lstm - drop this branch, looking for other way to generate music
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@ -1,26 +1,49 @@
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#!/usr/bin/env python3
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import numpy as np
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import midi
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import tensorflow as tf
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from keras.layers import Input, Dense, Conv2D
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from keras.models import Model
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from tensorflow.keras import layers
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from keras.layers import Input, Dense, Conv2D, Flatten, LSTM, Dropout, TimeDistributed, RepeatVector
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from keras.models import Model, Sequential
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import matplotlib.pyplot as plt
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import settings
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import pickle
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import sys
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#model
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input_shape = settings.midi_resolution*128
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input_img = tf.keras.layers.Input(shape=(input_shape,))
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encoded = tf.keras.layers.Dense(160, activation='relu')(input_img)
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decoded = tf.keras.layers.Dense(input_shape, activation='sigmoid')(encoded)
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autoencoder = tf.keras.models.Model(input_img, decoded)
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trained_model_path = sys.argv[1]
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output_path = sys.argv[2]
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# treshold = float(sys.argv[3])
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autoencoder.compile(optimizer='adadelta',
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loss='categorical_crossentropy',
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metrics=['accuracy'])
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#random seed
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generate_seed = np.random.rand(12288).reshape(1,96,128)
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# load weights into new model
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autoencoder.load_weights(settings.model_path)
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print("Loaded model from {}".format(settings.model_path))
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# load and predict
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model = pickle.load(open(trained_model_path, 'rb'))
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# generate_seed = np.random.rand(12288).reshape(1,12288)
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generate_seed = np.load(settings.samples_path)['arr_0'][15].reshape(1,12288)
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generated_music = np.empty((0,128))
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generated_sample = autoencoder.predict(generate_seed)
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np.savez_compressed(settings.generated_sample_path, generated_sample)
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for note in range(100):
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generated_vector = model.predict(generate_seed).reshape(1,4,128)
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generated_notes = np.zeros((4,128))
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for i, col in enumerate(generated_vector[0]):
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best_note = np.argmax(col)
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generated_notes[i][best_note] = 1
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generate_seed = np.concatenate([generated_notes, generate_seed[0][:-4]]).reshape(1,96,128)
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generated_music = np.concatenate([generated_music, generated_notes])
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# generated_sample = generated_sample.reshape(96,128)
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generated_sample = generated_music
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# print(generated_music)
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# binarize generated music
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# generated_sample = generated_sample > 0 * generated_sample.max()
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#save to midi
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midi.to_midi(generated_sample, output_path='{}.mid'.format(output_path) )
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#save piano roll to png
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plt.imshow(generated_sample, cmap = plt.get_cmap('gray'))
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plt.savefig('{}.png'.format(output_path))
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144
project/midi.py
144
project/midi.py
@ -8,36 +8,86 @@ 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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# this function export a samples from midi file:
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# and for every track in midi file chopped pianoroll
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# for a samples of given beat_lenth (midi_res)
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# every track is single line
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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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print('Exporting samples from: {}'.format(midi_file_path))
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track_timesteps = track.pianoroll.shape[0] - (m_timesteps + n_next_notes)
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all_beats = np.empty((0, settings.midi_resolution, 128))
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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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print('Track: {}'.format(track.name))
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if not track.is_drum:
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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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all_beats = np.concatenate([track_beats, all_beats], axis=0)
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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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print('Exported {} samples of {}'.format(number_of_beats, settings.midi_program[track.program]))
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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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# add code for drums samples
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# TODO: add code for drums samples
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pass
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return all_beats
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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, settings.generated_midi_path)
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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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@ -52,31 +102,61 @@ def delete_empty_samples(sample_pack):
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return temp_sample_pack
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def main():
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print('Exporting...')
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if sys.argv[1]=='export':
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print('Exporting started...')
