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:
- Snapshot analysis: tempo, pulse stability, swing proxy, key clarity, harmonic tension, timbre, structure
- Whisper lyric transcription and filtering first — keep only real lyric text, drop artifact tags like
[MUSIC] - Temporal listen: windowed energy / mood / tension journey
- 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.