def get_pipeline(melody_encoder_decoder):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
melody_encoder_decoder: A magenta.music.MelodyEncoderDecoder object.
Returns:
A pipeline.Pipeline instance.
"""
quantizer = pipelines_common.Quantizer(steps_per_quarter=4)
melody_extractor = pipelines_common.MelodyExtractor(
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 testMelodyRNNPipeline(self):
FLAGS.eval_ratio = 0.0
note_sequence = magenta.common.testing_lib.parse_test_proto(
music_pb2.NoteSequence, """
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 120}""")
magenta.music.testing_lib.add_track_to_sequence(
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.MelodyExtractor(
min_bars=7,
min_unique_pitches=5,
gap_bars=1.0,
ignore_polyphonic_notes=False)
one_hot_encoder = magenta.music.OneHotMelodyEncoderDecoder(0, 127, 0)
quantized = quantizer.transform(note_sequence)[0]
print quantized.tracks
melody = melody_extractor.transform(quantized)[0]
one_hot = one_hot_encoder.squash_and_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 testMelodyExtractor(self):
quantized_sequence = sequences_lib.QuantizedSequence()
quantized_sequence.steps_per_quarter = 1
testing_lib.add_quantized_track_to_sequence(quantized_sequence, 0,
[(12, 100, 2, 4),
(11, 1, 6, 7)])
testing_lib.add_quantized_track_to_sequence(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
], [
NO_EVENT, NO_EVENT, 12, NO_EVENT, NOTE_OFF, NO_EVENT, 14, NO_EVENT
]]
expected_melodies = []
for events_list in expected_events:
melody = melodies_lib.Melody(events_list,
steps_per_quarter=1,
steps_per_bar=4)
expected_melodies.append(melody)
unit = pipelines_common.MelodyExtractor(min_bars=1,
min_unique_pitches=1,
gap_bars=1)
self._unit_transform_test(unit, quantized_sequence, expected_melodies)