- name
- chord-analyzer
- description
- Analyze music audio files to extract chord progressions, key signature, tempo, and song structure. Use when user wants to identify chords, analyze a song's harmony, or extract musical information from audio files (mp3, wav, m4a, etc.).
- homepage
- https://github.com/librosa/librosa
- metadata
- { "openclaw": { "emoji": "🎸", "requires": { "bins": ["python3"], "pip": ["librosa", "numpy"] } } }
Chord Analyzer Skill
Analyze music audio files to extract chord progressions, key signature, tempo, and song structure.
When to Use
✅ USE this skill when:
- User wants to analyze a song's chords and harmony
- "What are the chords in this song?"
- "Analyze this audio file"
- "Extract the chord progression"
- "What key is this song in?"
- User provides an audio file path and asks for musical analysis
When NOT to Use
❌ DON'T use this skill when:
- Only wants general music info (lyrics, artist) → use web search
- Wants to generate music → use music generation skills
- Needs professional-grade transcription → recommend specialized software (Chordify, Hookpad)
- Requires detailed instrument separation → use dedicated source separation tools
Supported Formats
- Audio: mp3, wav, m4a, flac, ogg
- Duration: Works best for songs under 5 minutes
Installation
First time use requires installing dependencies:
pip3 install librosa numpy scipy scikit-learn soundfileUsage
Basic Analysis
# Analyze an audio file
python3 chord_analyzer.py
# Edit the script to change the audio path
# Default: /Users/chentiewen/Music/网易云音乐/example.mp3Script Integration
Copy the chord_analyzer.py script to your workspace and modify the audio_path variable:
audio_path = "/path/to/your/song.mp3"
result = analyze_audio(audio_path)Output
The analyzer provides:
- Key Signature: Detected musical key (e.g., C, F#m, G)
- Tempo: Speed in BPM with rhythm classification
- Chord Progression: Complete chord sequence with timestamps
- Chord Statistics: Most frequently used chords
- Song Structure: Intro/Verse/Outro segmentation (basic)
Sample Output
调性: F#m
速度: 123.0 BPM
节奏: 快板 (Allegro)
和弦走向:
F#mdim → A → D → Bm → E → A → D → Bm → E ...
主要和弦:
A: 15次 (20.3%)
E: 14次 (18.9%)
D: 12次 (16.2%)How It Works
- Load Audio: Uses
librosa.load()to read audio at 22.05kHz - Extract Chroma: Computes chroma features (pitch class profiles) using STFT
- Detect Key: Analyzes chroma energy across all 12 keys (major + minor)
- Track Tempo: Uses
librosa.beat.beat_track()for tempo detection - Analyze Chords: Samples chroma at measure boundaries and matches against chord templates
- Merge & Simplify: Combines consecutive identical chords
Limitations
- Accuracy: Chord detection is approximated; not professional-grade
- Complexity: Struggles with heavily layered or distorted music
- Structure: Simple segmentation (not verse/chorus detection)
- Melody: Does not extract melodic lines or instrument parts
- Chord Extensions: Detects basic triads (major, minor, diminished), not 7th/9th chords
For Complete Transcription
For professional music transcription, recommend:
- Chordify: https://chordify.net (online chord detection)
- Hookpad: https://www.hooktheory.com/hookpad (theory + chords)
- MuseScore: https://musescore.org (manual transcription)
- Capo: https://capoapp.com (slow down + chord detection)
Notes
- Analysis takes ~10-30 seconds depending on song length
- Best results with clear, non-distorted audio
- Works best for pop/rock/folk styles with clear harmony
- Not suitable for atonal, experimental, or heavily percussive music