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 get_pipeline():
return melody_rnn_create_dataset.get_pipeline(
attention_rnn_encoder_decoder.MelodyEncoderDecoder())
def get_pipeline():
return melody_rnn_create_dataset.get_pipeline(
lookback_rnn_encoder_decoder.MelodyEncoderDecoder())
def get_pipeline():
return melody_rnn_create_dataset.get_pipeline(
attention_rnn_encoder_decoder.MelodyEncoderDecoder())
def get_pipeline():
return melody_rnn_create_dataset.get_pipeline(
lookback_rnn_encoder_decoder.MelodyEncoderDecoder())