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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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# 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 os.listdir(settings.midi_dir):
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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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midi_samples = to_samples(midi_file_path)
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if midi_samples is None:
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continue
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sample_pack = np.concatenate((midi_samples, sample_pack), axis=0)
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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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# I commented out this line, because it was too slow
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# TODO: Delete empty samples
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# sample_pack = delete_empty_samples(sample_pack)
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np.savez_compressed(settings.samples_dir, sample_pack)
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print('Exported {} samples'.format(sample_pack.shape[0]))
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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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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, sample_pack.shape[0])
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sample = sample_pack[n]
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ax.imshow(sample, cmap = plt.get_cmap('gray'))
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plt.savefig(settings.sample_preview_path)
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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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@ -1,16 +1,28 @@
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## MUSIC GENERATION USING DEEP LEARNING ##
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## AUTHOR: CEZARY PUKOWNIK
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files:
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- midi.py - code for data extraction, and midi convertion
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- train.py - code for model definition, and training session
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- generate.py - code for model loading, predicting ang saving to midi_dir
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- settings.py - file where deafult settings are stored
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- readme - this file
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- data/midi - directory where input midi are stored
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- data/models - directory where trained models are stored
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- data/output - directory where generated music is stored
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- data/samples - directory where extracted data from midi is stored
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- data/samples.npz - deprecated
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How to use:
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1. Use midi.py to export data from midi files
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./midi.py export <midi_folder_path> <output_path>
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./midi.py <midi_folder_path> <output_path>
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2. Use train.py to train a model (this can take a while)
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./train.py <input_training_data> <model_save_path>
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./train.py <input_training_data> <model_save_path> <epochs>
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3. Use generate.py to generate music from trained models
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./generate.py <model_weights_path> <output_path>
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./generate.py <trained_model_path> <output_path> <treshold>
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@ -1,18 +0,0 @@
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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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import settings
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instruments = np.load(settings.generated_sample_path)['arr_0'][0]
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instruments = instruments.reshape(96,128)
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# instruments = instruments>0.5
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instruments = instruments*255
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i = roll.Track(instruments, program=0)
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generated_midi = roll.Multitrack(tracks=[i], tempo=120.0, downbeat=[0, 96, 192, 288], beat_resolution=24)
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roll.write(generated_midi, settings.generated_midi_path)
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plt.imshow(instruments.T, cmap='gray')
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plt.savefig(settings.generated_pianoroll_path)
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@ -15,11 +15,11 @@ beats_per_sample = 1
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ignore_note_lenght = False
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#train_settings
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epochs = 1000
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epochs = 1
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#extras
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midi_program = {
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0 : 'Perc',
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# Piano
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1 : 'Acoustic Grand Piano',
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2 : 'Bright Acoustic Piano',
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3 : 'Electric Grand Piano',
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@ -28,6 +28,7 @@ midi_program = {
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6 : 'Electric Piano 2',
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7 : 'Harpsichord',
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8 : 'Clavi',
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# Chromatic Percussion
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9 : 'Celesta',
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10 : 'Glockenspiel',
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11 : 'Music Box',
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@ -36,6 +37,7 @@ midi_program = {
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14 : 'Xylophone',
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15 : 'Tubular Bells',
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16 : 'Dulcimer',
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# Organ
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17 : 'Drawbar Organ',
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18 : 'Percussive Organ',
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19 : 'Rock Organ',
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@ -44,6 +46,7 @@ midi_program = {
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22 : 'Accordion',
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23 : 'Harmonica',
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24 : 'Tango Accordion',
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# Guitar
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25 : 'Acoustic Guitar (nylon)',
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26 : 'Acoustic Guitar (steel)',
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27 : 'Electric Guitar (jazz)',
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@ -52,6 +55,7 @@ midi_program = {
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30 : 'Overdriven Guitar',
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31 : 'Distortion Guitar',
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32 : 'Guitar harmonics',
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# Bass
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33 : 'Acoustic Bass',
