Generate spectrograms and multi-panel audio feature visualizations from audio files.
npx skills add https://github.com/zerwiz/wayofpi --skill songsee-zerwiz
Please help me install this Agent Skill. Skill: songsee Source: https://github.com/zerwiz/wayofpi/tree/main/.hermes/hermes-agent/skills/media/songsee Command: npx skills add https://github.com/zerwiz/wayofpi --skill songsee-zerwiz
It removes the need to manually inspect audio waveforms and guess what is happening in a track by turning sound into readable visual diagnostics.
Ask the skill to analyze an audio file and produce a spectrogram or multi-feature visualization for the exact track you provide.
High-intent search queries and answers about installing and using this skill.
You can generate a spectrogram by asking this skill to analyze your audio file, which turns sound into readable visual diagnostics. It creates clear frequency views that reveal energy patterns, transients, and tonal balance for music production debugging.
Yes, you can combine mel, chroma, MFCC, tempogram, and loudness into a single multi-panel visualization. This multi-panel audio analysis allows for deeper comparison of mastered versus raw mixes or audio-processing results.
You need ffmpeg support to analyze non-WAV or MP3 audio inputs. The command-line songsee installation requires optional ffmpeg support to process these formats and apply configurable visualization, frequency, and time-slice parameters.
The best way to inspect a specific time slice of a long recording is using configurable time-slice parameters. This skill processes the exact track you provide, allowing you to isolate and visualize specific segments for detailed audio analysis.
A spectrogram visualizes audio energy patterns and transients by converting sound into readable visual diagnostics. It provides clear frequency views that reveal the energy patterns, transients, and tonal balance present in your recordings.
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