def velocity_range_from_sequence(ns): """Derive a VelocityRange proto from a NoteSequence.""" velocities = [note.velocity for note in ns.notes] velocity_max = np.max(velocities) if velocities else 0 velocity_min = np.min(velocities) if velocities else 0 velocity_range = music_pb2.VelocityRange(min=velocity_min, max=velocity_max) return velocity_range
def _FillExample(self, sequence, wav_data, filename):
velocity_range = music_pb2.VelocityRange(min=0, max=127)
feature_dict = {
'id':
tf.train.Feature(bytes_list=tf.train.BytesList(
value=[filename.encode('utf-8')])),
'sequence':
tf.train.Feature(bytes_list=tf.train.BytesList(
value=[sequence.SerializeToString()])),
'audio':
tf.train.Feature(bytes_list=tf.train.BytesList(value=[wav_data])),
'velocity_range':
tf.train.Feature(bytes_list=tf.train.BytesList(
value=[velocity_range.SerializeToString()])),
}
return tf.train.Example(features=tf.train.Features(
feature=feature_dict))