The next generation MusicBrainz tagger

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for metabrainz from gravatar.com metabrainz Avatar for phw from gravatar.com phw Avatar for sambhav_kothari from gravatar.com sambhav_kothari Avatar for Wieland.Hoffmann from gravatar.com Wieland.Hoffmann

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: GNU General Public License v2 or later (GPLv2+)
  • Tags MusicBrainz, metadata, tagger, picard
  • Requires: Python ~=3.7
Classifiers

Project description

MusicBrainz Picard

Github Actions Status Codacy Grade

MusicBrainz Picard is a cross-platform (Linux, macOS, Windows) audio tagging application. It is the official MusicBrainz tagger.

Picard supports the majority of audio file formats, is capable of using audio fingerprints (AcoustIDs), performing CD lookups and disc ID submissions, and it has excellent Unicode support. Additionally, there are several plugins available that extend Picard's features.

When tagging files, Picard uses an album-oriented approach. This approach allows it to utilize the MusicBrainz data as effectively as possible and correctly tag your music. For more information, see the illustrated quick start guide to tagging and the documentation.

Features

  • Multiple formats: Picard supports all popular music formats, including MP3, FLAC, OGG, M4A, WMA, WAV, and more.
  • AcoustID: Picard uses AcoustID audio fingerprints, allowing files to be identified by the actual music, even if they have no metadata.
  • Comprehensive database: Picard uses the open and community-maintained MusicBrainz database to provide accurate information about millions of music releases.
  • CD lookups: Picard can lookup entire music CDs with a click.
  • Plugin support: If you need a particular feature, you can choose from a selection of available plugins or write your own.
  • Scripting: A flexible and powerful, yet easy to learn, scripting language allows you to exactly specify how your music files will be named and how the tags will look like.
  • Cover Art: Picard can find and download the correct cover art for your albums.
  • Open Source: Picard is licensed under the GNU General Public License 2.0 or later, and is hosted on GitHub where it is actively developed.

Installation

Binary downloads are available on the Picard download page.

INSTALL.md has instructions on building this codebase.

Support and issue reporting

Please report all bugs and feature requests in the MusicBrainz issue tracker. If you need support in using Picard please read the documentation first and have a look at the MusicBrainz community forums.

Trivia

Picard is named after Captain Jean-Luc Picard from the TV series Star Trek: The Next Generation.

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for metabrainz from gravatar.com metabrainz Avatar for phw from gravatar.com phw Avatar for sambhav_kothari from gravatar.com sambhav_kothari Avatar for Wieland.Hoffmann from gravatar.com Wieland.Hoffmann

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: GNU General Public License v2 or later (GPLv2+)
  • Tags MusicBrainz, metadata, tagger, picard
  • Requires: Python ~=3.7
Classifiers

Release history Release notifications | RSS feed

2.12.3

2.12.2

2.12.1

2.12

2.11

2.11.0rc1 pre-release

2.10

2.10.0rc1 pre-release

2.9.2

2.9.1

2.9

2.9.0b3 pre-release

This version

2.9.0b2 pre-release

2.9.0b1 pre-release

2.9.0a1 pre-release

2.8.5

2.8.4

2.8.3

2.8.2

2.8.1

2.8

2.8.0rc2 pre-release

2.8.0rc1 pre-release

2.7.3

2.7.2

2.7.1

2.7

2.7.0b3 pre-release

2.7.0b2 pre-release

2.7.0b1 pre-release

2.6.4

2.6.3

2.6.2

2.6.1

2.6

2.6.0b3 pre-release

2.6.0b2 pre-release

2.6.0b1 pre-release

2.5.6

2.5.5

2.5.4

2.5.3

2.5.2

2.5.1

2.5

2.5.0b1 pre-release

2.4.4

2.4.3

2.4.2

2.4.1

2.4

2.4.0b2 pre-release

2.4.0b1 pre-release

2.3.2

2.3.1

2.3

2.3.0rc1 pre-release

2.3.0b1 pre-release

2.3.0a1 pre-release

2.2.3

2.2.2

2.2.1

2.2

2.2.0.dev4 pre-release

2.1.3

2.1.2

2.1.1

2.1

2.0.4

2.0.3

2.0.2

2.0

0.12.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

picard-2.9.0b2.tar.gz (4.8 MB view details)

Uploaded Source

Built Distributions

picard-2.9.0b2-cp311-cp311-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.11 Windows x86-64

picard-2.9.0b2-cp311-cp311-macosx_10_9_universal2.whl (2.9 MB view details)

Uploaded CPython 3.11 macOS 10.9+ universal2 (ARM64, x86-64)

picard-2.9.0b2-cp310-cp310-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.10 Windows x86-64

picard-2.9.0b2-cp310-cp310-macosx_11_0_x86_64.whl (2.9 MB view details)

Uploaded CPython 3.10 macOS 11.0+ x86-64

picard-2.9.0b2-cp39-cp39-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.9 Windows x86-64

picard-2.9.0b2-cp39-cp39-macosx_11_0_x86_64.whl (2.9 MB view details)

Uploaded CPython 3.9 macOS 11.0+ x86-64

picard-2.9.0b2-cp38-cp38-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.8 Windows x86-64

picard-2.9.0b2-cp38-cp38-macosx_10_15_x86_64.whl (2.9 MB view details)

Uploaded CPython 3.8 macOS 10.15+ x86-64

picard-2.9.0b2-cp37-cp37m-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.7m Windows x86-64

picard-2.9.0b2-cp37-cp37m-macosx_10_15_x86_64.whl (2.9 MB view details)

Uploaded CPython 3.7m macOS 10.15+ x86-64

File details

Details for the file picard-2.9.0b2.tar.gz.

