A neural network for generating drum tracks for songs.
Practical project for the COMP6590: Computational Creativity module.
The code can be run from the Jupyter notebook file main.ipynb.
You will need TensorFlow installed in your Python environment and
also PrettyMIDI, Mido, numpy, and matplotlib.
Please use requirements.txt or the following command:
pip install pretty_midi mido numpy tensorflow matplotlib
Each cell in the notebook should be executed consecutively with the
exception of the nn.train() and nn.plot() cells (which are for training
the network if you wish.) The model saves its weights to the
/saved/ directory and can be loaded in the cell nn.load().
Feel free to modify the INPUT_PATH in the final cell to point to a MIDI
file of your choosing. You can also modify the cut-off parameter of the
tokeniser.add_drum_track() within the same cell to adjust the sensitivity
of the result. You should find the output as a file named combined.mid.
In order for the model to learn, you will require a collection of MIDI files
containing drum tracks.
During development, I used the "Lakh MIDI Dataset Clean", available
here. Once the MIDI files have been
pre-processed, the original files are no longer needed. The result of the
pre-processing is stored in a file named saved.txt.
Note: You will need a fair amount of memory to load the neural network and the inputs from the saved file.