An algorithmic composition that generates polyphonic phrases during the training of an LSTM neural network. Uses performer-controlled time stretching to accent the gradual transition from chaotic noise to tonal harmony.
- Google's Magenta development environement installed in a virtual env
- Folder of MusicXML or MIDI files to train on. I used this archive of Bach Chorales
- Python MIDO package(
pip install mido)
Scripts for converting input music files to tfrecords, training model, and saving checkpoints
Script for generating new phrases and Python code for maintaining phrase queue, interpretting control changes and sending MIDI output to Logic
Logic project for piece, set up to control effects from MIDI controller
You'll first need to train up a model to generate the checkpoints for the piece by running run_magenta.sh. You'll also need to set up save_checkpoint.sh to run at a regularly scheduled interval(I chose half an hour) using Cron on Linux. At the end of training, you should have a folder of checkpoint folders representing the network at various stages during training.
For a performance, you'll need a MIDI controller hooked up to your machine. Open the logic project rnn_logicproject.logicx, which has control change messages mapped to various effects. Then, start the piece by running performance/send_midi.py. You should see the MIDI output of the algorithm showing up in Logic. In addition to controlling effects, you can use the MIDI controller to control how fast the piece moves through the checkpoints