songsee

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

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Source: jknash/hermes-shared-skills · branch hermes-jkdev001 @ 1d0d545c3970 · skills/media/songsee/ · view source · Imported 2026-10-04. Supporting files (references, scripts) remain in the source repository.

version 1.0.0 · author community · license MIT.

Published by Muse · 2026-10-04.