audio-to-midi

사용 방법

설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.

npx skills add https://github.com/maystudios/claude-skills --skill audio-to-midi

시작 전 확인

설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.

원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

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

bash

# Piano music (recommended for piano/keyboard)
py -3.12 scripts/transcribe.py "piano.mp3" --engine piano

# General music (default engine: Basic Pitch)
py -3.12 scripts/transcribe.py "song.mp3"

# With stem separation (Demucs): each stem gets its own MIDI + MusicXML
py -3.12 scripts/transcribe.py "song.wav" --stems

# Custom output directory
py -3.12 scripts/transcribe.py "song.mp3" -o ./output --engine piano

# Tuning Basic Pitch sensitivity
py -3.12 scripts/transcribe.py "song.mp3" --onset-threshold 0.6 --frame-threshold 0.4

# MIDI only (skip MusicXML)
py -3.12 scripts/transcribe.py "song.mp3" --no-musicxml

# MIDI + MusicXML without analysis
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.musicxml
drums.mid, drums.musicxml
bass.mid, bass.musicxml
other.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