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AudioMuse-AI
Python

AudioMuse-AI

AudioMuse-AI uses sonic analysis to rediscover forgotten songs, uncover hidden connections in your music library, and generate intelligent playlists for Navidrome, Jellyfin, LMS, Lyrion, Emby and Plex: no metadata or external services required.

by NeptuneHub · GitHub
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Self-hostable
clapdockerembyAGPL-3.0PythonSelf-hostable
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In plain words

Create personalized music playlists and rediscover forgotten songs from your library without needing any song details.

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2026-07-072026-08-31
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📄 About

AudioMuse-AI uses sonic analysis to rediscover forgotten songs, uncover hidden connections in your music library, and generate intelligent playlists for Navidrome, Jellyfin, LMS, Lyrion, Emby and Plex: no metadata or external services required.

AudioMuse-AI has 2.5k stars on GitHub. It has been forked 145 times. AudioMuse-AI is written mainly in Python. It has been in active development since 2025. AudioMuse-AI is available under the AGPL-3.0 license. Its main topics are clap, docker, emby, jellyfin.

Frequently asked questions

What is AudioMuse-AI?

AudioMuse-AI uses sonic analysis to rediscover forgotten songs, uncover hidden connections in your music library, and generate intelligent playlists for Navidrome, Jellyfin, LMS, Lyrion, Emby and Plex: no metadata or external services required.

Is AudioMuse-AI open source?

AudioMuse-AI is an open-source project. It is released under the AGPL-3.0 license.

Is AudioMuse-AI free?

Yes. AudioMuse-AI is free and open source — you can use, modify and self-host it. Its AGPL-3.0 license is a strong copyleft: if you distribute a modified version — including offering it as a network service — your changes must be released under the same license.

What license does AudioMuse-AI use?

AudioMuse-AI is available under the AGPL-3.0 license.

What language is AudioMuse-AI written in?

AudioMuse-AI is written mainly in Python.

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