Beets statistics

Intro

A well maintained music library is a great thing. Wouldn't insights on such a library be great? With beets you can manage and relate your music files to MusicBrainz IDs with all their great metadata.

This project, beets statistics, offers a simple web page, which shows how many jazz albums you have, what the quality factor of your library is and what your top decade of music is.

In order to work, beets-statistics needs a beets library, so you should import all your music files with beets first.

What does beets-statistics look like?

Here's a couple of screenshot showing beets-statistics in action:

General statistics about the music library	Complete album list	Top genre list

Genre distribution	Bitrate distribution

How to run beets statistics?

Run beets-statistics with Python

• Install Python 3.9 or higher
• Install pipenv
• Run pipenv install in the source directory
• Locate your beets library database
• Run MUSICLIBRARY_DB="<path to db file>" pipenv run prod to start the web app in production mode.
• Open the device's IP address on port 8000

Run beets-statistics with Docker Compose

docker-compose.yaml

Be sure to mount the musiclibrary.db in the volume section

services:
    beets-statistics:
        container_name: beets-statistics
        restart: always
        image: ghcr.io/guerda/guerda/beets-statistics:0.0.1
        volumes:
            - /home/user/.beets/musiclibrary.db:/app/musiclibrary.db:ro
        ports:
            - 8000:8000
        environment:
            - MUSICLIBRARY_DB=/app/musiclibrary.db

How to develop and/or contribute?

The development setup is pretty slim with pipenv and ruff. Installing the necessary tools via pipenv install --dev gets you along.

You can start the web server in dev mode via pipenv run dev

If you have a correction or an issue, open an issue here or contact me directly. A PR in a separater branch in your fork goes a long way, so feel free to open it up.

License

This project is licensed under GNU GPL 3.0.