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_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)]) 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_pipeline.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(FLAGS.log) config = melody_rnn_config_flags.config_from_flags() pipeline_instance = melody_rnn_pipeline.get_pipeline( config, eval_ratio=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 = melody_rnn_config_flags.config_from_flags() pipeline_instance = melody_rnn_pipeline.get_pipeline( config, eval_ratio=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)
for file_in_dir in files_in_dir: full_file_path = os.path.join(input_dir, file_in_dir) print(full_file_path) try: sequence = midi_io.midi_to_sequence_proto( tf.io.gfile.GFile(full_file_path, 'rb').read()) except midi_io.MIDIConversionError as e: tf.logging.warning('Could not parse midi file %s. Error was: %s', full_file_path, e) sequence.collection_name = os.path.basename(work_dir) sequence.filename = os.path.join(output_dir, os.path.basename(full_file_path)) sequence.id = note_sequence_io.generate_note_sequence_id( sequence.filename, sequence.collection_name, 'midi') if sequence: writer.write(sequence) filenames = [anthems_file] dataset = tf.data.TFRecordDataset(filenames) config = melody_rnn_model.default_configs['attention_rnn'] pipeline_instance = melody_rnn_pipeline.get_pipeline(config, eval_ratio=0.0) pipeline.run_pipeline_serial( pipeline_instance, pipeline.tf_record_iterator(anthems_file, pipeline_instance.input_type), output_dir)