Repo with original and forked (from pretty_midi) scripts for easy midi preprocessing for deep learning.
The purpose of the scripts (write_midi_as_np.py and generate_training_samples.py) are to encode midi files as numpy arrays and to generate arbitrary number of note sequences of any length from the midi files encoded as numpy arrays, respectively. Therefore, you must run write_midi_as_np.py on your folder of midi files before you can run generate_training_samples.py.
Simply clone this repo to your local computer, but you must also install all dependencies of pretty_midi: numpy, mido, and six.
Use this to encode midi files into numpy arrays. During the process we will only retain the following information: note start time, end time, and pitch.
The encoded file will have a shape of (num_tracks, ) where num_tracks refers to the number of tracks present in the original midi file. However, each track will also be a numpy array of shape (num_notes, 3) where num_notes refers to the number of notes present in the track. The number 3 refers to the information we retain from the original midi file: start time, end time, pitch.
You must specify source_filepath, target_filepath, target_key, and max_n_files as arguments to run the file.
source_filepath is the path to the FOLDER containing midi files.
target_filepath is the path to the FOLDER where you want the np arrays to be.
target_key is the key you want the songs to be converted to, and must be a number between 0 and 11. If you do not wish to convert keys, then pass None.
max_n_files is the max number of files you want processed. If you wish to process all files in the folder, then pass None.
Example: python3 write_midi_as_np.py midi_files midi_np_files None None
Note: write_midi_as_np_v2.py will write duration as (start time, length) as opposed to (start time, end time).
Run this script to generate note sequence samples for deep learning, with pitches being converted into one-hot. You can specify how long each note sequence should be (sample_length) and how many samples to generate (n_samples). The output will have shape (n_samples, sample_length, 130) where 130 refers to start time and end time, plus the one-hot encoded pitches (length of 128). You also need to specify whether the numpy arrays for duration are written as (start time, end time), in which case add True as the last argument, or False if written as (start time, length).
Example: python3 generate_training_samples.py midi_np_files training_samples 20 50000 True