A project that trains a LSTM recurrent neural network over a data-set of MIDI files.

Numpy (http://www.numpy.org/)

Tensorflow 0.8 (https://github.com/tensorflow/tensorflow) its best to uses virtual env to do this

virtual env (https://virtualenv.pypa.io/en/stable/)

Python Midi (https://github.com/vishnubob/python-midi.git)

Mingus (https://github.com/bspaans/python-mingus)

Matplotlib (https://github.com/matplotlib/matplotlib)

1. mkdir data && mkdir models
2. run python main.py. This will collect the data, create the chord mapping file in data/nottingham.pickle, and train the model
3. Run python rnn_sample.py --config_file new_config_file.config to generate a new MIDI song.

Give it 1-2 hours to train on your local machine, then generate the new song. You don't have to wait for it to finish, just wait until you see the 'saving model' message in terminal. To increase speed you can use a cloud based GPU such as www.fomoro.com

Credit for the vast majority of code here goes to Yoav Zimmerman. My only addition was a code wrapper. The main use of this project is as a proof of concept for RNN.