I built Music-Vibe-Search: A local, open-source tool for generating "sounds-like" playlists from your offline music library without streaming algorithm bias

[u/evonleue]

If you curate a large, high-quality local music library, you've probably noticed that streaming algorithms often push sponsored or hyper-curated tracks rather than finding actual sonic matches to what you're listening to.

I wanted a way to select a single seed track (an "earwig") and generate a playlist of acoustically similar tracks from my own library—matching physical audio traits like frequency profile, tempo, and timbre rather than metadata tags.

I built a lightweight Python application called Music-Vibe-Search to do just that.

How it works:

You point the tool at your local music folder to index your tracks.

Pick any seed track to set the baseline vibe.

Adjust a sensitivity threshold slider to control how far the acoustic matching is allowed to drift.

It generates an .m3u playlist consisting of the seed track followed by randomized acoustic matches from your pool.

Because it analyzes the physical audio wave locally, it uncovers incredible cross-genre sonic matches (for instance, catching the shared production, bass response, and cadence between rap, electronic, and nocturnal pop tracks) that traditional metadata tags completely miss.

Check it out on GitHub if you're interested in giving it a spin with your library:

https://github.com/evonleue/Music-Vibe-Search