Scripts used to convert midi files to something that text-based neural networks can understand, and vice versa.
Install python-midi, then flatten midi files with demidi.py
python3 demidi.py --mididir "/path/to/your/midis" --outdir "/path/to/output"
Run remidi.py to create a midi file that uses the same syntax
python3 remidi.py --datafile "/path/to/datafile.txt" --outfile "/path/to/outfile.mid"
Let's say you have a NoteOnEvent on Track 1 that looks like this.
NoteOnEvent(tick=8, channel=0, data=[66, 83])
It will be converted to this in text format
1NoteOnEvent8t0c66d83d
Using --include-resolution will append the resolution to the beginning of the file. This is useful if you plan to train with midis that have different resolutions (if you're not sure, try running it and see if the numbers at the beginning of the text files are different). If you don't use that option (e.g. all resolutions are the same), it's recommended you use --resolution with remidi.py when converting text back to midis.
Here are some samples that were generated using textgenrnn, using different types of songs as training data.