AudioMuse-AI - Where Music Takes Shape

AudioMuse-AI is an open-source, Dockerized environment that brings automatic playlist generation to your self-hosted music library. Using tools such as Librosa and ONNX, it performs sonic analysis on your audio files locally, allowing you to curate playlists for any mood or occasion without relying on external APIs.

Deploy it easily on your local machine with Docker Compose or Podman, or scale it in a Kubernetes cluster (supports AMD64 and ARM64). It integrates with the main music servers' APIs such as Jellyfin, Navidrome, LMS, Lyrion, and Emby. More integrations may be added in the future.

AudioMuse-AI lets you explore your music library in innovative ways, just start with an initial analysis, and you’ll unlock features like:

• 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.

More information, like Frequently Asked Question (FAQ) can be found in the docs folder.

The full list or AudioMuse-AI related repository are:

• AudioMuse-AI: the core application, it run Flask and Worker containers to actually run all the feature;
• AudioMuse-AI Helm Chart: helm chart for easy installation on Kubernetes;
• AudioMuse-AI Plugin for Jellyfin: Jellyfin Plugin;
• AudioMuse-AI MusicServer: Experimental Open Subosnic like Music Sever with integrated sonic functionality.

And now just some NEWS:

• Version 0.7.4-beta add the support of Cron Job to schedule Analysis and Clustering task.
• Version 0.7.2-beta ad the new Song Map functionality to visualize your music. Also introduce the experimental support of Emby.
• Version 0.7.1-beta add the Song Alchemy functionality, create your playlist by blending together different song.
• Version 0.7.0-beta remove Tensorflow and use instead ONXX. This new version should have better result on different CPU and be also more stable between update. IMPORTANT: this new version will require a new analysis of the entire library to work correctly.
• Version 0.6.9-beta introduce the support to Lyrion Music Server.

Disclaimer

Important: Despite the similar name, this project (AudioMuse-AI) is an independent, community-driven effort. It has no official connection to the website audiomuse.ai.

We are not affiliated with, endorsed by, or sponsored by the owners of audiomuse.ai.

Table of Contents

• Quick Start Deployment on K3S WITH HELM
• Quick Start Deployment on K3S
• Local Deployment with Docker Compose
• Local Deployment with Podman Quadlets
• Hardware Requirements
• Configuration Parameters
• Docker Image Tagging Strategy
• Key Technologies
• How To Contribute
• Star History

Quick Start Deployment on K3S WITH HELM

The best way to install AudioMuse-AI on K3S (kubernetes) is by AudioMuse-AI Helm Chart repository

• Prerequisites:
  • A running K3S cluster.
  • kubectl configured to interact with your cluster.
  • helm installed.
  • Jellyfin or Navidrome or Lyrion installed.
  • Respect the HW requirements (look the specific chapter)

You can directly check the Helm Chart repo for more details and deployments examples.

Quick Start Deployment on K3S

This section provides a minimal guide to deploy AudioMuse-AI on a K3S (Kubernetes) cluster by directly using the deployment manifests.

• Prerequisites:

  • A running K3S cluster.
  • kubectl configured to interact with your cluster.
  • Jellyfin or Navidrome or Lyrion installed.
  • Respect the HW requirements (look the specific chapter)

• Jellyfin Configuration:

  • Navigate to the deployment/ directory.
  • Edit deployment.yaml to configure mandatory parameters:
    • Secrets:
      • jellyfin-credentials: Update api_token and user_id.
      • postgres-credentials: Update POSTGRES_USER, POSTGRES_PASSWORD, and POSTGRES_DB.
      • gemini-api-credentials (if using Gemini for AI Naming): Update GEMINI_API_KEY.
      • mistral-api-credentials (if using Mistral for AI Naming): Update MISTRAL_API_KEY.
    • ConfigMap (audiomuse-ai-config):
      • Update JELLYFIN_URL.
      • Ensure POSTGRES_HOST, POSTGRES_PORT, and REDIS_URL are correct for your setup (defaults are for in-cluster services).

