def testSplitMidi(self):
sequence = music_pb2.NoteSequence()
sequence.notes.add(pitch=60, start_time=1.0, end_time=2.9)
sequence.notes.add(pitch=60, start_time=8.0, end_time=11.0)
sequence.notes.add(pitch=60, start_time=14.0, end_time=17.0)
sequence.notes.add(pitch=60, start_time=20.0, end_time=23.0)
sequence.total_time = 25.
sample_rate = 160
samples = np.zeros(sample_rate * int(sequence.total_time))
splits = create_dataset_util.find_split_points(sequence, samples,
sample_rate, 0, 3)
self.assertEqual(splits,
[0., 3., 6., 9., 12., 15., 18., 21., 24., 25.])
samples[int(8.5 * sample_rate)] = 1
samples[int(8.5 * sample_rate) + 1] = -1
splits = create_dataset_util.find_split_points(sequence, samples,
sample_rate, 0, 3)
self.assertEqual(splits, [
0.0, 3.0, 6.0, 8.50625, 11.50625, 14.50625, 17.50625, 20.50625,
23.50625, 25.
])
def generate_train_set(exclude_ids):
"""Generate the train TFRecord."""
train_file_pairs = []
for directory in TRAIN_DIRS:
path = os.path.join(FLAGS.input_dir, directory)
path = os.path.join(path, '*.wav')
wav_files = glob.glob(path)
# find matching mid files
for wav_file in wav_files:
base_name_root, _ = os.path.splitext(wav_file)
mid_file = base_name_root + '.mid'
if filename_to_id(wav_file) not in exclude_ids:
train_file_pairs.append((wav_file, mid_file))
train_output_name = os.path.join(FLAGS.output_dir,
'maps_config2_train.tfrecord')
with tf.python_io.TFRecordWriter(train_output_name) as writer:
for idx, pair in enumerate(train_file_pairs):
print("{} of {}: {}".format(idx, len(train_file_pairs), pair[0]))
# load the wav data
wav_data = tf.gfile.Open(pair[0], 'rb').read()
samples = audio_io.wav_data_to_samples(wav_data, FLAGS.sample_rate)
norm_samples = librosa.util.normalize(samples, norm=np.inf)
# load the midi data and convert to a notesequence
ns = midi_io.midi_file_to_note_sequence(pair[1])
splits = create_dataset_util.find_split_points(
ns, norm_samples, FLAGS.sample_rate, FLAGS.min_length,
FLAGS.max_length)
velocities = [note.velocity for note in ns.notes]
velocity_max = np.max(velocities)
velocity_min = np.min(velocities)
new_velocity_tuple = music_pb2.VelocityRange(min=velocity_min,
max=velocity_max)
for start, end in zip(splits[:-1], splits[1:]):
if end - start < FLAGS.min_length:
continue
new_ns = sequences_lib.extract_subsequence(ns, start, end)
samples_start = int(start * FLAGS.sample_rate)
samples_end = samples_start + int(
(end - start) * FLAGS.sample_rate)
new_samples = samples[samples_start:samples_end]
new_wav_data = audio_io.samples_to_wav_data(
new_samples, FLAGS.sample_rate)
example = tf.train.Example(features=tf.train.Features(
feature={
'id':
tf.train.Feature(bytes_list=tf.train.BytesList(
value=[pair[0].encode()])),
'sequence':
tf.train.Feature(bytes_list=tf.train.BytesList(
value=[new_ns.SerializeToString()])),
'audio':
tf.train.Feature(bytes_list=tf.train.BytesList(
value=[new_wav_data])),
'velocity_range':
tf.train.Feature(bytes_list=tf.train.BytesList(
value=[new_velocity_tuple.SerializeToString()])),
}))
writer.write(example.SerializeToString())