ClawHub

Chord Analyzer

Other

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.).

Install

openclaw skills install chord-analyzer

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 soundfile

Usage

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.mp3

Script 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:

  1. Key Signature: Detected musical key (e.g., C, F#m, G)
  2. Tempo: Speed in BPM with rhythm classification
  3. Chord Progression: Complete chord sequence with timestamps
  4. Chord Statistics: Most frequently used chords
  5. 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

  1. Load Audio: Uses librosa.load() to read audio at 22.05kHz
  2. Extract Chroma: Computes chroma features (pitch class profiles) using STFT
  3. Detect Key: Analyzes chroma energy across all 12 keys (major + minor)
  4. Track Tempo: Uses librosa.beat.beat_track() for tempo detection
  5. Analyze Chords: Samples chroma at measure boundaries and matches against chord templates
  6. 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:

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