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INSTALL.md

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Installing AcousticBrainz Server

AcousticBrainz Server consists of two parts: web server that powers acousticbrainz.org website and high-level data extractor that processes incoming low-level information about recordings.

Prerequisites

For example in the latest Ubuntu, this command will install pre-requisites:

$ sudo apt-get install python-dev python-virtualenv memcached pxz \
    postgresql-9.3 postgresql-client-9.3 postgresql-server-dev-9.3

Web Server

It is recommended, although optional, to first set up a virtual environment and activate it:

$ virtualenv venv
$ . ./venv/bin/activate

Python dependencies

Then use pip to install the required Python dependencies:

$ pip install -r requirements.txt

Configuration

Copy over config.py.sample to config.py and edit its content to fit your environment.

Configuring MusicBrainz OAuth

You need to create a new application and set two variables: MUSICBRAINZ_CLIENT_ID and MUSICBRAINZ_CLIENT_SECRET. On MusicBrainz set Callback URL to http://<host>/login/musicbrainz/post.

Creating the database

After you tweak configuration file, database needs to be created:

$ python manage.py init_db

Optional: You might want to create a database that will be used by tests:

$ python manage.py init_test_db

Node.js dependencies

Node.js dependencies are managed using npm. To install these dependencies run the following command:

$ npm install

Building static files

We use Gulp as our JavaScript/CSS build system. It should be installed with node.js dependencies. Calling gulp on its own will build everything necessary to access the server in a web browser:

./node_modules/.bin/gulp

Keep in mind that you'll need to rebuild static files after you modify JavaScript or CSS.

Starting the server

After all this, you can run the site/server using ./server.py. Use ./server.py -h to get a list of command-line switches to further suit your local environment (e.g., port, listening address, ...).

High-level Data Extractor

To run high-level data extractor you'll need two things:

  1. streaming_extractor_music_svm which is a part of the Essentia library
  2. svm_models which are available at http://essentia.upf.edu/documentation/svm_models/

Building streaming_extractor_music_svm

To build the extractor binary you'll need to get Gaia and Essentia libraries. Generally, you should use installation instructions provided with them, but to save you some time here's a set of steps that you can follow:

  1. git clone Gaia and install it without using additional options during the configuration step

  2. git clone Essentia and configure it like this:

     $ ./waf configure --mode=release --with-gaia --with-example=streaming_extractor_music_svm

  3. Build Essentia and copy streaming_extractor_music_svm binary from build/src/examples/ directory into ./hl_extractor/ in the project root.

Installing SVM models

Download archive from http://essentia.upf.edu/documentation/svm_models/, extract it, and move contents of svm_models directory into /hl_extractor/svm_models in the project root.

Starting the extractor

$ cd high-level
$ python hl_calc.py