def testMelodyRNNPipeline(self):
FLAGS.eval_ratio = 0.0
note_sequence = testing_lib.parse_test_proto(
music_pb2.NoteSequence, """
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 120}""")
testing_lib.add_track(note_sequence, 0, [(12, 100, 0.00, 2.0),
(11, 55, 2.1, 5.0),
(40, 45, 5.1, 8.0),
(55, 120, 8.1, 11.0),
(53, 99, 11.1, 14.1)])
quantizer = pipelines_common.Quantizer(steps_per_quarter=4)
melody_extractor = pipelines_common.MonophonicMelodyExtractor(
min_bars=7,
min_unique_pitches=5,
gap_bars=1.0,
ignore_polyphonic_notes=False)
one_hot_encoder = melodies_lib.OneHotEncoderDecoder(0, 127, 0)
quantized = quantizer.transform(note_sequence)[0]
print quantized.tracks
melody = melody_extractor.transform(quantized)[0]
one_hot = one_hot_encoder.encode(melody)
print one_hot
expected_result = {'training_melodies': [one_hot], 'eval_melodies': []}
pipeline_inst = melody_rnn_create_dataset.get_pipeline(one_hot_encoder)
result = pipeline_inst.transform(note_sequence)
self.assertEqual(expected_result, result)
def testMelodyRNNPipeline(self):
note_sequence = testing_lib.parse_test_proto(
music_pb2.NoteSequence,
"""
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
bpm: 120}""")
testing_lib.add_track(
note_sequence, 0,
[(12, 100, 0.01, 10.0), (11, 55, 0.22, 0.50), (40, 45, 2.50, 3.50),
(55, 120, 4.0, 4.01), (52, 99, 4.75, 5.0)])
quantizer = pipelines_common.Quantizer(steps_per_beat=4)
melody_extractor = pipelines_common.MonophonicMelodyExtractor(
min_bars=7, min_unique_pitches=5,
gap_bars=1.0)
one_hot_encoder = melody_rnn_create_dataset.OneHotEncoder()
quantized = quantizer.transform(note_sequence)[0]
melody = melody_extractor.transform(quantized)[0]
one_hot = one_hot_encoder.transform(melody)[0]
expected_result = {'melody_rnn_train': [one_hot], 'melody_rnn_eval': []}
pipeline_inst = melody_rnn_create_dataset.MelodyRNNPipeline(eval_ratio=0)
result = pipeline_inst.transform(note_sequence)
self.assertEqual(expected_result, result)
def testMonophonicMelodyExtractor(self):
quantized_sequence = sequences_lib.QuantizedSequence()
quantized_sequence.steps_per_beat = 1
testing_lib.add_quantized_track(
quantized_sequence, 0,
[(12, 100, 2, 4), (11, 1, 6, 7)])
testing_lib.add_quantized_track(
quantized_sequence, 1,
[(12, 127, 2, 4), (14, 50, 6, 8)])
expected_events = [
[NO_EVENT, NO_EVENT, 12, NO_EVENT, NOTE_OFF, NO_EVENT, 11, NOTE_OFF],
[NO_EVENT, NO_EVENT, 12, NO_EVENT, NOTE_OFF, NO_EVENT, 14, NO_EVENT,
NOTE_OFF]]
expected_melodies = []
for events_list in expected_events:
melody = melodies_lib.MonophonicMelody()
melody.from_event_list(events_list)
melody.steps_per_bar = 4
expected_melodies.append(melody)
expected_melodies[0].end_step = 8
expected_melodies[1].end_step = 12
unit = pipelines_common.MonophonicMelodyExtractor(
min_bars=1, min_unique_pitches=1, gap_bars=1)
self._unit_transform_test(unit, quantized_sequence, expected_melodies)
def get_pipeline(melody_encoder_decoder):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
melody_encoder_decoder: A melodies_lib.MelodyEncoderDecoder object.
Returns:
A pipeline.Pipeline instance.
"""
quantizer = pipelines_common.Quantizer(steps_per_beat=4)
melody_extractor = pipelines_common.MonophonicMelodyExtractor(
min_bars=7,
min_unique_pitches=5,
gap_bars=1.0,
ignore_polyphonic_notes=False)
encoder_pipeline = EncoderPipeline(melody_encoder_decoder)
partitioner = pipelines_common.RandomPartition(
tf.train.SequenceExample, ['eval_melodies', 'training_melodies'],
[FLAGS.eval_ratio])
dag = {
quantizer: dag_pipeline.Input(music_pb2.NoteSequence),
melody_extractor: quantizer,
encoder_pipeline: melody_extractor,
partitioner: encoder_pipeline,
dag_pipeline.Output(): partitioner
}
return dag_pipeline.DAGPipeline(dag)
def __init__(self, melody_encoder_decoder, eval_ratio):
self.training_set_name = 'training_melodies'
self.eval_set_name = 'eval_melodies'
super(MelodyRNNPipeline,
self).__init__(input_type=music_pb2.NoteSequence,
output_type={
self.training_set_name:
tf.train.SequenceExample,
self.eval_set_name: tf.train.SequenceExample
})
self.eval_ratio = eval_ratio
self.quantizer = pipelines_common.Quantizer(steps_per_beat=4)
self.melody_extractor = pipelines_common.MonophonicMelodyExtractor(
min_bars=7,
min_unique_pitches=5,
gap_bars=1.0,
ignore_polyphonic_notes=False)
self.encoder_unit = EncoderPipeline(melody_encoder_decoder)
self.stats_dict = {}
