Generate spectrograms and feature-panel visualizations from WAV/MP3 audio files.
npx skills add https://github.com/Luzzer/Kraken --skill songsee-luzzer
Please help me install this Agent Skill. Skill: songsee Source: https://github.com/Luzzer/Kraken/tree/main/skills/songsee Command: npx skills add https://github.com/Luzzer/Kraken --skill songsee-luzzer
Generate spectrograms and feature-panel visualizations from audio with minimal setup, enabling quick visual analysis of audio tracks.
Run songsee on an audio file (for example track.mp3) to generate a spectrogram and a set of feature panels.
High-intent search queries and answers about installing and using this skill.
To generate a spectrogram from an MP3 or WAV file, you can run the songsee binary on your audio track to render and export the visualization directly to PNG or JPG formats for quick visual inspection.
For music research, you can generate feature-panel visualizations including mel, chroma, HPSS, self-similarity, loudness, tempo, MFCC, and flux to visually inspect a track's texture and timing.
You do not strictly need ffmpeg to process audio files for visualization, as WAV and MP3 decoding is handled natively; however, ffmpeg is supported to handle decoding when needed for broader compatibility.
Yes, you can use spectrogram visualization for batch audio reviews and podcast production workflows, as the tool supports both single-track analysis and batch processing to inform editing decisions.
The best way to visually analyze audio texture and timing is by generating feature-panel visualizations like mel, chroma, and self-similarity, which provide in-depth graphical representations of your audio track's characteristics.
dsactivi-2Generate spectrograms and feature-panel visualizations from audio files using the songsee CLI.
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Dolonia333Generate spectrograms and feature-panel visualizations from audio files using the songsee CLI.
