Generate high-resolution spectrograms and multi-panel audio feature visualizations from audio files.
npx skills add https://github.com/Yogi-076/Vajrascan.on --skill songsee-yogi-076
Please help me install this Agent Skill. Skill: songsee Source: https://github.com/Yogi-076/Vajrascan.on/tree/main/moltbot/skills/songsee Command: npx skills add https://github.com/Yogi-076/Vajrascan.on --skill songsee-yogi-076
Quickly produce visual, shareable representations of audio so you can inspect frequency content, temporal features, and derived audio descriptors without manual signal processing.
Generate a spectrogram image of track.mp3 and save the result as out.png using the songsee CLI.
High-intent search queries and answers about installing and using this skill.
To generate a spectrogram, run the songsee CLI on a local audio file to create a high-resolution frequency visualization image, saving the output to formats like PNG. It natively decodes audio and falls back to ffmpeg for unsupported formats.
Multi-panel audio feature visualization generates combined image panels displaying mel, chroma, hpss, self-similarity, loudness, tempogram, mfcc, and flux. This extracts derived audio descriptors to inspect temporal and frequency features without manual signal processing.
Yes, you can visualize piped stdin audio streams and time-sliced excerpts. The songsee CLI supports flexible input and slicing, allowing you to generate visual feature panels for specific track segments or streamed audio data.
Yes, songsee works with ffmpeg to handle non-native audio decodes. When the CLI encounters an unsupported audio format, it automatically falls back to ffmpeg to process the raw audio file for visualization generation.
You can extract mel, chroma, hpss, self-similarity, loudness, tempogram, mfcc, and flux features. These derived audio descriptors are rendered as visual panels, making the tool useful for music analysis, audio debugging, and dataset previewing.
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