WB2024 / Essentia-to-Metadata
Intelligent audio analysis and automatic genre/mood tagging using Essentia ML models
Why this job: works in the areas we cover, but does not say which job it does
What it does
- Audio-based analysis. Analyzes actual audio content, not metadata lookups
- 400 genre classifications. Uses Discogs taxonomy for detailed genre tagging
- Mood detection. Detects moods like energetic, dark, happy, aggressive, etc.
- Analysis mode selection. Choose genres only, moods only, or both per run
- Default library path. Save your library root; browse it with an arrow-key folder navigator on every run
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Inactive
- Last commit 7 months ago
- Stars
- 132
- +2 this week
- Licence
- MIT
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- None yet
- No releases
- Built with
- Python
- 85% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 0% closed
- 4 still open
- Pull requests
- 1 merged
- 4 waiting
- Releases
- None
- Never published
- No contributing guide
- No security policy
- No code of conduct
Similar repos
| Repo | Stars | Last commit | Licence | Downloads for |
|---|---|---|---|---|
| libAudioFlux / audioFlux | 3,383 | 8 months ago | MIT | No downloads |
| magenta / magenta-realtime | 1,866 | 3 months ago | Apache-2.0 | No downloads |
More: languages, topics and project record
Languages
- Python 85%
- Shell 14%
- Dockerfile 2%
Topics
- audio-analysis
- essentia
- machine-learning
- metadata
- music
- music-tagging
- python
Project record
- JobOther AI tools
- StatusListed
- Commits34
- Forks18
- Watching1
- Stars in 28 days+5
- CreatedFeb 17, 2026
- First seen by usNov 7, 2026
- CheckedNov 8, 2026