def main(unused_argv):
tf.logging.set_verbosity(FLAGS.log)
config = improv_rnn_config_flags.config_from_flags()
pipeline_instance = improv_rnn_pipeline.get_pipeline(
config, FLAGS.eval_ratio)
FLAGS.input = os.path.expanduser(FLAGS.input)
FLAGS.output_dir = os.path.expanduser(FLAGS.output_dir)
pipeline.run_pipeline_serial(
pipeline_instance,
pipeline.tf_record_iterator(FLAGS.input, pipeline_instance.input_type),
FLAGS.output_dir)
def main(unused_argv):
tf.logging.set_verbosity(FLAGS.log)
config = improv_rnn_config_flags.config_from_flags()
pipeline_instance = improv_rnn_pipeline.get_pipeline(
config, FLAGS.eval_ratio)
FLAGS.input = os.path.expanduser(FLAGS.input)
FLAGS.output_dir = os.path.expanduser(FLAGS.output_dir)
pipeline.run_pipeline_serial(
pipeline_instance,
pipeline.tf_record_iterator(FLAGS.input, pipeline_instance.input_type),
FLAGS.output_dir)
def testMelodyRNNPipeline(self):
note_sequence = magenta.common.testing_lib.parse_test_proto(
note_seq.NoteSequence, """
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 120}""")
note_seq.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)])
note_seq.testing_lib.add_chords_to_sequence(note_sequence,
[('N.C.', 0.0),
('Am9', 5.0),
('D7', 10.0)])
quantizer = note_sequence_pipelines.Quantizer(steps_per_quarter=4)
lead_sheet_extractor = lead_sheet_pipelines.LeadSheetExtractor(
min_bars=7,
min_unique_pitches=5,
gap_bars=1.0,
ignore_polyphonic_notes=False,
all_transpositions=False)
conditional_encoding = note_seq.ConditionalEventSequenceEncoderDecoder(
note_seq.OneHotEventSequenceEncoderDecoder(
note_seq.MajorMinorChordOneHotEncoding()),
note_seq.OneHotEventSequenceEncoderDecoder(
note_seq.MelodyOneHotEncoding(self.config.min_note,
self.config.max_note)))
quantized = quantizer.transform(note_sequence)[0]
lead_sheet = lead_sheet_extractor.transform(quantized)[0]
lead_sheet.squash(self.config.min_note, self.config.max_note,
self.config.transpose_to_key)
encoded = pipelines_common.make_sequence_example(
*conditional_encoding.encode(lead_sheet.chords, lead_sheet.melody))
expected_result = {
'training_lead_sheets': [encoded],
'eval_lead_sheets': []
}
pipeline_inst = improv_rnn_pipeline.get_pipeline(self.config,
eval_ratio=0.0)
result = pipeline_inst.transform(note_sequence)
self.assertEqual(expected_result, result)
def testMelodyRNNPipeline(self):
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)])
magenta.music.testing_lib.add_chords_to_sequence(
note_sequence,
[('N.C.', 0.0), ('Am9', 5.0), ('D7', 10.0)])
quantizer = note_sequence_pipelines.Quantizer(steps_per_quarter=4)
lead_sheet_extractor = lead_sheet_pipelines.LeadSheetExtractor(
min_bars=7, min_unique_pitches=5, gap_bars=1.0,
ignore_polyphonic_notes=False, all_transpositions=False)
conditional_encoding = magenta.music.ConditionalEventSequenceEncoderDecoder(
magenta.music.OneHotEventSequenceEncoderDecoder(
magenta.music.MajorMinorChordOneHotEncoding()),
magenta.music.OneHotEventSequenceEncoderDecoder(
magenta.music.MelodyOneHotEncoding(
self.config.min_note, self.config.max_note)))
quantized = quantizer.transform(note_sequence)[0]
lead_sheet = lead_sheet_extractor.transform(quantized)[0]
lead_sheet.squash(
self.config.min_note,
self.config.max_note,
self.config.transpose_to_key)
encoded = conditional_encoding.encode(lead_sheet.chords, lead_sheet.melody)
expected_result = {'training_lead_sheets': [encoded],
'eval_lead_sheets': []}
pipeline_inst = improv_rnn_pipeline.get_pipeline(
self.config, eval_ratio=0.0)
result = pipeline_inst.transform(note_sequence)
self.assertEqual(expected_result, result)