File metadata

  • Download URL: picard-2.9.0b2.tar.gz
  • Upload date:
  • Size: 4.8 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.17

File hashes

Hashes for picard-2.9.0b2.tar.gz
Algorithm Hash digest
SHA256 47b6cdb83751a3b04eb8270a07b944421af33469a1fbf44f3fe2e523eed05dca
MD5 0eb772d8605c7002f0a66bd3d12d6647
BLAKE2b-256 8afc459ee5fecac8e5b8dcbba7ff164cb0c51acca6a9165d945e074f2dfba9a0

See more details on using hashes here.

File details

Details for the file picard-2.9.0b2-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for picard-2.9.0b2-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 a520bb324117e9fb0d66bb8d5adb4bc67ae4e80aa0b7ffc1262ac08991cecc84
MD5 faa3ffcf0f6ff6c6d07c78f6ac8c26ab
BLAKE2b-256 ff00d473ea918ceb2304e0387c1875930883150fb32baa541f3b9f9f4137f348

See more details on using hashes here.

File details

Details for the file picard-2.9.0b2-cp311-cp311-macosx_10_9_universal2.whl.

File metadata

File hashes

Hashes for picard-2.9.0b2-cp311-cp311-macosx_10_9_universal2.whl
Algorithm Hash digest
SHA256 7b65a8a1e347cae24713b817ced5e1d10c527638a09b37530b079575134b7d20
MD5 d42899009aa7ca6fb43f0c846ac932d4
BLAKE2b-256 b06e20737106f45c1c0537a8a717733c8b9e11cb2624d6d7d40a809c6e7cf832

See more details on using hashes here.

File details

Details for the file picard-2.9.0b2-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: picard-2.9.0b2-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 2.9 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.11

File hashes

Hashes for picard-2.9.0b2-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 854cbcfc5a2b012dcabd051cbf58ac96bd67b00a18d7d4c829bafe887801a02c
MD5 1bbebfa2faa5a97d0a90c7d9e5d8d0a6
BLAKE2b-256 799fc73642799dfd400eafdcab26ee7e5ae13026b51ef7a7b97b549c798ddcb3

See more details on using hashes here.

File details

Details for the file picard-2.9.0b2-cp310-cp310-macosx_11_0_x86_64.whl.

File metadata

File hashes

Hashes for picard-2.9.0b2-cp310-cp310-macosx_11_0_x86_64.whl
Algorithm Hash digest
SHA256 0b0553c308317aaafee4306e088ba029b7e9c405f41c6422f47f6925c1509d9d
MD5 32d3b1e5c937eadb8d8d2bc16df7605b
BLAKE2b-256 6f57634c3eba196e4f8144acb14de14ccea9eb2c95d711dae15dcfee632c87cd

See more details on using hashes here.

File details

Details for the file picard-2.9.0b2-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: picard-2.9.0b2-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 2.9 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.13

File hashes

Hashes for picard-2.9.0b2-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 035dc255116efb4a03de405c06ef7fb9335cd6fc580ffbe461a8d2cfb5ed82ee
MD5 28732f9ae2b3b3ccbc6472828ecbc9b0
BLAKE2b-256 73d410537c7cefe3f0a853e3d9fa818954452b70f633ca2c6c7ebe74196a9676

See more details on using hashes here.

File details

Details for the file picard-2.9.0b2-cp39-cp39-macosx_11_0_x86_64.whl.

File metadata

File hashes

Hashes for picard-2.9.0b2-cp39-cp39-macosx_11_0_x86_64.whl
Algorithm Hash digest
SHA256 c6f0582b5ea135bdd4aaa093eed3cad2e93714f05131d81dfb199bf28f6950b4
MD5 4e35e13ac5484ad35e79906f87627a4d
BLAKE2b-256 1beb55d8631cbaa3a8695a4284bb409941ca30d7b2dcda4de1c58c3bd146abd9

See more details on using hashes here.

File details

Details for the file picard-2.9.0b2-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: picard-2.9.0b2-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 2.9 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.8.10

File hashes

Hashes for picard-2.9.0b2-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 185babc85e5e742d227b020c7d4907297f5fac3a3e915607056a4b515257e5d0
MD5 826e05c36c8d8ee6ae6ac1686ea34249
BLAKE2b-256 89a34c7149fffed9a417c59027c883d80c04132af78f6c050fcf7f09b7d8c543

See more details on using hashes here.

File details

Details for the file picard-2.9.0b2-cp38-cp38-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for picard-2.9.0b2-cp38-cp38-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 d431ee7bd42e1c825575d3de9bc810d6b334cd134772a502e0a5dd929b37565c
MD5 4eb00c5191c37c133f571b410abb248e
BLAKE2b-256 883ce8823d99fea8f2c4d1bea6b0c0495a8190c1ed807c1af11fe6c3685b8409

See more details on using hashes here.

File details

Details for the file picard-2.9.0b2-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: picard-2.9.0b2-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 2.9 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.7.9

File hashes

Hashes for picard-2.9.0b2-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 03c96d1a138a2bb08791d05a647267b809a34ec678f52e0286cdf88873456d62
MD5 82f999ea389df1ef941e4592062233ad
BLAKE2b-256 35780ea7184eb530403ea88110cc5ad95a8f41f609f8feb93e2458d9217ac216

See more details on using hashes here.

File details

Details for the file picard-2.9.0b2-cp37-cp37m-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for picard-2.9.0b2-cp37-cp37m-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 577bc529cefc2ad89c0fa948b59fa98547f311055f847d227166a76930369721
MD5 2d5f1ec74d5899af53cc043700c520c6
BLAKE2b-256 2af0ccb7ad9891894f016d141f88b4d868ef64d5c219565adbc621b634e33bea

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page