Studio
emoji: 🎼
colorFrom: indigo
colorTo: purple
sdk: docker
app_port: 7860
pinned: false
license: mit
short_description: Audio → MIDI via muscriptor (Kyutai × Mirelo)

MuScriptor Studio

Drop in an audio file → get a MIDI transcription back, using muscriptor (a transformer-LM audio-to-MIDI model from Kyutai × Mirelo). For busy mixes you can isolate the vocals first with demucs — voice is decoded much better on its own.

What it does

1. Upload any mp3 / wav / m4a / flac file.
2. Optionally tick Isolate vocals to run demucs htdemucs --two-stems vocals first (saves the voice stem, feeds that to muscriptor).
3. Pick Model size: small / medium / large. Larger = better, slower.
4. Optionally constrain the instruments (comma-separated, e.g. voice,acoustic_piano,accordion,drums for Balkan folk).
5. Hit Transcribe → MIDI — the .mid file appears for download.

The MIDI has detected tempo + time signature burned into the file when best-effort detection succeeds.

How it's built

• Python 3.11 slim base, ffmpeg + libsndfile + git
• requirements.txt installs muscriptor from its GitHub repo
• app.py is a Gradio Blocks UI that calls TranscriptionModel.load_model(...).transcribe_and_postprocess(...)
• Model weights cache at /data/hf (persistent HF cache volume)

Caveats

• First request downloads the chosen model's weights — a few hundred MB per size. They cache to /data across restarts.
• CPU is fine for small; medium/large benefit from a GPU Space.
• Beat/tempo detection is best-effort — tracks without a steady pulse get a 120 BPM placeholder.

License

• App: MIT
• muscriptor: MIT
• demucs: MIT