from midiwrapper import Song
from keras.models import load_model
import numpy as np
if __name__ == '__main__':
m = load_model('./temp/gan2.h5')
m.summary()
bs, seq_len, note_dim, _ = m.output_shape
code_dim = m.input_shape[-1]
def code_generator(bs):
Z = np.random.uniform(-1., 1., size=(bs, code_dim))
return Z
mid = Song()
track = mid.add_track()
notes = m.predict(code_generator(1))[0, :, :, 0]
mid._compose(track, notes, deltat=100, threshold=1.)
mid.save_as('gan2.mid')
THRESHOLD = 0.85
hots = Song(FILE).\
encode_onehot(
{'filter_f': lambda x: x.type in ['note_on', 'note_off'],
'unit': 'beat'},
{'resolution': 0.25})
print hots.shape
model = load_model('temp/memorize.h5')
mid = Song()
track = mid.add_track()
seq = [hots[i] for i in range(20)]
# seq = [np.random.binomial(1, 0.3, (dim,)) for _ in range(LEN)]
notes = [] # deepcopy(seq)
accumulate = np.zeros((dim,)).astype('int')
for _ in range(SONG_LEN):
note = model.predict(np.array([seq]))[0]
seq.pop(0)
print ''.join([('x' if char >= THRESHOLD else '_') for char in note])
notes.append(note)
# use output as input
# note = note * np.random.uniform(0.75, 1.2, size=(dim, ))
seq.append(note)
mid._compose(track, np.array(notes), deltat=100, threshold=THRESHOLD)
mid.save_as('memorize.mid')