This project allows you to train LSTM-based neural network models of jazz music in the .ls format, as well as convert the models into connectome files for use in Impro-Visor.

In order to run this project, you will need to install Python 3.5 (or later). You will also need the libraries theano, theano-lstm, sexpdata, and matplotlib, which you can install with

pip3 install theano theano-lstm sexpdata matplotlib

Note that Theano depends on SciPy. If you do not already have SciPy, pip3 should install SciPy automatically when you install Theano, but if that fails, you can download SciPy from their website. Alternately, you can install a Python 3.5 distribution that already has SciPy installed, such as Anaconda.

Before using the scripts, you will also need to make a file called .theanorc in your home directory, with the following contents:

[global]
floatX=float32

[mode]=FAST_RUN

For additional Theano configuration options, including instructions on how to use the GPU, see the theano config documentation.

Python scripts are provided to accomplish certain tasks. Each script can be invoked using the python3 executable, for example

python3 SCRIPTNAME.py [arguments]

• main.py: The main entry point for the project. Trains different types of model, as well as generating samples from them.
• param_cvt.py: Converts trained connectomes from pickle format (.p) to Impro-Visor connectome format (.ctome)
• plot_internal_state.py: Plots the internal state of a network, produced by main.py in generation mode.
• plot_data.py: Plots a .csv file as a graph, allowing you to visualize the training loss of a network
• lscat.py: Concatenates leadsheets together for easier viewing.
• lssplit.py: Splits leadsheets into multiple pieces.
• generate_trade_helper.py: Interleaves generated output with the original input in a single leadsheet, for use in autoencoder models.

Detailed instruction pages for each script are available in the instructions subdirectory, and each script will display a help message if the script is given the -h argument.

Some general examples follow. See detailed instruction pages for more in-depth examples.

Train a product-of-experts generative model on a directory of leadsheet fileswith path datasets/my_dataset, automatically resuming training if previously interrupted. By default, each leadsheet file will be split into 4-bar chunks starting at each bar.

$ python3 main.py --dataset datasets/my_dataset --outputdir output_my_dataset --resume_auto poex

Generate some leadsheets using the trained product-of-experts model, sampling from the dataset:

$ python3 main.py --dataset datasets/my_dataset --outputdir output_my_dataset/generated --resume 0 output_my_dataset/final_params.p --generate poex

Visualize the internal state of the network for the first generated leadsheet:

$ python3 plot_internal_state.py output_my_dataset/generated 0

Plot the training progress of the network:

$ python3 plot_data.py output_my_dataset/data.csv

Convert the trained network into a connectome file:

$ python3 param_cvt.py --keys param_keys/poex_keys.txt output_my_dataset/final_params.p

Train a compressing autoencoder on the same dataset, using fixed features and product-of-experts for compatibility with Impro-Visor:

$ python3 main.py --dataset datasets/my_dataset --outputdir output_my_dataset_compae --resume_auto compae poex queueless_std --feature_period 24 --add_loss

Run the autoencoder on some leadsheets from the dataset, and then combine the input and output into a trading summary leadsheet:

$ python3 main.py --dataset datasets/my_dataset --outputdir output_my_dataset_compae/generated --resume 0 output_my_dataset_compae/final_params.p --generate compae poex queueless_std --feature_period 24 --add_loss
$ python3 generate_trade_helper.py output_my_dataset_compae/generated

Convert the trained autoencoder into a connectome file:

$ python3 param_cvt.py --keys param_keys/ae_poex_keys.txt output_my_dataset_compae/final_params.p