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)
