Music Analysis

Analyze music/audio files locally without external APIs. Extract tempo, pocket/groove feel, pulse stability, swing proxy, section/repetition structure, key c...

Music Analysis (Local, No External APIs)

Primary tool: a full listen that combines snapshot analysis, structure, groove, harmonic tension, temporal mood mapping, and optional Whisper lyric alignment into one report.

1. Full Listen — primary / recommended

python3 skills/music-analysis/scripts/listen.py /path/to/audio.mp3
python3 skills/music-analysis/scripts/listen.py track.mp3 --json
python3 skills/music-analysis/scripts/listen.py track.mp3 --out report.txt
python3 skills/music-analysis/scripts/listen.py track.mp3 --json --out report.json

What it does in one pass:

1. Snapshot analysis: tempo, pulse stability, swing proxy, key clarity, harmonic tension, timbre, structure
2. Whisper lyric transcription and filtering first — keep only real lyric text, drop artifact tags like [MUSIC]
3. Temporal listen: windowed energy / mood / tension journey
4. Synthesis layer that aligns lyrics with peak / tension / quiet windows and lets the lyric layer override the final vibe when confidence is high

Human-readable output structure

• SNAPSHOT
  • groove/pocket
  • structure summary + repeated sections
  • harmony (key clarity + tension)
  • timbre descriptor tags
• INSTRUMENT READ
  • likely instrument palette (strong/likely/possible confidence)
  • per-section instrument entrances and exits
  • how instruments color the emotional feel
  • written as natural language, not clinical data
• TEMPORAL JOURNEY
  • opening / middle / closing mood-energy-tension read
  • peak / quietest / tensest moments
  • mood journey and transition count
• EMOTIONAL READ
  • explainable emotion summary based on measured features
• LYRICS
  • Whisper segment count
  • excerpt or graceful skip note
• SYNTHESIS
  • lyric-energy/tension alignment
  • peak / tension / quiet lyric moments
• ALIGNED TIMELINE
  • per-window moments where transitions / lyrics / tension spikes occur

2. Snapshot Analysis — standalone

python3 skills/music-analysis/scripts/analyze_music.py /path/to/audio.mp3
python3 skills/music-analysis/scripts/analyze_music.py track.mp3 --json

Reports:

• tempo / pulse stability / pulse confidence / swing proxy / pocket
• key estimate / key clarity / chroma entropy / harmonic change / tonal motion / tension
• timbre descriptors (brightness, richness, low-end, contrast, dynamic range)
• section labels (A/B/C...) and repeated material detection
• explainable emotional read with reasons

3. Temporal Listen — standalone

python3 skills/music-analysis/scripts/temporal_listen.py /path/to/audio.mp3
python3 skills/music-analysis/scripts/temporal_listen.py track.mp3 --json

Reports:

• sliding-window timeline (4s windows, 2s hops)
• energy contour
• mood labels
• harmonic tension + tonal motion
• transition types (drop hits, pulls back, tightens harmonically, shifts color, evolves)
• narrative arc (mountain / ascending / descending / plateau / wave)

Interpretation rules

• Structure labels are similarity labels, not verse/chorus claims.
• Swing proxy is a feel estimate, not drummer-grade microtiming truth.
• Emotion is explainable, derived from pulse + timbre + harmonic tension rather than a black-box mood guess.
• Lyrics can override the final vibe when filtered Whisper text is confident and emotionally clear.

Audio sourcing

The tool needs a real audio file on disk.

• Direct file (mp3, wav, flac, ogg, m4a — anything ffmpeg/librosa can read)
• YouTube / supported URLs: yt-dlp -x --audio-format mp3 -o "output.mp3" "URL_OR_SEARCH"

Whisper lyrics transcription

listen.py uses:

• CLI: /opt/homebrew/bin/whisper-cli
• Model: ~/.local/share/whisper-cpp/ggml-large-v3-turbo.bin
• Preprocess: convert input to mono 16kHz WAV via ffmpeg
• Fallback: skip gracefully if Whisper is missing or errors

Dependencies

Python:

• librosa
• numpy

System:

• ffmpeg
• ffprobe

Workspace hygiene

• Keep temporary audio files in a dedicated temp/output folder for the skill.
• Avoid modifying unrelated project files while working on audio analysis tasks.