LSTM Music Generator
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@ -48,18 +48,14 @@ output_path = sys.argv[2]
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# this dictionary is generated with model
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print('Loading... {}'.format(trained_model_path))
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model = pickle.load(open(trained_model_path, 'rb'))
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int_to_note = pickle.load(open('{}_dict'.format(trained_model_path), 'rb'))
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# TODO: 16 it should a variable by integrated with model seq_len
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# TODO: random.randint(0,50), the range should be a variable of lenght of vocab size
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seed = [random.randint(0,250) for x in range(16)]
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int_to_note, n_vocab, seq_len = pickle.load(open('{}_dict'.format(trained_model_path), 'rb'))
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seed = [random.randint(0,n_vocab) for x in range(seq_len)]
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music = []
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print('Generating...')
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for i in trange(124):
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#TODO: 16 it should a variable by integrated with model seq_len
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predicted_vector = model.predict(np.array(seed).reshape(1,16,1))
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predicted_vector = model.predict(np.array(seed).reshape(1,seq_len,1))
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# using best fitted note
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# predicted_index = np.argmax(predicted_vector)
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# using propability distribution for choosing note
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@ -67,14 +63,14 @@ for i in trange(124):
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predicted_index = choose_by_prob(predicted_vector)
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music.append(int_to_note[predicted_index])
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seed.append(predicted_index)
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#TODO: 16 it should a variable by integrated with model seq_len
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seed = seed[1:1+16]
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seed = seed[1:1+seq_len]
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print('Saving...')
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offset = 0
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output_notes = []
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for event in tqdm(music):
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for _event in tqdm(music):
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event, note_len = _event.split(';')
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if (' ' in event) or event.isdigit():
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notes_in_chord = event.split(' ')
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notes = []
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@ -91,7 +87,7 @@ for event in tqdm(music):
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new_note.storedInstrument = instrument.Piano()
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output_notes.append(new_note)
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offset += 0.5
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offset += float(note_len)
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midi_stream = stream.Stream(output_notes)
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@ -8,8 +8,7 @@ that is prepared for model training.
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output_path - the output path where will be created samples of data
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Usage:
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>>> ./midi.py <midi_folder_path> <output_path>
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>>> ./midi.py <midi_folder_path> <output_path> <sequence_lenth>
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'''
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import settings
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@ -24,9 +23,22 @@ import pickle
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from music21 import converter, instrument, note, chord, stream
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import music21
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midi_folder_path = sys.argv[1]
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output_path = sys.argv[2]
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seq_len = int(sys.argv[3])
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class MidiParseError(Exception):
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"""Error that is raised then midi file cannot be parsed"""
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pass
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def parse_argv(argv):
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'''This function is parsing given arguments when running a midi script.
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Returns a tuple consinting of midi_folder_path, output_path, seq_len'''
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try:
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midi_folder_path = argv[1]
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output_path = argv[2]
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seq_len = int(argv[3])
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return midi_folder_path, output_path, seq_len
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except IndexError:
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raise AttributeError('You propably didnt pass parameters to run midi.py script.\
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>>> ./midi.py <midi_folder_path> <output_path> <sequence_lenth>')
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def to_sequence(midi_path, seq_len):
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''' This function is supposed to be used on one midi file in directory loop.
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@ -40,22 +52,24 @@ def to_sequence(midi_path, seq_len):
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- midi_path: path to midi file
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- seq_len: lenght of sequance before prediction
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Returns: Tuple of train_X, train_y directories'''
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Returns: Tuple of train_X, train_y dictionaries consisinting of samples of song grouped by instruments
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'''
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seq_by_instrument = defaultdict( lambda : [] )
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try:
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midi_file = music21.converter.parse(midi_path)
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except music21.midi.MidiException:
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raise MidiParseError
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stream = music21.instrument.partitionByInstrument(midi_file)
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for part in stream:
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for event in part:
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if part.partName != None:
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# TODO: add note lenght as parameter
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if isinstance(event, music21.note.Note):
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# to_export_event = (str(event.pitch), event.quarterLength)
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to_export_event = str(event.pitch)
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to_export_event = '{};{}'.format(str(event.pitch), float(event.quarterLength))
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seq_by_instrument[part.partName].append(to_export_event)
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elif isinstance(event, music21.chord.Chord):
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to_export_event = ' '.join(str(note) for note in event.pitches)
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# to_export_event = (' '.join(str(note) for note in event.pitches), event.quarterLength)
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to_export_event = '{};{}'.format(' '.join(str(note) for note in event.pitches), float(event.quarterLength))
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seq_by_instrument[part.partName].append(to_export_event)
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X_train_by_instrument = defaultdict( lambda : [] )
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@ -65,39 +79,54 @@ def to_sequence(midi_path, seq_len):
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for i in range(len(sequence)-(seq_len)) :
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X_train_by_instrument[instrument].append(np.array(sequence[i:i+seq_len])) # <seq lenth
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y_train_by_instrument[instrument].append(np.array(sequence[i+seq_len]))
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# TODO: Notes to integers
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return X_train_by_instrument, y_train_by_instrument
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def main():
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print('Exporting...')