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34 : 'Electric Bass (finger)',
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35 : 'Electric Bass (pick)',
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@ -60,6 +64,7 @@ midi_program = {
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38 : 'Slap Bass 2',
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39 : 'Synth Bass 1',
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40 : 'Synth Bass 2',
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# Strings
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41 : 'Violin',
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42 : 'Viola',
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43 : 'Cello',
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@ -68,6 +73,7 @@ midi_program = {
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46 : 'Pizzicato Strings',
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47 : 'Orchestral Harp',
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48 : 'Timpani',
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# Ensemble
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49 : 'String Ensemble 1',
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50 : 'String Ensemble 2',
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51 : 'SynthStrings 1',
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@ -76,6 +82,7 @@ midi_program = {
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54 : 'Voice Oohs',
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55 : 'Synth Voice',
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56 : 'Orchestra Hit',
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# Brass
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57 : 'Trumpet',
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58 : 'Trombone',
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59 : 'Tuba',
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@ -84,6 +91,7 @@ midi_program = {
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62 : 'Brass Section',
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63 : 'SynthBrass 1',
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64 : 'SynthBrass 2',
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# Reed
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65 : 'Soprano Sax',
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66 : 'Alto Sax',
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67 : 'Tenor Sax',
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@ -92,6 +100,7 @@ midi_program = {
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70 : 'English Horn',
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71 : 'Bassoon',
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72 : 'Clarinet',
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# Pipe
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73 : 'Piccolo',
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74 : 'Flute',
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75 : 'Recorder',
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@ -100,6 +109,7 @@ midi_program = {
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78 : 'Shakuhachi',
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79 : 'Whistle',
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80 : 'Ocarina',
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# Synth Lead
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81 : 'Lead 1 (square)',
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82 : 'Lead 2 (sawtooth)',
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83 : 'Lead 3 (calliope)',
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@ -108,6 +118,7 @@ midi_program = {
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86 : 'Lead 6 (voice)',
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87 : 'Lead 7 (fifths)',
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88 : 'Lead 8 (bass + lead)',
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# Synth Pad
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89 : 'Pad 1 (new age)',
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90 : 'Pad 2 (warm)',
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91 : 'Pad 3 (polysynth)',
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@ -116,6 +127,7 @@ midi_program = {
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94 : 'Pad 6 (metallic)',
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95 : 'Pad 7 (halo)',
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96 : 'Pad 8 (sweep)',
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# Synth Effects
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97 : 'FX 1 (rain)',
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98 : 'FX 2 (soundtrack)',
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99 : 'FX 3 (crystal)',
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@ -124,6 +136,7 @@ midi_program = {
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102 : 'FX 6 (goblins)',
|
||||
103 : 'FX 7 (echoes)',
|
||||
104 : 'FX 8 (sci-fi)',
|
||||
# Ethnic
|
||||
105 : 'Sitar',
|
||||
106 : 'Banjo',
|
||||
107 : 'Shamisen',
|
||||
@ -132,6 +145,7 @@ midi_program = {
|
||||
110 : 'Bag pipe',
|
||||
111 : 'Fiddle',
|
||||
112 : 'Shanai',
|
||||
# Percussive
|
||||
113 : 'Tinkle Bell',
|
||||
114 : 'Agogo',
|
||||
115 : 'Steel Drums',
|
||||
@ -140,6 +154,7 @@ midi_program = {
|
||||
118 : 'Melodic Tom',
|
||||
119 : 'Synth Drum',
|
||||
120 : 'Reverse Cymbal',
|
||||
# Sound Effects
|
||||
121 : 'Guitar Fret Noise',
|
||||
122 : 'Breath Noise',
|
||||
123 : 'Seashore',
|
||||
|
||||
@ -3,31 +3,50 @@
|
||||
import tensorflow as tf
|
||||
import settings
|
||||
from tensorflow.keras import layers
|
||||
from keras.layers import Input, Dense, Conv2D, Flatten
|
||||
from keras.layers import Input, Dense, Conv2D, Flatten, LSTM, Dropout, TimeDistributed, RepeatVector, Activation, Bidirectional, Reshape
|
||||
from keras.models import Model, Sequential
|
||||
import numpy as np
|
||||
from sys import exit
|
||||
import sys
|
||||
import pickle
|
||||
|
||||
print('Reading samples from: {}'.format(settings.samples_path))
|
||||
train_data_path_X = sys.argv[1]
|
||||
train_data_path_y = sys.argv[2]
|
||||
save_model_path = sys.argv[3]
|
||||
epochs = int(sys.argv[4])
|
||||
|
||||
train_X = np.load(settings.samples_path)['arr_0']
|
||||
# model architecture
|
||||
# model = Sequential()
|
||||
# model.add(LSTM(128, activation='relu', input_shape=(96,128)))
|
||||
# model.add(RepeatVector(96))
|
||||
# model.add(LSTM(128, activation='softmax', return_sequences=True))
|
||||
# model.add(TimeDistributed(Dense(128)))
|
||||
#
|
||||
# model.compile(optimizer='adam',
|
||||
# loss='categorical_crossentropy',
|
||||
# metrics=['accuracy'])
|
||||
|
||||
n_samples = train_X.shape[0]
|
||||
input_shape = settings.midi_resolution*128
|
||||
train_X = train_X.reshape(n_samples, input_shape)
|
||||
model = Sequential()
|
||||
model.add(LSTM(128,input_shape=(96, 128),return_sequences=True))
|
||||
model.add(Dropout(0.3))
|
||||
model.add(LSTM(512, return_sequences=True))
|
||||
model.add(Dropout(0.3))
|
||||
model.add(LSTM(512))
|
||||
model.add(Dense(512))
|
||||
# model.add(Dropout(0.3))
|
||||
# model.add(Dense(128))
|
||||
model.add(Activation('softmax'))
|
||||
model.add(Reshape((4, 128)))
|
||||
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
|
||||
|
||||
# encoder model
|
||||
input_img = tf.keras.layers.Input(shape=(input_shape,))
|
||||
encoded = tf.keras.layers.Dense(160, activation='relu')(input_img)
|
||||
decoded = tf.keras.layers.Dense(input_shape, activation='sigmoid')(encoded)
|
||||
autoencoder = tf.keras.models.Model(input_img, decoded)
|
||||
# load training data
|
||||
print('Reading samples from: {}'.format(train_data_path_X))
|
||||
train_X = np.load(train_data_path_X)['arr_0']
|
||||
train_y = np.load(train_data_path_y)['arr_0']
|
||||
|
||||
autoencoder.compile(optimizer='adam',
|
||||
loss='binary_crossentropy',
|
||||
metrics=['accuracy'])
|
||||
# model training
|
||||
model.fit(train_X, train_y, epochs=epochs, batch_size=32)
|
||||
|
||||
autoencoder.fit(train_X, train_X, epochs=settings.epochs, batch_size=32)
|
||||
|
||||
autoencoder.save_weights(settings.model_path)
|
||||
print("Model save to {}".format(settings.model_path))
|
||||
# save trained model
|
||||
pickle_path = '{}.pickle'.format(save_model_path)
|
||||
pickle.dump(model, open(pickle_path,'wb'))
|
||||
print("Model save to {}".format(pickle_path))
|
||||
|
||||
Loading…
Reference in New Issue
Block a user