WB2024/Essentia-to-Metadata
Intelligent audio analysis and automatic genre/mood tagging using Essentia ML models
This tool helps music enthusiasts, DJs, and librarians automatically organize their digital music collections. It takes your audio files (like MP3s, FLACs, or WAVs) and analyzes their actual sound content using machine learning to accurately assign genres and moods directly to the file's metadata tags. You get a consistently tagged library without manual effort or relying on potentially inaccurate online lookups.
Use this if you have a large music collection and want to automatically add detailed genre and mood tags based on the audio itself, rather than external databases, all without needing an internet connection.
Not ideal if you primarily rely on manual tagging, prefer to use online databases for metadata, or only need basic file organization.
Stars
35
Forks
3
Language
Python
License
MIT
Category
Last pushed
Mar 09, 2026
Commits (30d)
0
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