This code is written in Python, and depends on having Theano and theano-lstm (which can be installed with pip) installed. The bare minimum you should need to do to get everything running, assuming you have Python, is

sudo pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
sudo pip install numpy scipy theano-lstm python-midi

In addition, the included setup scripts should set up the environment exactly as it was when I trained the network on an Amazon EC2 g2.2xlarge instance with an external EBS volume. Installing it with other setups will likely be slightly different.

First, you will need to obtain a large selection of midi music, preferably in 4/4 time, with notes correctly aligned to beats. These can be placed in a directory "music".

To use the model, you need to first create an instance of the Model class:

import model
m = model.Model([300,300],[100,50], dropout=0.5)

where the numbers are the sizes of the hidden layers in the two parts of the network architecture. This will take a while, as this is where Theano will compile its optimized functions.

Next, you need to load in the data:

import multi_training
pcs = multi_training.loadPieces("music")

Then, after creating an "output" directory for trained samples, you can start training:

multi_training.trainPiece(m, pcs, 10000)

This will train using 10000 batches of 10 eight-measure segments at a time, and output a sampled output and the learned parameters every 500 iterations.

Finally, you can generate a full composition after training is complete. The function gen_adaptive in main.py will generate a piece and also prevent long empty gaps by increasing note probabilities if the network stops playing for too long.

gen_adaptive(m,pcs,10,name="composition")