Genretron aims to be an automatic music genre classifier. That means that given any song (even one that you just made) it will try to classify it in one of its learned genres. It uses Deep Learning techniques to learn the genre of the songs.
Genretron is part of Carmine Paolino's Master Thesis at Universiteit van Amsterdam.
Install Python 2.7+ with virtualenv, HDF5, libsndfile, freetype, libpng, a fortran compiler and postgresql.
On a Mac with Homebrew:
brew install python libsndfile homebrew/science/hdf5 freetype libpng gcc postgresql homebrew/python/matplotlib homebrew/python/numpy homebrew/python/scipy
pip install virtualenv
On Ubuntu 14.04 LTS:
sudo apt-get install libsndfile1-dev python-virtualenv python-tables python-matplotlib python-scikits-learn python-sqlalchemy python-psycopg2 python-yaml python-numpy python-scipy python-progressbar procmail
On Fedora 24:
sudo dnf install gcc-c++ libsndfile-devel python2-virtualenv python-tables python2-matplotlib python2-scikit-learn python-sqlalchemy python-psycopg2 PyYAML python2-numpy python2-scipy python-progressbar Cython procmail
git clone --recursive https://github.com/crmne/Genretron.git
cd Genretron
virtualenv --system-site-packages env
. env/bin/activate
python setup.py install
Make sure you set the environment variable PYLEARN2_DATA_PATH to a directory of your preference where you want the datasets to be stored. For example:
export PYLEARN2_DATA_PATH=/data/lisa/data
For your own convenience, you might want to add that line to your .bashrc
Then simply run
bin/download_gtzan
This procedure will download the classic Music Information Retrieval Genre Classification dataset GTZAN and unpack it in your PYLEARN2_DATA_PATH directory so it's ready for use with Genretron.
If you wanna download MNIST, run:
python bin/download_mnist
bin/train experiments/test/conv.yaml
Set the GENRETRON_PATH, DB_TABLE and THEANO_FLAGS environment variables. For example:
export GENRETRON_PATH=$HOME/Genretron
export DB_TABLE='postgres://user[:pass]@host/dbname?table=tablename'
export THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32
Then schedule the jobs with jobman:
bin/jobman_sqlschedule experiments/jobman/hyperparameters/experiment_name.yaml $DB_TABLE tablename
bin/jobman sql -n [number of jobs] $DB_TABLE $GENRETRON_PATH/resuts
sbatch -t [time in minutes] -p [cluster partition] -n [number of processes] -N [number of nodes] bin/launcher.slurm 1 $DB_TABLE
for i in {1..[number of jobs]}; do qsub -lwalltime=[time in hh:mm:ss] -v CONSECUTIVE_JOBS=1,DB_TABLE=$DB_TABLE bin/launcher.pbs; done
bin/predict results/experiments/test/conv.pkl song1.wav song2.wav ...