• Navidrome/LMS (Open Subsonic API Music Server) Configuration:

  • Navigate to the deployment/ directory.
  • Edit deployment-navidrome.yaml to configure mandatory parameters:
    • Secrets:
      • navidrome-credentials: Update NAVIDROME_USER and NAVIDROME_PASSWORD.
      • postgres-credentials: Update POSTGRES_USER, POSTGRES_PASSWORD, and POSTGRES_DB.
      • gemini-api-credentials (if using Gemini for AI Naming): Update GEMINI_API_KEY.
      • mistral-api-credentials (if using Mistral for AI Naming): Update MISTRAL_API_KEY.
    • ConfigMap (audiomuse-ai-config):
      • Update NAVIDROME_URL.
      • Ensure POSTGRES_HOST, POSTGRES_PORT, and REDIS_URL are correct for your setup (defaults are for in-cluster services).

    • The same instruction used for Navidrome could apply to other Mediaserver that support Subsonic API. LMS for example is supported, only remember to user the Subsonic API token instead of the password.

• Lyrion Configuration:

  • Navigate to the deployment/ directory.
  • Edit deployment-lyrion.yaml to configure mandatory parameters:
    • Secrets:
      • postgres-credentials: Update POSTGRES_USER, POSTGRES_PASSWORD, and POSTGRES_DB.
      • gemini-api-credentials (if using Gemini for AI Naming): Update GEMINI_API_KEY.
    • ConfigMap (audiomuse-ai-config):
      • Update LYRION_URL.
      • Ensure POSTGRES_HOST, POSTGRES_PORT, and REDIS_URL are correct for your setup (defaults are for in-cluster services).

• Deploy:

  kubectl apply -f deployment/deployment.yaml

• Access:

  • Main UI: Access at http://<EXTERNAL-IP>:8000
  • API Docs (Swagger UI): Explore the API at http://<EXTERNAL-IP>:8000/apidocs

Local Deployment with Docker Compose

AudioMuse-AI provides Docker Compose files for different media server backends:

• Jellyfin: Use deployment/docker-compose.yaml
• Navidrome: Use deployment/docker-compose-navidrome.yaml
• Lyrion: Use deployment/docker-compose-lyrion.yaml
• Emby: Use deployment/docker-compose-emby.yaml

Choose the appropriate file based on your media server setup.

Prerequisites:

• Docker and Docker Compose installed.
• Jellyfin or Navidrome or Lyrion or Emby installed.
• Respect the hardware requirements

Steps:

1. Create your environment file:

   cp deployment/.env.example deployment/.env

   you can find the example here: deployment/.env.example

2. Review and Customize: Edit .env and provide the media-server credentials (e.g., JELLYFIN_URL, JELLYFIN_USER_ID, JELLYFIN_TOKEN or NAVIDROME_*, EMBY_*, LYRION_URL) along with any API keys (GEMINI_API_KEY, MISTRAL_API_KEY). The same values are injected into every compose file, so you only need to edit them here.

3. Start the Services:

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

   Swap the compose filename if you're targeting Navidrome (docker-compose-navidrome.yaml), Lyrion (docker-compose-lyrion.yaml) or Emby (docker-compose-emby.yaml). This command starts all services (Flask app, RQ workers, Redis, PostgreSQL) in detached mode (-d).

   IMPORTANT: both docker-compose.yaml and .env file need to be in the same directory.

4. Access the Application: Once the containers are up, you can access the web UI at http://localhost:8000.

5. Stopping the Services:

   docker compose -f deployment/docker-compose.yaml down

   Swap the compose filename here as well if you started a different variant. Note:

If you use LMS instead of the password you need to create and use the Subsonic API token. Additional Subsonic API based Mediaserver could require it in place of the password.

Remote worker tip: If you deploy a worker on different hardware (using docker-compose-worker.yaml or docker-compose-worker-nvidia.yaml), copy your .env to that machine and update WORKER_POSTGRES_HOST and WORKER_REDIS_URL so the worker can reach the main server.

Local Deployment with Podman Quadlets

For an alternative local setup, Podman Quadlet files are provided in the deployment/podman-quadlets directory for interacting with Navidrome. The unit files can be edited for use with Jellyfin.

These files are configured to automatically update AudioMuse-AI using the latest stable release and should perform an automatic rollback if the updated image fails to start.

