Generate spectrograms and feature panels from WAV or MP3 audio files using the songsee CLI.
npx skills add https://github.com/hcnimi/clawdis --skill songsee-hcnimi
Please help me install this Agent Skill. Skill: songsee Source: https://github.com/hcnimi/clawdis/tree/main/skills/songsee Command: npx skills add https://github.com/hcnimi/clawdis --skill songsee-hcnimi
Visualize audio content quickly by producing spectrograms and feature panels from audio files, reducing manual plotting and analysis time.
Run songsee on an audio file to generate spectrograms and feature panels.
High-intent search queries and answers about installing and using this skill.
You generate a spectrogram by running the songsee CLI on an audio file. It processes input formats like WAV and MP3 to produce visual representations of frequency content and temporal structure.
Yes, you can visualize MFCCs, chroma, and spectral flux by creating multi-panel feature visualizations. These feature panels support music analysis and signal-processing tasks directly from the audio input.
Yes, generating audio spectrograms requires the songsee binary to be installed and accessible in your environment. The CLI relies on this system-level binary to process audio files and render visualizations.
Spectrogram generation supports WAV and MP3 input formats. The CLI processes these audio files to produce spectrograms and optional visualization panels for audio QA workflows.
The best way to compare tracks in QA pipelines is generating spectrograms and feature panels. This provides quick visual representations to verify audio processing results and inspect frequency content differences.
Using a CLI for audio visualization reduces manual plotting and analysis time. It automates spectrogram and feature panel generation, enabling quick visual inspections for research and data curation workflows.
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