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music library manager and MusicBrainz tagger

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beets

Beets is the media library management system for obsessive music geeks.

The purpose of beets is to get your music collection right once and for all. It catalogs your collection, automatically improving its metadata as it goes. It then provides a bouquet of tools for manipulating and accessing your music.

Here's an example of beets' brainy tag corrector doing its thing:

$ beet import ~/music/ladytron
Tagging:
    Ladytron - Witching Hour
(Similarity: 98.4%)
 * Last One Standing      -> The Last One Standing
 * Beauty                 -> Beauty*2
 * White Light Generation -> Whitelightgenerator
 * All the Way            -> All the Way...

Because beets is designed as a library, it can do almost anything you can imagine for your music collection. Via plugins, beets becomes a panacea:

If beets doesn't do what you want yet, writing your own plugin is shockingly simple if you know a little Python.

Install

You can install beets by typing pip install beets or directly from Github (see details here). Beets has also been packaged in the software repositories of several distributions. Check out the Getting Started guide for more information.

Contribute

Thank you for considering contributing to beets! Whether you're a programmer or not, you should be able to find all the info you need at CONTRIBUTING.rst.

Read More

Learn more about beets at its Web site. Follow @b33ts on Twitter for news and updates.

Contact

  • Encountered a bug you'd like to report? Check out our issue tracker!
    • If your issue hasn't already been reported, please open a new ticket and we'll be in touch with you shortly.
    • If you'd like to vote on a feature/bug, simply give a 👍 on issues you'd like to see prioritized over others.
  • Need help/support, would like to start a discussion, have an idea for a new feature, or would just like to introduce yourself to the team? Check out GitHub Discussions or Discourse!

Authors

Beets is by Adrian Sampson with a supporting cast of thousands.

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  • Python 95.9%
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