Prerequisites:

• Podman and systemd.
• Jellyfin or Navidrome installed.
• Respect the hardware requirements

Steps:

1. Navigate to the deployment/podman-quadlets directory:

   cd deployment/podman-quadlets

2. Review and Customize:

   The audiomuse-ai-postgres.container and audiomuse-redis.container files are pre-configured with default credentials and settings suitable for local testing.
   You will need to edit environment variables within audiomuse-ai-worker.container and audiomuse-ai-flask.container files to reflect your personal credentials and environment.

   • For Navidrome, update NAVIDROME_URL, NAVIDROME_USER and NAVIDROME_PASSWORD with your real credentials.
   • For Jellyfin replace these variables with JELLYFIN_URL, JELLYFIN_USER_ID, JELLYFIN_TOKEN; add your real credentials; and change the MEDIASERVER_TYPE to jellyfin.

   Once you've customized the unit files, you will need to copy all of them into a systemd container directory, such as /etc/containers/systemd/user/.
   

3. Start the Services:

   systemctl --user daemon-reload
   systemctl --user start audiomuse-pod

   The first command reloads systemd (generating the systemd service files) and the second command starts all AudioMuse services (Flask app, RQ worker, Redis, PostgreSQL).

4. Access the Application: Once the containers are up, you can access the web UI at http://localhost:8000.

5. Stopping the Services:

   systemctl --user stop audiomuse-pod

Hardware Requirements

AudioMuse-AI has been tested on:

• Intel: HP Mini PC with Intel i5-6500, 16 GB RAM and NVMe SSD
• ARM: Raspberry Pi 5, 8 GB RAM and NVMe SSD

Suggested requirements:

• A 4-core Intel or ARM CPU (produced in 2015 or later) with AVX support
• 8 GB RAM
• SSD storage

It may run on older CPUs (3rd generation and above) and with less RAM (for example, 4 GB), but these configurations are untested.

First-generation Intel i7 CPUs and older are not supported because TensorFlow requires AVX support.

If you have tested the software on older CPUs, please open an issue to share your feedback.

You can check the Tested Hardware and Configuration notes to see which hardware has already been validated.

IMPORTANT: From v0.7.0-beta, ONNX replaces TensorFlow. As a result, some CPUs previously not supported may now work.

(Optional) Experimental Nvidia Support

NVidia GPU support is available for the worker process. This can significantly speed up processing of tracks.

This has been tested with an NVidia RX 3060 running CUDA 12.9 and Driver V575.64.05. During testing, the worker used up to 10GiB of VRAM but your mileage may vary.

Configuration Parameters

These are the parameters accepted for this script. You can pass them as environment variables using, for example, /deployment/deployment.yaml in this repository.

How to find jellyfin userid:

• Log into Jellyfin from your browser as an admin
• Go to Dashboard > “admin panel” > Users.
• Click on the user’s name that you are interested
• The User ID is visible in the URL (is the part just after = ):
  • http://your-jellyfin-server/web/index.html#!/useredit.html?userId=xxxxx

How to create an the jellyfin's API token:

• The API Token, still as admin you can go to Dashboard > “Admin panel” > API Key and create a new one.

The mandatory parameter that you need to change from the example are this:

Parameter	Description	Default Value
JELLYFIN_URL	(Required) Your Jellyfin server's full URL	http://YOUR_JELLYFIN_IP:8096
JELLYFIN_USER_ID	(Required) Jellyfin User ID.	(N/A - from Secret)
JELLYFIN_TOKEN	(Required) Jellyfin API Token.	(N/A - from Secret)
NAVIDROME_URL	(Required) Your Navidrome server's full URL	http://YOUR_JELLYFIN_IP:4553
NAVIDROME_USER	(Required) Navidrome User ID.	(N/A - from Secret)
NAVIDROME_PASSWORD	(Required) Navidrome user Password.	(N/A - from Secret)
LYRION_URL	(Required) Your Lyrion server's full URL	http://YOUR_LYRION_IP:9000
POSTGRES_USER	(Required) PostgreSQL username.	(N/A - from Secret)
POSTGRES_PASSWORD	(Required) PostgreSQL password.	(N/A - from Secret)
POSTGRES_DB	(Required) PostgreSQL database name.	(N/A - from Secret)
POSTGRES_HOST	(Required) PostgreSQL host.	postgres-service.playlist
POSTGRES_PORT	(Required) PostgreSQL port.	5432
REDIS_URL	(Required) URL for Redis.	redis://localhost:6379/0
GEMINI_API_KEY	(Required if AI_MODEL_PROVIDER is GEMINI) Your Google Gemini API Key.	(N/A - from Secret)
MISTRAL_API_KEY	(Required if AI_MODEL_PROVIDER is MISTRAL) Your Mistral API Key.	(N/A - from Secret)

