Generate spectrograms and audio feature visualizations from audio files.
npx skills add https://github.com/afel6/scal-ai-pipeline --skill songsee-afel6
Please help me install this Agent Skill. Skill: songsee Source: https://github.com/afel6/scal-ai-pipeline/tree/main/hermes_skills_library/media/songsee Command: npx skills add https://github.com/afel6/scal-ai-pipeline --skill songsee-afel6
Produce clear, publication-ready spectrograms and multi-panel audio feature visualizations from raw audio files so engineers and producers can quickly inspect spectral content, temporal patterns, and feature summaries without manual plotting.
Run the songsee CLI on an audio file to create a multi-panel visualization image by running songsee track.mp3 -o spectrogram.png
High-intent search queries and answers about installing and using this skill.
Generate spectrograms by running the songsee CLI on an audio file and specifying an output image path. This produces publication-ready visualizations of spectral content for immediate analysis and documentation.
Yes, you can render spectrogram, mel, chroma, MFCC, tempogram, flux, and self-similarity views together. The tool produces a single multi-panel image grid for direct visual comparison of these audio features.
You need the Go-installed songsee binary for spectrogram generation, while ffmpeg is optional. FFmpeg provides broader audio format decoding support if you are processing files beyond standard formats.
Automate batch audio visualization by integrating the CLI-first songsee tool into your pipelines. You can specify start and duration parameters, window and hop sizes, and output formats to inspect synthesis artifacts across multiple files.
The best way to document audio processing steps is generating multi-panel visualizations. Create clear, publication-ready spectrogram and feature summary images to verify processing chains and include them directly in your reports.
Yes, songsee supports tempogram and spectral flux visualizations alongside mel, chroma, and MFCC views. You can generate these specialized audio feature plots to inspect temporal patterns and verify processing chains.
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