def compose(self, index, num_measures=16):
"""Use a pre-trained neural network to compose a melody.
"""
np.set_printoptions(threshold=np.nan)
for diversity in [0.2, 0.4, 0.6, 0.8, 1.0, 1.2]:
print
print '----- diversity:', diversity
SEED = self.dataset[3].transpose()
SEED = SEED[:self.window_size-1] # Use the window at the start. Subtract 1 since normal window size includes prediction.
melody = np.expand_dims(SEED, axis=0)
#print len(SEED)
#print len(melody)
for i in range(num_measures * BEATS_PER_MEASURE - len(SEED)):
#print 'melody.shape', melody.shape
x = np.expand_dims(np.array(melody[0][i:i + self.window_size]), axis=0)
#print 'i:', i
#print 'x:', x
#print 'x.shape', x.shape
next_frame = self.model.predict(x, verbose=0)[0]
#print 'next_frame normalized:', next_frame
#print 'melody.shape', melody.shape
#print 'next_frame.shape', next_frame.shape
#print 'next_frame raw:', next_frame
if SAMPLE_FROM_MELODY_PROBS:
# sample from melody probabilities.
next_frame = self._sample_melody(next_frame, MELODY_INDICES_RANGE, diversity)
else:
# Winner-takes-all on melody to force monophonic, and force other floats in vector to 0 or 1.
next_frame = self._winner_takes_all(next_frame, MELODY_INDICES_RANGE)
next_frame = self._get_binary_vector(next_frame)
next_frame = np.expand_dims(next_frame, axis=0)
#print 'next_frame normalized:', next_frame
#print 'melody.shape', melody.shape
#print 'next_frame.shape', next_frame.shape
melody = np.concatenate([melody, np.expand_dims(next_frame, axis=0)], axis=1)
#print 'Appended melody:', melody
# end of for loop
# Done with melody.
#print 'Final melody:', melody
#print
# Record the melody matrix to disk.
melody_csv = 'output/random_%d_%.2f.csv' % (index, diversity)
np.savetxt(melody_csv, melody[0], fmt='%d', delimiter=',')
#melody[0].tofile(melody_csv, format='%d', sep=',')
# Convert to MIDI and write to disk.
exporter = MidiExporter(melody[0])
exporter.create_midi_file('output/random_%d_%.2f.midi' % (index, diversity))
__author__ = 'epnichols'
import glob
import os
from exporter.neural_net_to_midi import MidiExporter
import numpy as np
np.set_printoptions(threshold=np.nan)
BASE_PATH = '../output'
if __name__ == '__main__':
# Loop over all files
for f in glob.glob('%s/*.csv' % BASE_PATH):
# Load numpy array.
melody = np.loadtxt(f, delimiter=',')
filename = os.path.basename(f)[:-4]
outfile = '%s/%s.midi' % (BASE_PATH, filename)
print '\ninput:', f
exporter = MidiExporter(melody)
exporter.create_midi_file(outfile)
def compose(self, index, num_measures=16):
"""Use a pre-trained neural network to compose a melody.
"""
np.set_printoptions(threshold=np.nan)
for diversity in [0.2, 0.4, 0.6, 0.8, 1.0, 1.2]:
print
print '----- diversity:', diversity
SEED = self.dataset[3].transpose()
SEED = SEED[:self.window_size -
1] # Use the window at the start. Subtract 1 since normal window size includes prediction.
melody = np.expand_dims(SEED, axis=0)
#print len(SEED)
#print len(melody)
for i in range(num_measures * BEATS_PER_MEASURE - len(SEED)):
#print 'melody.shape', melody.shape
x = np.expand_dims(np.array(melody[0][i:i + self.window_size]),
axis=0)
#print 'i:', i
#print 'x:', x
#print 'x.shape', x.shape
next_frame = self.model.predict(x, verbose=0)[0]
#print 'next_frame normalized:', next_frame
#print 'melody.shape', melody.shape
#print 'next_frame.shape', next_frame.shape
#print 'next_frame raw:', next_frame
if SAMPLE_FROM_MELODY_PROBS:
# sample from melody probabilities.
next_frame = self._sample_melody(next_frame,
MELODY_INDICES_RANGE,
diversity)
else:
# Winner-takes-all on melody to force monophonic, and force other floats in vector to 0 or 1.
next_frame = self._winner_takes_all(
next_frame, MELODY_INDICES_RANGE)
next_frame = self._get_binary_vector(next_frame)
next_frame = np.expand_dims(next_frame, axis=0)
#print 'next_frame normalized:', next_frame
#print 'melody.shape', melody.shape
#print 'next_frame.shape', next_frame.shape
melody = np.concatenate(
[melody, np.expand_dims(next_frame, axis=0)], axis=1)
#print 'Appended melody:', melody
# end of for loop
# Done with melody.
#print 'Final melody:', melody
#print
# Record the melody matrix to disk.
melody_csv = 'output/random_%d_%.2f.csv' % (index, diversity)
np.savetxt(melody_csv, melody[0], fmt='%d', delimiter=',')
#melody[0].tofile(melody_csv, format='%d', sep=',')
# Convert to MIDI and write to disk.
exporter = MidiExporter(melody[0])
exporter.create_midi_file('output/random_%d_%.2f.midi' %
(index, diversity))