These parameters can be left as-is:

Parameter	Description	Default Value
TEMP_DIR	Temp directory for audio files	/app/temp_audio
CLEANING_SAFETY_LIMIT	Max number of albums deleted during cleaning	100
MUSIC_LIBRARIES	Comma-separated list of music libraries/folders for analysis. If empty, all libraries/folders are scanned. For Lyrion: Use folder paths like "/music/myfolder". For Jellyfin/Navidrome: Use library/folder names.	"" (empty - scan all)
ENABLE_PROXY_FIX	Enable Proxy Fix for Flask when behind a reverse proxy. Example Nginx configuration: config.py	false

These are the default parameters used when launching analysis or clustering tasks. You can change them directly in the front-end.

Parameter	Description	Default Value
Analysis General
NUM_RECENT_ALBUMS	Number of recent albums to scan (0 for all).	0
TOP_N_MOODS	Number of top moods per track for feature vector.	5
Clustering General
ENABLE_CLUSTERING_EMBEDDINGS	Whether to use audio embeddings (True) or score-based features (False) for clustering.	true
CLUSTER_ALGORITHM	Default clustering: kmeans, dbscan, gmm, spectral.	kmeans
MAX_SONGS_PER_CLUSTER	Max songs per generated playlist segment.	0
MAX_SONGS_PER_ARTIST	Max songs from one artist per cluster.	3
MAX_DISTANCE	Normalized distance threshold for tracks in a cluster.	0.5
CLUSTERING_RUNS	Iterations for Monte Carlo evolutionary search.	5000
TOP_N_PLAYLISTS	POST Clustering it keep only the top N diverse playlist.	8
Similarity General
INDEX_NAME	Name of the index, no need to change.	music_library
VOYAGER_EF_CONSTRUCTION	Number of element analyzed to create the neighbor list in the index.	1024
VOYAGER_M	Number of neighbore More = higher accuracy.	64
VOYAGER_QUERY_EF	Number neighbor analyzed during the query.	1024
VOYAGER_METRIC	Different tipe of distance metrics: angular, euclidean,dot	angular
SIMILARITY_ELIMINATE_DUPLICATES_DEFAULT	It enable the possibility of use the MAX_SONGS_PER_ARTIST also in similar song	true
SIMILARITY_RADIUS_DEFAULT	Default behavior for radius similarity mode. When true, similarity results may be re-ordered using the radius (bucketed) algorithm for better listening paths.	true
Sonic Fingerprint General
SONIC_FINGERPRINT_NEIGHBORS	Default number of track for the sonic fingerprint	100
Song Alchemy General
ALCHEMY_DEFAULT_N_RESULTS	Number of similar songs to return when creating the Alchemy result (default).	100
ALCHEMY_MAX_N_RESULTS	Maximum number of similar songs to return for Alchemy results.	200
ALCHEMY_TEMPERATURE	Temperature for probabilistic sampling in Song Alchemy (softmax temperature). Use 0.0 for deterministic selection.	1.0
ALCHEMY_SUBTRACT_DISTANCE	Minimum distance from the subtract-centroid to keep a candidate (metric-dependent).	0.2
Similar Song and Song Path Duplicate filtering General
DUPLICATE_DISTANCE_THRESHOLD_COSINE	Less than this cosine distance the track is a duplicate.	0.01
DUPLICATE_DISTANCE_THRESHOLD_EUCLIDEAN	Less than this euclidean distance the track is a duplicate.	0.15
DUPLICATE_DISTANCE_CHECK_LOOKBACK	How many previous song need to be checked for duplicate.	1
MOOD_SIMILARITY_THRESHOLD	Maximum normalized distance for mood similarity filtering. Lower value will give more importance to mood	0.15
Song Path General
PATH_DISTANCE_METRIC	The distance metric to use for pathfinding. Options: 'angular', 'euclidean'	angular
PATH_DEFAULT_LENGTH	Default number of songs in the path if not specified in the API request	25
PATH_AVG_JUMP_SAMPLE_SIZE	Number of random songs to sample for calculating the average jump distance	200
PATH_CANDIDATES_PER_STEP	Number of candidate songs to retrieve from Voyager for each step in the path	25
PATH_LCORE_MULTIPLIER	It multiply the number of centroid created based on the distance. Higher is better for distant song and worst for nearest.	3
