163 lines
6.8 KiB
Python
163 lines
6.8 KiB
Python
#!/usr/bin/env python3
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import settings
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import pypianoroll as roll
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import matplotlib.pyplot as plt
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import numpy as np
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import os
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from tqdm import tqdm
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from math import floor
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import sys
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import pickle
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from tqdm import tqdm
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from tqdm import trange
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from collections import defaultdict
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import bz2
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import pickle
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def to_samples(midi_file_path, midi_res=settings.midi_resolution):
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# TODO: add transpositions of every sample to every possible key transposition
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# np.roll(sample, pitch_interval, axis=1) for transposition
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# np.roll(sample, time_steps, axis=0) for time shifting
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all_X_samples = []
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all_y_samples = []
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for track in roll.Multitrack(midi_file_path).tracks:
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if not track.is_drum:
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# TODO: this makes rollable samples and dataset of y_train for prdiction
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# the idea is to predict next N timesteps from prevous M timesteps
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m_timesteps = 96
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n_next_notes = 4
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track_timesteps = track.pianoroll.shape[0] - (m_timesteps + n_next_notes)
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X_track_samples = []
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y_track_samples = []
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for i in range(track_timesteps):
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X = track.pianoroll[i : i + m_timesteps].reshape(96,128)
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y = track.pianoroll[i + m_timesteps : i + m_timesteps + n_next_notes].reshape(4,128)
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X_track_samples.append(X)
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y_track_samples.append(y)
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all_X_samples.extend(X_track_samples)
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all_y_samples.extend(y_track_samples)
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else:
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# TODO: add code for drums samples
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pass
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return all_X_samples, all_y_samples
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def to_samples_by_instrument(midi_file_path, midi_res=settings.midi_resolution):
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# add transpositions of every sample to every possible key transposition
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# np.roll(sample, pitch_interval, axis=1) for transposition
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# np.roll(sample, time_steps, axis=0) for time shifting
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# TODO: make rollable samples with train_Y set
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fill_empty_array = lambda : [ np.empty((0, 96, 128)) , np.empty((0, 1, 128)) ]
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samples_by_instrument = defaultdict(fill_empty_array)
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all_beats = np.empty((0, 96, 128))
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for track in roll.Multitrack(midi_file_path).tracks:
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if not track.is_drum:
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key = track.program + 1
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# TODO: this makes pack of samples of N x 96 x 128 shape
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# number_of_beats = floor(track.pianoroll.shape[0] / midi_res)
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# track_pianoroll = track.pianoroll[: number_of_beats * midi_res]
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# track_beats = track_pianoroll.reshape(number_of_beats, midi_res, 128)
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# TODO: this makes rollable samples and dataset of y_train for prdiction
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# the idea is to predict next n notes from prevous m timesteps
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m_timesteps = 96
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n_next_notes = 4
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for i, value in tqdm(enumerate(track.pianoroll[:-(m_timesteps + n_next_notes)])):
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X = track.pianoroll[i : i + m_timesteps].reshape(1,96,128)
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y = track.pianoroll[i + m_timesteps : i + m_timesteps + n_next_notes].reshape(1,1,128)
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samples_by_instrument[key][0] = np.concatenate([X, samples_by_instrument[ key ][0]], axis=0)
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samples_by_instrument[key][1] = np.concatenate([y, samples_by_instrument[ key ][1]], axis=0)
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# samples_by_instrument[track.program + 1][0] = np.concatenate([track_beats, samples_by_instrument[ track.program + 1]], axis=0)
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else:
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# TODO: add code for drums samples
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pass
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return samples_by_instrument
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def to_midi(samples, output_path=settings.generated_midi_path, program=0, tempo=120, beat_resolution=settings.beat_resolution):
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tracks = [roll.Track(samples, program=program)]
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return_midi = roll.Multitrack(tracks=tracks, tempo=tempo, downbeat=[0, 96, 192, 288], beat_resolution=beat_resolution)
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roll.write(return_midi, output_path)
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# todo: this function is running too slow.
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def delete_empty_samples(sample_pack):
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print('Deleting empty samples...')
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temp_sample_pack = sample_pack
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index_manipulator = 1
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for index, sample in enumerate(sample_pack):
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if sample.sum() == 0:
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temp_sample_pack = np.delete(temp_sample_pack, index-index_manipulator, axis=0)
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index_manipulator = index_manipulator + 1
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print('Deleted {} empty samples'.format(index_manipulator-1))
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return temp_sample_pack
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def main():
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print('Exporting...')
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# from collections import defaultdict
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# fill_empty_array = lambda : [ np.empty((0, 96, 128)) , np.empty((0, 1, 128)) ]
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# samples_pack_by_instrument = defaultdict(fill_empty_array)
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# sample_pack = np.empty((0,settings.midi_resolution,128))
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X_train = []
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y_train = []
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for midi_file in tqdm(os.listdir(settings.midi_dir)):
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print(midi_file)
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midi_file_path = '{}/{}'.format(settings.midi_dir, midi_file)
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X, y = to_samples(midi_file_path)
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# if midi_samples is None:
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# continue
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X_train.extend(X)
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y_train.extend(y)
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# this is for intrument separation
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# for key, value in midi_samples.items():
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# samples_pack_by_instrument[key][0] = np.concatenate((samples_pack_by_instrument[key][0], value[0]), axis=0)
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# samples_pack_by_instrument[key][1] = np.concatenate((samples_pack_by_instrument[key][1], value[1]), axis=0)
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# TODO: Delete empty samples
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# sample_pack = delete_empty_samples(sample_pack)
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# save as compressed pickle (sample-dictionary)
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# sfile = bz2.BZ2File('data/samples.pickle', 'w')
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# pickle.dump(dict(samples_pack_by_instrument), sfile)
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# this is for intrument separation
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# print('Saving...')
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# for key, value in tqdm(samples_pack_by_instrument.items()):
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# np.savez_compressed('data/samples/X_{}.npz'.format(settings.midi_program[key][0]), value)
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# np.savez_compressed('data/samples/y_{}.npz'.format(settings.midi_program[key][1]), value)
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# this if for one big list
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print('Saving...')
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np_X_train = np.array(X_train)
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np_y_train = np.array(y_train)
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print(np_X_train.shape, np_y_train.shape)
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np.savez_compressed('data/samples/X_{}.npz'.format(1), np_X_train)
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np.savez_compressed('data/samples/y_{}.npz'.format(1), np_y_train)
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# Give a preview of what samples looks like
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# fig, axes = plt.subplots(nrows=10, ncols=10, figsize=(20, 20))
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# for idx, ax in enumerate(axes.ravel()):
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# n = np.random.randint(0, value[0].shape[0])
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# sample = value[n]
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# ax.imshow(sample, cmap = plt.get_cmap('gray'))
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# plt.savefig('data/samples/{}.png'.format(settings.midi_program[key]))
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print('Exported {} samples'.format(np_X_train.shape[0]))
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print('Done!')
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if __name__ == '__main__':
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main()
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