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Audio & Voice AI

Audiomuse AI

Audiomuse AI is an open-source, self-hosted music analysis application that uses sonic similarities to surface forgotten and related tracks and generate playlists from a personal library. It connects to supported media servers and offers tools including clustering, a music map, similar-track playlists, song paths, and search.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Audiomuse AI — OpenViST project profile in Audio & Voice AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you already have a library in a supported media server and want sound-based discovery, analysis, or playlist creation rather than relying only on metadata browsing.

Who it is for

  • People maintaining a personal music library in a supported media server.
  • Self-hosters who want sonic discovery and playlist tools alongside their existing music server.
  • Operators managing supported libraries across one or more media servers.

Documented uses

  • Find tracks in a library that sound similar to a selected song.
  • Rediscover tracks that have been overlooked in a collection.
  • Group music by sonic similarity and create playlists from those groups.
  • Explore a collection through a genre-based visual map or create a listening path between two songs.
  • Search a library by text, lyrical theme, or a short recording.
Before you choose

Requirements & limits

The project documents self-hosted deployments through Docker Compose or Podman, Kubernetes, and native applications for macOS, Windows and Linux. It also identifies a managed cloud service offered by Elestio.

  • A supported media server is a prerequisite for the documented containerized quick start.
  • The project states minimum hardware of 8 GB RAM, NVMe SSD storage, and a four-core Intel CPU with AVX2 support or ARM.
  • The example deployment uses PostgreSQL 15, and the project warns that other versions could cause errors.
  • The repository specifies the AGPL-3.0 license.
  • Lyrics search is limited to the 72 languages listed by the project.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Deploy with Docker Compose and start a library analysis

Launch AudioMuse AI and begin its initial analysis of a supported media-server library.

Before you start

  • Docker and Docker Compose
  • A running Navidrome, Jellyfin, Lyrion, Emby or Plex server
  • At least 8 GB RAM, NVMe SSD storage, and a four-core Intel CPU with AVX2 or ARM
  • The example deployment is designed for PostgreSQL 15

Steps

  1. Copy deployment/.env.example to deployment/.env and edit the file; the project suggests changing the default database user and password.

  2. Start the services with the command below.

    Documented command
    docker compose -f deployment/docker-compose.yaml up -d

  3. Open the web interface at http://localhost:8000.

  4. Go to Analysis and Clustering, select Start Analysis, and wait for the scan to finish; then explore clustering or the music map.

Expected outcome

A running deployment with an initial library analysis completed, enabling features such as clustering and the music map.

Guide limitations

  • The project warns that PostgreSQL versions other than 15 may cause errors in the example deployment.
  • The initial analysis must complete before exploring the analysis-based features.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
11 October 2026
Original website title
NeptuneHub/AudioMuse-AI · Official homepage title · retained in its source language
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