PATH_FIX_SIZE	When true, path generation will attempt to produce exactly the requested path length using centroid merging and backfilling. When false, the algorithm will perform a single best pick per centroid and may return a shorter path. Can be overridden per-request via the path_fix_size query parameter.	false
Evolutionary Clustering & Scoring
ITERATIONS_PER_BATCH_JOB	Number of clustering iterations processed per RQ batch job.	20
MAX_CONCURRENT_BATCH_JOBS	Maximum number of clustering batch jobs to run simultaneously.	10
CLUSTERING_BATCH_TIMEOUT_MINUTES	Max time a batch can run before being considered failed (prevents infinite hangs).	60
CLUSTERING_MAX_FAILED_BATCHES	Max number of failed batches before stopping new launches and forcing completion.	10
CLUSTERING_BATCH_CHECK_INTERVAL_SECONDS	How often to check batch status for timeout detection.	30
TOP_K_MOODS_FOR_PURITY_CALCULATION	Number of centroid's top moods to consider when calculating playlist purity.	3
EXPLOITATION_START_FRACTION	Fraction of runs before starting to use elites.	0.2
EXPLOITATION_PROBABILITY_CONFIG	Probability of mutating an elite vs. random generation.	0.7
MUTATION_INT_ABS_DELTA	Max absolute change for integer parameter mutation.	3
MUTATION_FLOAT_ABS_DELTA	Max absolute change for float parameter mutation.	0.05
MUTATION_KMEANS_COORD_FRACTION	Fractional change for KMeans centroid coordinates.	0.05
K-Means Ranges
NUM_CLUSTERS_MIN	Min $K$ for K-Means.	40
NUM_CLUSTERS_MAX	Max $K$ for K-Means.	100
USE_MINIBATCH_KMEANS	Whether to use MiniBatchKMeans (True) or standard KMeans (False) when clustering embeddings.	false
DBSCAN Ranges
DBSCAN_EPS_MIN	Min epsilon for DBSCAN.	0.1
DBSCAN_EPS_MAX	Max epsilon for DBSCAN.	0.5
DBSCAN_MIN_SAMPLES_MIN	Min min_samples for DBSCAN.	5
DBSCAN_MIN_SAMPLES_MAX	Max min_samples for DBSCAN.	20
GMM Ranges
GMM_N_COMPONENTS_MIN	Min components for GMM.	40
GMM_N_COMPONENTS_MAX	Max components for GMM.	100
GMM_COVARIANCE_TYPE	Covariance type for GMM (task uses full).	full
Spectral Ranges
SPECTRAL_N_CLUSTERS_MIN	Min components for Spectral clustering.	40
SPECTRAL_N_CLUSTERS_MAX	Max components for Spectral clustering.	100
SPECTRAL_N_NEIGHBORS	Number of Neighbors on which do clustering. Higher is better but slower	20
PCA Ranges
PCA_COMPONENTS_MIN	Min PCA components (0 to disable).	0
PCA_COMPONENTS_MAX	Max PCA components (e.g., 8 for feature vectors, 199 for embeddings).	199
AI Naming (*)
AI_MODEL_PROVIDER	AI provider: OLLAMA, GEMINI, MISTRAL or NONE.	NONE
TOP_N_ELITES	Number of best solutions kept as elites.	10
SAMPLING_PERCENTAGE_CHANGE_PER_RUN	Percentage of songs to swap out in the stratified sample between runs (0.0 to 1.0).	0.2
MIN_SONGS_PER_GENRE_FOR_STRATIFICATION	Minimum number of songs to target per stratified genre during sampling.	100
STRATIFIED_SAMPLING_TARGET_PERCENTILE	Percentile of genre song counts to use for target songs per stratified genre.	50
OLLAMA_SERVER_URL	URL for your Ollama instance (if AI_MODEL_PROVIDER is OLLAMA).	http://<your-ip>:11434/api/generate
OLLAMA_MODEL_NAME	Ollama model to use (if AI_MODEL_PROVIDER is OLLAMA).	mistral:7b
GEMINI_MODEL_NAME	Gemini model to use (if AI_MODEL_PROVIDER is GEMINI).	gemini-2.5-pro
MISTRAL_MODEL_NAME	Mistral model to use (if AI_MODEL_PROVIDER is MISTRAL).	ministral-3b-latest
Scoring Weights
SCORE_WEIGHT_DIVERSITY	Weight for inter-playlist mood diversity.	2.0
SCORE_WEIGHT_PURITY	Weight for playlist purity (intra-playlist mood consistency).	1.0
SCORE_WEIGHT_OTHER_FEATURE_DIVERSITY	Weight for inter-playlist 'other feature' diversity.	0.0
SCORE_WEIGHT_OTHER_FEATURE_PURITY	Weight for intra-playlist 'other feature' consistency.	0.0
SCORE_WEIGHT_SILHOUETTE	Weight for Silhouette Score (cluster separation).	0.0
SCORE_WEIGHT_DAVIES_BOULDIN	Weight for Davies-Bouldin Index (cluster separation).	0.0
SCORE_WEIGHT_CALINSKI_HARABASZ	Weight for Calinski-Harabasz Index (cluster separation).	0.0

