Server Components for the AcousticBrainz project
The AcousticBrainz project aims to crowd source acoustic information for all music in the world and to make it available to the public. This acoustic information describes the acoustic characteristics of music and includes low-level spectral information and information for genres, moods, keys, scales and much more. The goal of AcousticBrainz is to provide music technology researchers and open source hackers with a massive database of information about music.
AcousticBrainz organizes the data on a recording basis, indexed by the MusicBrainz ID for recordings. If you know the MBID for a recording, you can easily fetch from AcousticBrainz. For details on how to do this, visit our API documentation.
Changes and other important announcements about the AcousticBrainz project will be announced on our blog.
If you are interested in helping out, consider donating to the MetaBrainz Foundation.
If you are interested in contributing code or documentation, please have a look at the issue tracker or come visit us in the #metabrainz IRC channel on irc.libera.chat.
Installation and Running
Copy the following two configuration files:
profile.conf.inyou need to set the
models_essentia_git_shavalue. Unless you know what you are doing, this value should be v2.1_beta1
For convenience, we provide a script
develop.sh which calls
docker-compose. We also have some additional
subcommands for commonly used commands. Some of these subcommands take no arguments:
./develop.sh bash # open a bash shell in a new container in the webserver service ./develop.sh psql # run psql, connecting to the database ./develop.sh shell # run a flask shell in ipython
And some subcommands take arguments, passing them to the underlying program:
./develop.sh npm # run npm in a new container in the webserver service ./develop.sh manage # run python manage.py in a new container in the webserver service ./develop.sh ... # run docker-compose
If you want to run
docker-compose yourself you are welcome to do so, however keep in
mind that we call it in the following way, to standardise the project name:
docker-compose -f docker/docker-compose.dev.yml -p acousticbrainz-server <args>
Build and initial configuration
Build the docker containers needed for AcousticBrainz by running the following:
Start the webserver and other required services with:
The first time you install AcousticBrainz, you will need to initialize the AcousticBrainz database. Run in a separate terminal:
./develop.sh manage init_db
You will be able to view your local AcousticBrainz server at http://localhost:8080
In order to load a psql session, use the following command:
Building static files
First-time npm setup
For development, the first time that you install acousticbrainz you must install node packages in your local directory.
./develop.sh npm install
This has the effect of creating a
node_modules directory in your local code checkout.
./develop.sh npm run build:dev
./develop.sh npm run build:watch
To use the dataset tools you need to configure OAuth with MusicBrainz. Log in to your MusicBrainz account (or create one if needed) and create a new application.
Choose a name (for example, "AcousticBrainz development"), set Type to "Web Application" and set the Callback URL to http://localhost:8080/login/musicbrainz/post
Copy the OAuth Client ID and OAuth Client Secret values to
You should now be able to use the menu in the top corner of your AcousticBrainz server to log in.
Once you have logged in, you can make your user an admin, by running
./develop.sh manage add_admin <your user>
You should now be able to access the admin section at http://localhost:8080/admin
Working with data
Before you import or export data, make sure you understand how docker bind mounts work. The following commands will work if you specify paths in the current directory, but if you want to specify paths somewhere else (e.g. a Downloads or tmp directory) you must specify an additional
AcousticBrainz provides data dumps that you can import into your own server. Latest database dump is available at http://acousticbrainz.org/download. You need to download full database dump from this page and use it during database initialization:
./develop.sh run --rm webserver python2 manage.py init_db path_to_the_archive
you can also easily remove existing database before initialization using
./develop.sh run --rm webserver python2 manage.py init_db --force path_to_the_archive
or import archive after database is created:
./develop.sh run --rm webserver python2 manage.py import_data path_to_the_archive
You can also import dumps that you created yourself. This process is described
dump full_db command).
There are several ways to export data out of AcousticBrainz server. You can create full database dump or export only low-level and high-level data in JSON format. Both ways support incremental dumping.
Full database dump:
./develop.sh manage dump full_db
./develop.sh manage dump json
Creates two separate full JSON dumps with low-level and high-level data.
./develop.sh manage dump incremental
Creates new incremental dump in three different formats: usual database dump, low-level and high-level JSON.
Previous incremental dumps:
./develop.sh manage dump incremental --id 42
Same as another one, but recreates previously created incremental dump.
Test your changes with unit tests
Unit tests are an important part of AcousticBrainz. It helps make it easier for developers to test changes and help prevent easily avoidable mistakes later on. Before commiting new code or making a pull request, run the unit tests on your code.
This will start a set of docker containers separate from your development environment, run the tests, and then stop and remove the containers. To run tests more rapidly without having to bring up and take down containers all the time, you can run each step individually. To bring up containers in the background:
Then run your tests when you need with:
./test.sh [optional arguments to pass to py.test]
Stop the test containers with:
This will stop but not delete the containers. You can delete the containers with:
We use the
-p flag to
docker-compose to start the test containers as a new
acousticbrainztest so that containers don't conflict with
already running development containers. You can access containers directly
while they are running (e.g. with
docker exec) with this name (e.g.
The database has no separate volume for data, this means that any data
in the test database will disappear when the containers are
deleted (at the end of standalone
./test.sh, or after
We forward the port from postgres to
localhost:15431, so you can connect to it
psql on your host if you want to inspect the contents of the database.