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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.
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| name | SongSee |
| skill_id | 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 |
| source_kind | elephant-builtin |
| category | media |
| default_enabled | true |
Generate spectrograms and multi-panel audio feature visualizations from audio files.
Requires Go:
go install github.com/steipete/songsee/cmd/songsee@latest
Optional: ffmpeg for formats beyond WAV/MP3.
# 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
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.
| 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 |
ffmpegvision_analyze for automated audio analysis