(*) For using GEMINI API you need to have a Google account, a free account can be used if needed. Same goes for Mistral. Instead if you want to self-host Ollama here you can find a deployment example:

• https://github.com/NeptuneHub/k3s-supreme-waffle/tree/main/ollama

Docker Image Tagging Strategy

Our GitHub Actions workflow automatically builds and pushes Docker images. Here's how our tags work:

• :latest
  • Builds from the main branch.
  • Represents the latest stable release.
  • Recommended for most users.
• :devel
  • Builds from the devel branch.
  • Contains features still in development, not fully tested, and they could not work.
  • Use only for development.
• :vX.Y.Z (e.g., :v0.1.4-alpha, :v1.0.0)
  • Immutable tags created from specific Git releases/tags.
  • Ensures you're running a precise, versioned build.
  • Use for reproducible deployments or locking to a specific version.

IMPORTANT: the -nvidia images are experimental. Try them if you want to help us improve the support, but we do not recommend using them for daily production use.

Key Technologies

AudioMuse AI is built upon a robust stack of open-source technologies:

• Flask: Provides the lightweight web interface for user interaction and API endpoints.
• Redis Queue (RQ): A simple Python library for queueing jobs and processing them in the background with Redis.
• Supervisord: Supervisor is a client/server system that allows its users to monitor and control a number of processes on UNIX-like operating systems.
• Essentia-tensorflow An open-source library for audio analysis, feature extraction, and music information retrieval. (used only until version v0.5.0-beta)
• MusicNN Tensorflow Audio Models from Essentia Leverages pre-trained MusicNN models for feature extraction and prediction. More details and models.
• Librosa Library for audio analysis, feature extraction, and music information retrieval. (used from version v0.6.0-beta)
• ONNX Open Neural Network Exchange format and ONNX Runtime for fast, portable, cross-platform model inference. (Used from v0.7.0-beta, replaces TensorFlow)
• Tensorflow Platform developed by Google for building, training, and deploying machine learning and deep learning models. (Used only in versions before v0.7.0-beta)
• scikit-learn Utilized for machine learning algorithms:
• voyager Approximate Nearest Neighbors used for the /similarity interface. Used from v0.6.3-beta
• PostgreSQL: A powerful, open-source relational database used for persisting:
• Ollama Enables self-hosting of various open-source Large Language Models (LLMs) for tasks like intelligent playlist naming.
• Docker / OCI-compatible Containers – The entire application is packaged as a container, ensuring consistent and portable deployment across environments.

How To Contribute

Contributions, issues, and feature requests are welcome!
This is a BETA early release, so expect bugs or functions that are still not implemented.

For more details on how to contribute please follow the Contributing Guidelines

Star History