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Compiling Essentia from source

Essentia depends on (at least) the following libraries:
  • FFTW: for the FFT implementation
  • libavcodec (from the FFmpeg project): for loading/saving any type of audio files (optional)
  • libsamplerate: for resampling audio (optional)
  • TagLib: for reading audio metadata tags (optional)
  • LibYAML: for YAML files input/output (optional)

Installing dependencies on Linux

You can install those dependencies on a Debian/Ubuntu system using the following command:

sudo apt-get install build-essential libyaml-dev libfftw3-dev libavcodec-dev libavformat-dev python-dev libsamplerate0-dev libtag1-dev

In order to use python bindings for the library, you might also need to install python-numpy-dev or python-numpy on Ubuntu:

sudo apt-get install python-numpy-dev python-numpy

Installing dependencies on Mac OS X

Install a scientific python environment first:

  1. install Command Line Tools for Xcode:
  2. install homebrew (package manager):
  3. install prerequisites: brew install pkg-config gcc readline sqlite gdbm freetype libpng
  4. install python: brew install python --framework
  5. install ipython and numpy: pip install ipython numpy
  6. install matplotlib: pip install matplotlib
  7. when launching ipython, use:
  1. ipython --pylab if you have matplotlib >= 1.3
  2. ipython --pylab=tk if you have matplotlib < 1.3

Note that you are advised to install python environment as described here, i.e., via homebrew and pip. You will most probably encounter installation errors when using python/numpy preinstalled with OSX 10.9.

More details can be found at

Then run:

brew install libyaml fftw ffmpeg libsamplerate libtag

Installing dependencies on Windows

Essentia does compile and run correctly on Windows, however there is no Visual Studio project readily available, so you will have to setup one yourself and compile the dependencies too. We will be working on Windows installer in the near future.

Additional dependencies (python, all platforms)

To build the documentation you will also need the following dependencies (you might need to run this command with sudo):

pip install sphinx pyparsing sphinxcontrib-doxylink docutils

Other useful dependencies:

pip install pyyaml   # make sure to have libyaml installed first

You might need to install pip before, if you are on Linux:

sudo apt-get install python-pip

Compiling Essentia

Once your dependencies are installed, you can compile Essentia (the library) by going into its directory and start by configuring it:

./waf configure --mode=release --with-python --with-cpptests --with-examples --with-vamp
Use the keys:
--with-python to enable python bindings, --with-examples to build examples based on the library, --with-vamp to build vamp plugin wrapper.

NOTE: you must always configure at least once before building!

The following will give you a list of options:

./waf --help

To compile everything you’ve configured:


To install the C++ library and the python bindings (if configured successfully; you might need to run this command with sudo):

./waf install

To run the C++ base unit tests (only test basic library behavior):

./waf run_tests

To run the python unit tests (include all unittests on algorithms, need python bindings installed first):

./waf run_python_tests

To generate the full documentation (need python bindings installed first):

./waf doc

Documentation will be located in doc/sphinxdoc/_build/html/ folder.

All built examples (including the out-of-box features extractors) will be located in build/src/examples/ folder, as well as the vamp plugin file In order to use the plugin you will need to place this file to the the standard vamp plugin folder of your system (such as /usr/local/lib/vamp/ on Linux).

Using pre-trained high-level models in Essentia

The 2.0.1 version of Essentia includes a number of pre-trained classifier models for genres, moods and instrumentation. In order to use them you need to:

Universitat Pompeu Fabra - Music Technology Group