| name | audio-to-midi |
| description | Convert MP3/WAV/FLAC audio files to MIDI (.mid) and MusicXML (.musicxml) with full music analysis. Two engines: Basic Pitch (general-purpose polyphonic) and Piano Model (high-accuracy piano, 96.7% F1). Optional Demucs stem separation. Use --engine piano for piano/keyboard music for best results. Trigger when the user wants to: transcribe audio to MIDI, convert music to sheet music/notes, extract notes from audio, get MusicXML from a recording, analyze tempo/key/chords of a song, separate stems (vocals/drums/bass) and transcribe each, or any audio-to-notation task. Also triggers for requests like "convert this MP3 to MIDI", "get the notes from this song", "what key is this song in", "transcribe this audio", "separate and transcribe stems".
|
Audio-to-MIDI Transcription
Convert audio files to MIDI and MusicXML with full music analysis (tempo, key, chords, dynamics, instruments).
Tool
Script: scripts/transcribe.py -- wraps Basic Pitch, Demucs, librosa, and music21. Auto-installs dependencies.
Workflow
- Identify the input audio file (MP3, WAV, FLAC, OGG, M4A)
- Determine options:
- Stems? Add
--stems to separate vocals/drums/bass/other with Demucs first
- Output dir? Use
-o path or default to input file's directory
- Skip analysis? Add
--no-analysis if only MIDI/MusicXML needed
- Skip MusicXML? Add
--no-musicxml if only MIDI needed
- Run the transcription script
- Report output files and analysis summary to user
Engines
| Engine | Flag | Best for | Accuracy |
|---|
| Basic Pitch | --engine basic-pitch (default) | Mixed/polyphonic music | Good |
| Piano Model | --engine piano | Piano/keyboard music | 96.7% F1 |
For piano or keyboard music, always use --engine piano — it captures sustain/pedal, has far fewer ghost notes, and produces much more accurate MIDI.
Usage
py -3.12 scripts/transcribe.py "piano.mp3" --engine piano
py -3.12 scripts/transcribe.py "song.mp3"
py -3.12 scripts/transcribe.py "song.wav" --stems
py -3.12 scripts/transcribe.py "song.mp3" -o ./output --engine piano
py -3.12 scripts/transcribe.py "song.mp3" --onset-threshold 0.6 --frame-threshold 0.4
py -3.12 scripts/transcribe.py "song.mp3" --no-musicxml
py -3.12 scripts/transcribe.py "song.mp3" --no-analysis
Output Files
For input song.mp3:
song.mid -- MIDI file (for DAWs, notation software)song.musicxml -- MusicXML (for MuseScore, Finale, Sibelius, Dorico)song_analysis.json -- Full analysis (tempo, key, chords, dynamics, spectral)
With --stems, each stem produces its own MIDI + MusicXML:
vocals.mid, vocals.musicxmldrums.mid, drums.musicxmlbass.mid, bass.musicxmlother.mid, other.musicxml
Analysis Output
The _analysis.json contains:
- tempo_bpm: Detected BPM
- key: Detected key and mode (e.g. "A minor")
- key_confidence: 0-1 confidence score
- chords: Time-stamped chord progression
- unique_chords: Deduplicated chord list
- dynamics: Mean/max/min dB, dynamic range
- spectral: Centroid, bandwidth, rolloff, ZCR
- instrument_hints: Detected instrument categories
Tuning Parameters
| Flag | Default | Effect |
|---|
--onset-threshold | 0.5 | Higher = fewer ghost notes, may miss quiet notes |
--frame-threshold | 0.3 | Higher = stricter note detection |
--min-note-length | 58 | Minimum note duration in ms |
For clean recordings (piano, guitar): defaults work well.
For complex mixes: use --stems for best results.
For percussive music: lower onset threshold to 0.3-0.4.
Dependencies
Auto-installed on first run: basic-pitch, librosa, music21, pretty_midi, numpy, onnxruntime.
With --stems: also installs demucs (includes PyTorch).
Requires: Python 3.12 (py -3.12), ffmpeg (for MP3 decoding).
Important: Use py -3.12 (not python) to run the script. Python 3.14 has compatibility issues with ML packages. The script auto-selects the ONNX backend for Basic Pitch (most compatible).
Supported Input Formats
MP3, WAV, FLAC, OGG, M4A, AAC, WMA
