songsee
Audio spectrograms/features (mel, chroma, MFCC) via CLI.
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skill
Media
beginner
Audio spectrograms/features (mel, chroma, MFCC) via CLI.
Real data. Real impact.
Emerging
Developers
Per week
Excellent
Skills give you superpowers. Install in 30 seconds.
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 |
|---|---|
| Standard frequency spectrogram |
| Mel-scaled spectrogram |
| Pitch class distribution |
| Harmonic/percussive separation |
| Self-similarity matrix |
| Loudness over time |
| Tempo estimation |
| Mel-frequency cepstral coefficients |
| Spectral flux (onset detection) |
Multiple
--viz types render as a grid in a single image.
| Flag | Description |
|---|---|
| Visualization types (comma-separated) |
| Color palette: , , , , |
/ | Output image dimensions |
/ | FFT window and hop size |
/ | Frequency range filter |
/ | Time slice of the audio |
| Output format: or |
| Output file path |
ffmpegvision_analyze for automated audio analysisMIT
mkdir -p ~/.hermes/skills/media/songsee && curl -o ~/.hermes/skills/media/songsee/SKILL.md https://raw.githubusercontent.com/NousResearch/hermes-agent/main/skills/media/songsee/SKILL.md1,500+ AI skills, agents & workflows. Install in 30 seconds. Part of the Torly.ai family.
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