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)