/music-analyze — Full Music Analysis

Analyze a local audio file and output a comprehensive structured JSON containing rhythm, emotion, timbre, tonality, lyrics, onset, and color palette data.

Usage

/music-analyze <audio_file_path>

Accepted Formats

MP3, WAV, FLAC, OGG, M4A, AAC, WMA

Steps

1. Validate the audio file path exists and is a supported format
2. Run the full analysis pipeline:

python3 -m music_analyzer analyze "<audio_file_path>"

3. Parse the JSON output and present a structured summary to the user:

   • Rhythm: BPM, time signature, song structure sections
   • Emotion: Primary mood, energy level, valence, genre
   • Timbre: Brightness, warmth, dynamic range, MFCC summary
   • Tonality: Key, mode, chord progression highlights
   • Lyrics: Whether vocals detected, language, transcription preview
   • Onsets: Onset rate (for visual sync reference)
   • Color Palette: Suggested colors based on mood

4. If the user wants to generate Dreamina prompts or storyboards, suggest:

   • /music-to-dreamina for Dreamina image/video generation prompts
   • /music-to-storyboard for shot-by-shot storyboard
   • /music-color-palette for detailed color scheme

Options

• Add --no-cache to force re-analysis (skip cached results)
• Add --no-separation to skip Demucs source separation (faster)
• Add --output <path> to save JSON to a specific file

Output

The analysis JSON follows the MusicAnalysisResult schema and can be saved and reused as input for the formatter commands (dreamina, storyboard, color-palette).

Error Handling

• If librosa is not installed, instruct user: pip install -e ~/.claude/plugins/music-analyzer/src/
• If optional features are missing, the tool will degrade gracefully and note which tier is active (lite/standard/full)