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def colect_samples(midi_folder_path, seq_len):
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'''This function is looping throuth given directories and
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collecting samples from midi files.
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Parameters: midi_folder_path - a path to directory with midi files
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seq_len - a lenth of train_X sample that tells
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how many notes is given do LSTM to predict the next note.
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Returns: Tuple of train_X, train_y dictionaries consisinting
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of samples of all songs in directory grouped by instruments.
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'''
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print('Collecting samples...')
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train_X = defaultdict( lambda : [] )
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train_y = defaultdict( lambda : [] )
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for directory, subdirectories, files in os.walk(midi_folder_path):
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for midi_file in tqdm(files):
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midi_file_path = os.path.join(directory, midi_file)
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# some midi files can be corupted, and cannot be parsed
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# so we just omit corupted files, and go to the next file.
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try:
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_X_train, _y_train = to_sequence(midi_file_path, seq_len)
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except music21.midi.MidiException:
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except MidiParseError:
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continue
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for (X_key, X_value), (y_key, y_value) in zip(_X_train.items(), _y_train.items()):
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train_X[X_key].extend(np.array(X_value))
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train_y[y_key].extend(np.array(y_value))
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# this is for intrument separation
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return train_X, train_y
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def save_samples(output_path, samples):
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'''This function save samples to npz packages, splitted by instrument.'''
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print('Saving...')
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if not os.path.exists(output_path):
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os.makedirs(output_path)
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train_X, train_y = samples
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for (X_key, X_value), (y_key, y_value) in tqdm(zip(train_X.items(), train_y.items())):
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if X_key == y_key:
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np.savez_compressed('{}/{}.npz'.format(output_path, X_key), np.array(X_value), np.array(y_value))
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def main():
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midi_folder_path, output_path, seq_len = parse_argv(sys.argv)
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save_samples(output_path, colect_samples(midi_folder_path, seq_len))
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print('Done!')
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if __name__ == '__main__':
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@ -1,15 +1,14 @@
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#!python3
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#!/usr/bin/env python3
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import tensorflow as tf
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import sys
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import pickle
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import settings
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#from tensorflow.keras import layers
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import numpy as np
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from keras.layers import Input, Dense, Conv2D, Flatten, LSTM, Dropout, TimeDistributed, RepeatVector, Activation, Bidirectional, Reshape
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from keras.models import Model, Sequential
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from keras.utils.np_utils import to_categorical
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import numpy as np
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import sys
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import pickle
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def load_data(samples_path):
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print('Loading... {}'.format(train_data_path))
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@ -25,7 +24,6 @@ def preprocess_samples(train_X, train_y):
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n_vocab = vocab.shape[0]
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note_to_int = dict((note, number) for number, note in enumerate(vocab))
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int_to_note = dict((number, note) for number, note in enumerate(vocab))
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_train_X = []
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_train_y = []
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for sample in train_X:
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@ -58,12 +56,13 @@ model.add(Dense(n_vocab))
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model.add(Activation('softmax'))
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model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
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# model training
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# This code will train our model, with given by parameter number of epochs
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print('Training...')
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model.fit(train_X, train_y, epochs=epochs, batch_size=64)
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# save trained model
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# it saves model, and additional informations of model
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# that is needed to generate music from it
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pickle.dump(model, open(save_model_path,'wb'))
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pickle.dump(int_to_note, open('{}_dict'.format(save_model_path),'wb'))
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pickle.dump((int_to_note, n_vocab, train_X.shape[1]), open('{}_dict'.format(save_model_path),'wb'))
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print('Done!')
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print("Model saved to: {}".format(save_model_path))
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