Imported from ibragimov-oasis/vibe-coder (vc-kit/content/COMBINED/COMBINED/skills/skills-hermes/hermes/builtin/media/songsee/SKILL.md). Install upstream with npx skills add ibragimov-oasis/vibe-coder --skill songsee. Copyright stays with the author.

name: songsee description: Generate spectrograms and audio feature visualizations (mel, chroma, MFCC, tempogram, etc.) from audio files via CLI. Useful for audio analysis, music production debugging, and visual documentation. version: 1.0.0 author: community license: MIT metadata: hermes: tags: [Audio, Visualization, Spectrogram, Music, Analysis] homepage: https://github.com/steipete/songsee prerequisites: commands: [songsee]

• domain/skills
• artifact/skill
• source/skills-hermes

songsee

Generate spectrograms and multi-panel audio feature visualizations from audio files.

Prerequisites

Requires Go:

go install github.com/steipete/songsee/cmd/songsee@latest

Optional: ffmpeg for formats beyond WAV/MP3.

Quick Start

# Basic spectrogram
songsee track.mp3

# Save to specific file
songsee track.mp3 -o spectrogram.png

# Multi-panel visualization grid
songsee track.mp3 --viz spectrogram,mel,chroma,hpss,selfsim,loudness,tempogram,mfcc,flux

# Time slice (start at 12.5s, 8s duration)
songsee track.mp3 --start 12.5 --duration 8 -o slice.jpg

# From stdin
cat track.mp3 | songsee - --format png -o out.png

Visualization Types

Use --viz with comma-separated values:

Multiple --viz types render as a grid in a single image.

Common Flags

Notes

• WAV and MP3 are decoded natively; other formats require ffmpeg
• Output images can be inspected with vision_analyze for automated audio analysis
• Useful for comparing audio outputs, debugging synthesis, or documenting audio processing pipelines

🔗 Связи

• [[MOC - Skills]] — Skills library
• [[skills/skills-hermes]] — Category: skills-hermes