songsee

Solid

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.

AI & Automation 191,515 stars 33299 forks Updated today MIT

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Skill Content

# songsee Generate spectrograms and multi-panel audio feature visualizations from audio files. ## Prerequisites Requires [Go](https://go.dev/doc/install): ```bash go install github.com/steipete/songsee/cmd/songsee@latest ``` Optional: `ffmpeg` for formats beyond WAV/MP3. ## Quick Start ```bash # 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...

Details

Author
NousResearch
Repository
NousResearch/hermes-agent
Created
10 months ago
Last Updated
today
Language
Python
License
MIT

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