Over 2,521 Navidrome users rely on AudioMuse-AI today (based on Navidrome anonymous usage statistics), and that's before counting all our Jellyfin, Emby, Plex, LMS and Lyrion users.
What started as a personal project now powers music libraries all over the world. AudioMuse-AI will always remain free and open source because I believe sonic analysis should be accessible to everyone, regardless of their budget. No ads, no paywalls, no subscriptions. Your music library is analyzed on your own server, keeping your data completely under your control.
However, keeping the project alive and moving forward takes ongoing resources: AI licenses, homelab infrastructure, research, and countless hours spent on releases and user support.
If you choose to donate €10, or whatever feels right for you, you will help keep AudioMuse-AI alive and growing throughout 2027.
Every single contribution makes a difference.
Thank you,
NeptuneHub
Maintainer of AudioMuse-AI
AudioMuse-AI - Where Music Takes Shape
AudioMuse-AI is an opensource and self-hosted tool that uses sonic analysis to rediscover forgotten songs in your music library and generate groove-aware playlists that also capture the meaning behind each track, without relying on metadata or external APIs.
You can run it locally with Docker Compose or Podman, deploy it at scale in a Kubernetes cluster (AMD64 and ARM64 supported), or use native applications available for macOS, Windows, and Linux. It integrates with major self-hosted music servers including Navidrome, Jellyfin, LMS, Lyrion, Emby, and Plex, with more integrations planned.
Prefer not to self-host? Elestio offers AudioMuse-AI as a managed cloud service, and their YouTube video is a good introduction to the project and its features.
AudioMuse-AI lets you explore your music library in innovative ways, just start with an initial analysis, and you’ll unlock features like:
- Multiple Music Servers (from
v3.0.0): connect several media servers - any mix of Navidrome, Jellyfin, LMS, Lyrion, Emby and Plex - to a single AudioMuse-AI deployment. Built-in duplicate detection recognizes the same song across servers, so each track is analyzed only once and every server shares the result.
- Clustering: Automatically groups sonically similar songs, creating genre-defying playlists based on the music's actual sound.
- Instant Playlists: Simply tell the AI what you want to hear-like "high-tempo, low-energy music" and it will instantly generate a playlist for you.
- Music Map: Discover your music collection visually with a vibrant, genre-based 2D map.
- Playlist from Similar Songs: Pick a track you love, and AudioMuse-AI will find all the songs in your library that share its sonic signature, creating a new discovery playlist.
- Song Paths: Create a seamless listening journey between two songs. AudioMuse-AI finds the perfect tracks to bridge the sonic gap.
- Sonic Fingerprint: Generates playlists based on your listening habits, finding tracks similar to what you've been playing most often.
- Song Alchemy: Mix your ideal vibe, mark tracks as "ADD" or "SUBTRACT" to get a curated playlist and a 2D preview. Export the final selection directly to your media server.
- Text Search: search your song with simple text that can contains mood, instruments and genre like calm piano songs.
- Lyrics Search: search your library by theme, story or meaning, like love songs, not just the sound.
- Search by Recording: record 20 seconds of whatever is playing around you, or upload a clip, and find which song in your library it is; or pick a song of the library and find its other recordings.
Lyrics language support: the Lyrics Search feature works only with the 72 languages listed below.
Afrikaans, Albanian, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Lao, Latvian, Lithuanian, Macedonian, Malay, Malayalam, Marathi, Mongolian, Nepali, Norwegian, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Serbian, Sinhala, Slovak, Slovenian, Somali, Spanish, Swahili, Swedish, Tagalog, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Vietnamese, Welsh, Yoruba.
More information can be found in the docs folder: ARCHITECTURE, ALGORITHM DESCRIPTION, MULTIPLE MUSIC SERVERS, DEPLOYMENT STRATEGY, NAVIDROME SETUP, GPU DEPLOYMENT, CONFIGURATION PARAMETERS, AUTHENTICATION, PLUGINS, ERROR CODES and FAQ.
以上為 README 的開頭部分。其餘內容請閱讀原文。 檢視完整 README ↗
