preprocessing.py

import config.config_preprocessing as config
import matplotlib.pyplot as plt
from midiocrity.dataset import MidiDataset
import os
import numpy as np
import pretty_midi as pm
import pypianoroll as pproll
import pprint
from mpl_toolkits.mplot3d import Axes3D

pp = pprint.PrettyPrinter(indent=4)

if __name__ == "__main__":
    dataset = MidiDataset(**config.midi_params, **config.general_params, **config.preprocessing_params)

    if config.preprocessing:
        print("Preprocessing dataset...")
        # set early_exit to None to preprocess entire dataset
        # dataset.preprocess_dataset_clean("./data/clean_midi", "./data/clean_midi_processed", early_exit=10)
        # dataset.count_genres(config.preprocessing_params["dataset_path"], max_genres=config.model_params["s_length"])
        # dataset.create_batches(batch_size=config.training_params["batch_size"])
        # dataset.extract_real_song_names("lmd_matched", "lmd_matched_h5", early_exit=None)

        dataset.preprocess_dataset_clean("./data/clean_midi", early_exit=100)
        # dataset.create_tensor_batches(batch_size=10)