Songsee — Audio spectrograms/features (mel, chroma, MFCC) via CLI

Songsee

Audio spectrograms/features (mel, chroma, MFCC) via CLI.

Skill metadata


Source	Bundled (installed by default)
Path	`skills/media/songsee`
Version	`1.0.0`
Author	community
License	MIT
Tags	`Audio`, `Visualization`, `Spectrogram`, `Music`, `Analysis`

Reference: full SKILL.md

The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.

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:

Type	Description
`spectrogram`	Standard frequency spectrogram
`mel`	Mel-scaled spectrogram
`chroma`	Pitch class distribution
`hpss`	Harmonic/percussive separation
`selfsim`	Self-similarity matrix
`loudness`	Loudness over time
`tempogram`	Tempo estimation
`mfcc`	Mel-frequency cepstral coefficients
`flux`	Spectral flux (onset detection)

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

Common Flags

Flag	Description
`--viz`	Visualization types (comma-separated)
`--style`	Color palette: `classic`, `magma`, `inferno`, `viridis`, `gray`
`--width` / `--height`	Output image dimensions
`--window` / `--hop`	FFT window and hop size
`--min-freq` / `--max-freq`	Frequency range filter
`--start` / `--duration`	Time slice of the audio
`--format`	Output format: `jpg` or `png`
`-o`	Output file path

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