def testDrumsRNNPipeline(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, [(36, 100, 0.00, 2.0), (40, 55, 2.1, 5.0), (44, 80, 3.6, 5.0),
(41, 45, 5.1, 8.0), (64, 100, 6.6, 10.0), (55, 120, 8.1, 11.0),
(39, 110, 9.6, 9.7), (53, 99, 11.1, 14.1),
(51, 40, 12.6, 13.0), (55, 100, 14.1, 15.0),
(54, 90, 15.6, 17.0), (60, 100, 17.1, 18.0)],
is_drum=True)
quantizer = note_sequence_pipelines.Quantizer(steps_per_quarter=4)
drums_extractor = drum_pipelines.DrumsExtractor(min_bars=7,
gap_bars=1.0)
one_hot_encoding = magenta.music.OneHotEventSequenceEncoderDecoder(
magenta.music.MultiDrumOneHotEncoding())
quantized = quantizer.transform(note_sequence)[0]
drums = drums_extractor.transform(quantized)[0]
one_hot = one_hot_encoding.encode(drums)
expected_result = {
'training_drum_tracks': [one_hot],
'eval_drum_tracks': []
}
pipeline_inst = drums_rnn_pipeline.get_pipeline(self.config,
eval_ratio=0.0)
result = pipeline_inst.transform(note_sequence)
self.assertEqual(expected_result, result)
def testDrumsRNNPipeline(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,
[(36, 100, 0.00, 2.0), (40, 55, 2.1, 5.0), (44, 80, 3.6, 5.0),
(41, 45, 5.1, 8.0), (64, 100, 6.6, 10.0), (55, 120, 8.1, 11.0),
(39, 110, 9.6, 9.7), (53, 99, 11.1, 14.1), (51, 40, 12.6, 13.0),
(55, 100, 14.1, 15.0), (54, 90, 15.6, 17.0), (60, 100, 17.1, 18.0)],
is_drum=True)
quantizer = note_sequence_pipelines.Quantizer(steps_per_quarter=4)
drums_extractor = drum_pipelines.DrumsExtractor(min_bars=7, gap_bars=1.0)
one_hot_encoding = magenta.music.OneHotEventSequenceEncoderDecoder(
magenta.music.MultiDrumOneHotEncoding())
quantized = quantizer.transform(note_sequence)[0]
drums = drums_extractor.transform(quantized)[0]
one_hot = one_hot_encoding.encode(drums)
expected_result = {'training_drum_tracks': [one_hot],
'eval_drum_tracks': []}
pipeline_inst = drums_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 = drums_rnn_config_flags.config_from_flags()
pipeline_instance = drums_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 = drums_rnn_config_flags.config_from_flags()
pipeline_instance = drums_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)
