""" This module prepares midi file data and feeds it to the neural network for training """ import glob import pickle import numpy from music21 import converter, instrument, note, chord from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from keras.layers import LSTM from keras.layers import Activation from keras.layers import BatchNormalization as BatchNorm from keras.utils import np_utils from keras.callbacks import ModelCheckpoint def train_network(): """ Train a Neural Network to generate music """ notes = get_notes() # get amount of pitch names n_vocab = len(set(notes)) network_input, network_output = prepare_sequences(notes, n_vocab) model = create_network(network_input, n_vocab) train(model, network_input, network_output) def get_notes(): """ Get all the notes and chords from the midi files in the ./midi_songs directory """ notes = [] for file in glob.glob("midi_songs/*.mid"): midi = converter.parse(file) print("Parsing %s" % file) notes_to_parse = None try: # file has instrument parts s2 = instrument.partitionByInstrument(midi) notes_to_parse = s2.parts[0].recurse() except: # file has notes in a flat structure notes_to_parse = midi.flat.notes for element in notes_to_parse: if isinstance(element, note.Note): notes.append(str(element.pitch)) elif isinstance(element, chord.Chord): notes.append('.'.join(str(n) for n in element.normalOrder)) with open('data/notes', 'wb') as filepath: pickle.dump(notes, filepath) return notes def prepare_sequences(notes, n_vocab): """ Prepare the sequences used by the Neural Network """ sequence_length = 100 # get all pitch names pitchnames = sorted(set(item for item in notes)) # create a dictionary to map pitches to integers note_to_int = dict((note, number) for number, note in enumerate(pitchnames)) network_input = [] network_output = [] # create input sequences and the corresponding outputs for i in range(0, len(notes) - sequence_length, 1): sequence_in = notes[i:i + sequence_length] sequence_out = notes[i + sequence_length] network_input.append([note_to_int[char] for char in sequence_in]) network_output.append(note_to_int[sequence_out]) n_patterns = len(network_input) # reshape the input into a format compatible with LSTM layers network_input = numpy.reshape(network_input, (n_patterns, sequence_length, 1)) # normalize input network_input = network_input / float(n_vocab) network_output = np_utils.to_categorical(network_output) return (network_input, network_output) def create_network(network_input, n_vocab): """ create the structure of the neural network """ model = Sequential() model.add(LSTM( 512, input_shape=(network_input.shape[1], network_input.shape[2]), recurrent_dropout=0.3, return_sequences=True )) model.add(LSTM(512, return_sequences=True, recurrent_dropout=0.3,)) model.add(LSTM(512)) model.add(BatchNorm()) model.add(Dropout(0.3)) model.add(Dense(256)) model.add(Activation('relu')) model.add(BatchNorm()) model.add(Dropout(0.3)) model.add(Dense(n_vocab)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='rmsprop') return model def train(model, network_input, network_output): """ train the neural network """ filepath = "weights-improvement-{epoch:02d}-{loss:.4f}-bigger.hdf5" checkpoint = ModelCheckpoint( filepath, monitor='loss', verbose=0, save_best_only=True, mode='min' ) callbacks_list = [checkpoint] model.fit(network_input, network_output, epochs=200, batch_size=128, callbacks=callbacks_list) if __name__ == '__main__': train_network()