Using a trained LSTM-based neural network model to generate Depeche Mode-style lyrics

depeche_mode_lyric_generator.py

import numpy as np
import sys
from keras.models import Sequential

filename = 'trained_depechemode_lyrics_model.h5'

model = Sequential()
model.load_weights(filename)
model.compile(loss='categorical_crossentropy', optimizer='adam')

int_to_char = dict((i, c) for i, c in enumerate(chars))


''' make predictions '''
# pick a random seed value to start
# with a single character
start = np.random.randint(0, len(dataX)-1)
pattern = dataX[start]   # see https://gist.github.com/jasonsalas/793a451a2c0a4f7fa6766bc29c4c0544 for this variable
print('seed: ')
print("\"", ''.join([int_to_char[value] for value in pattern]), "\"")

# generate characters 
# characters form words
# words form sentences
# sentences form stanzas
# stanzas form verses and choruses
print('\n\n\n****[START SYNTHETIC LYRICS]****\n\n\n')
for i in range(250):
    x = np.reshape(pattern, (1, len(pattern), 1))
    x = x / float(n_vocab)
    prediction = model.predict(x, verbose=0)
    index = np.argmax(prediction)
    result = int_to_char[index]
    seq_in = [int_to_char[value] for value in pattern]
    sys.stdout.write(result)
    pattern.append(index)
    pattern = pattern[1:len(pattern)]

print('\n\n\n****[FIN]****')