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)]) quantizer = pipelines_common.Quantizer(steps_per_quarter=4) melody_extractor = melody_pipelines.MelodyExtractor( min_bars=7, min_unique_pitches=5, gap_bars=1.0, ignore_polyphonic_notes=False) one_hot_encoding = magenta.music.OneHotEventSequenceEncoderDecoder( magenta.music.MelodyOneHotEncoding( self.config.min_note, self.config.max_note)) quantized = quantizer.transform(note_sequence)[0] melody = melody_extractor.transform(quantized)[0] melody.squash( self.config.min_note, self.config.max_note, self.config.transpose_to_key) one_hot = one_hot_encoding.encode(melody) expected_result = {'training_melodies': [one_hot], 'eval_melodies': []} pipeline_inst = melody_rnn_create_dataset.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)]) quantizer = note_sequence_pipelines.Quantizer(steps_per_quarter=4) melody_extractor = melody_pipelines.MelodyExtractor( min_bars=7, min_unique_pitches=5, gap_bars=1.0, ignore_polyphonic_notes=False) one_hot_encoding = magenta.music.OneHotEventSequenceEncoderDecoder( magenta.music.MelodyOneHotEncoding( self.config.min_note, self.config.max_note)) quantized = quantizer.transform(note_sequence)[0] melody = melody_extractor.transform(quantized)[0] melody.squash( self.config.min_note, self.config.max_note, self.config.transpose_to_key) one_hot = one_hot_encoding.encode(melody) expected_result = {'training_melodies': [one_hot], 'eval_melodies': []} pipeline_inst = melody_rnn_create_dataset.get_pipeline(self.config, eval_ratio=0.0) result = pipeline_inst.transform(note_sequence) self.assertEqual(expected_result, result)
def main(unused_argv): tf.logging.set_verbosity(md.FLAGS.log) config = md.melody_rnn_config_flags.config_from_flags() pipeline_instance = md.get_pipeline(config, md.FLAGS.eval_ratio) md.pipeline.run_pipeline_serial( pipeline_instance, md.pipeline.tf_record_iterator(tgt.SEQUENCE_FILE, pipeline_instance.input_type), tgt.OUTPUT_DIR)