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

CODEBLOCK0

What it does in one pass:

1. 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 INLINECODE0
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

CODEBLOCK1

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

CODEBLOCK2

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: INLINECODE1

Whisper lyrics transcription

INLINECODE2 uses:

• - CLI: INLINECODE3
• Model: INLINECODE4
• 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.