AudioMuse-AI
AudioMuse-AI is a self-hosted tool that uses sonic analysis to rediscover forgotten songs and generate groove-aware playlists for your music library, without external APIs. It integrates with Jellyfin, Navidrome, Emby, Lyrion or Plex and runs a Flask web UI backed by Redis Queue workers and PostgreSQL.
AudioMuse-AI
In Development
This script is currently in active development and may be unstable or incomplete. Use in production environments is not recommended.
This script is currently in active development and may be unstable or incomplete. Use in production environments is not recommended.
Installation
Default install:
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bash -c "$(curl -fsSL https://raw.githubusercontent.com/community-scripts/ProxmoxVED/main/ct/audiomuse-ai.sh)"
Default Credentials
| Username | Password |
|---|---|
admin | None |
Configuration
Config file:
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/opt/audiomuse-ai_data/audiomuse.env
Notes
The generated web UI password is saved to /root/audiomuse-ai.creds inside the container (user: admin). Open http://[IP]:8000 to log in.
Connect your music server before running an analysis: edit /opt/audiomuse-ai_data/audiomuse.env and set MEDIASERVER_TYPE (jellyfin, navidrome, emby, lyrion, plex) plus the matching URL and credentials, then 'systemctl restart audiomuse-ai audiomuse-ai-worker audiomuse-ai-worker-high'.
The installer downloads roughly 2.2 GB of ML models (musicnn, CLAP, Whisper, silero, gte) into /opt/audiomuse-ai_data/model, so the setup takes a while. Configuration, models and caches live in /opt/audiomuse-ai_data so they survive updates; the PostgreSQL database holds the analysis results. Sonic analysis itself is CPU- and memory-intensive.
Running two analysis workers can use significant RAM. If the container runs out of memory, disable audiomuse-ai-worker-high or increase the assigned RAM.