def __init__(self, root, verbose=False):
assert os.path.isdir(root), root
paths = utils.find_files_by_extensions(root, ['.data'])
self.root = root
self.samples = []
self.seqlens = []
if verbose:
paths = Bar(root).iter(list(paths))
for path in paths:
eventseq, controlseq = torch.load(path)
controlseq = ControlSeq.recover_compressed_array(controlseq)
assert len(eventseq) == len(controlseq)
self.samples.append((eventseq, controlseq))
self.seqlens.append(len(eventseq))
self.avglen = np.mean(self.seqlens)
def main(_):
if os.path.isfile(FLAGS.control) or os.path.isdir(FLAGS.control):
if os.path.isdir(FLAGS.control):
files = list(utils.find_files_by_extensions(FLAGS.control))
assert len(files) > 0, 'no file in "{control}"'.format(
control=FLAGS.control)
control = np.random.choice(files)
events, compressed_controls = torch.load(FLAGS.control)
controls = ControlSeq.recover_compressed_array(compressed_controls)
max_len = FLAGS.max_length
if FLAGS.max_length == 0:
max_len = controls.shape[0]
control = np.expand_dims(controls, 1).repeat(1, 1)
control = 'control sequence from "{control}"'.format(control=control)
assert max_len > 0, 'either max length or control sequence length should be given'
#FLAGS.start_string = FLAGS.start_string.decode('utf-8')
if os.path.isdir(FLAGS.checkpoint_path):
FLAGS.checkpoint_path =\
tf.train.latest_checkpoint(FLAGS.checkpoint_path)
model = CharRNN(EventSeq.dim(),
ControlSeq.dim(),
sampling=True,
lstm_size=FLAGS.lstm_size,
num_layers=FLAGS.num_layers,
use_embedding=FLAGS.use_embedding,
embedding_size=FLAGS.embedding_size)
model.sess.run(tf.global_variables_initializer())
model.load(FLAGS.checkpoint_path)
outputs = model.sample(1000,
prime=events[0:100],
vocab_size=EventSeq.dim())
outputs = outputs.reshape([-1, 1])
print(outputs)
name = 'output-{i:03d}.mid'.format(i=0)
path = os.path.join("output/", name)
n_notes = utils.event_indeces_to_midi_file(outputs[:, 0], path)
print('===> {path} ({n_notes} notes)'.format(path=path, n_notes=n_notes))
if use_beam_search:
greedy_ratio = 'DISABLED'
else:
beam_size = 'DISABLED'
assert os.path.isfile(sess_path), f'"{sess_path}" is not a file'
if control is not None:
if os.path.isfile(control) or os.path.isdir(control):
if os.path.isdir(control):
files = list(utils.find_files_by_extensions(control))
assert len(files) > 0, f'no file in "{control}"'
control = np.random.choice(files)
_, compressed_controls = torch.load(control)
controls = ControlSeq.recover_compressed_array(compressed_controls)
if max_len == 0:
max_len = controls.shape[0]
controls = torch.tensor(controls, dtype=torch.float32)
controls = controls.unsqueeze(1).repeat(1, batch_size, 1).to(device)
control = f'control sequence from "{control}"'
else:
pitch_histogram, note_density = control.split(';')
pitch_histogram = list(filter(len, pitch_histogram.split(',')))
if len(pitch_histogram) == 0:
pitch_histogram = np.ones(12) / 12
else:
pitch_histogram = np.array(list(map(float, pitch_histogram)))
assert pitch_histogram.size == 12
assert np.all(pitch_histogram >= 0)