def setUp(self):
super().setUp()
self.config = events_rnn_model.EventSequenceRnnConfig(
None,
note_seq.OneHotEventSequenceEncoderDecoder(
note_seq.MultiDrumOneHotEncoding()),
contrib_training.HParams())
def testDrumsRNNPipeline(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, [(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 = note_seq.OneHotEventSequenceEncoderDecoder(
note_seq.MultiDrumOneHotEncoding())
quantized = quantizer.transform(note_sequence)[0]
drums = drums_extractor.transform(quantized)[0]
one_hot = pipelines_common.make_sequence_example(
*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)
# open hi-hat
[46, 67, 72, 74, 79, 81, 26,
49, 52, 55, 57, 58, # crash
51, 53, 59, 82], # ride
]
# Default configurations.
default_configs = {
'one_drum':
events_rnn_model.EventSequenceRnnConfig(
generator_pb2.GeneratorDetails(
id='one_drum', description='Drums RNN with 2-state encoding.'),
note_seq.OneHotEventSequenceEncoderDecoder(
note_seq.MultiDrumOneHotEncoding(
[[39] + # use hand clap as default when decoding
list(range(note_seq.MIN_MIDI_PITCH, 39)) +
list(range(39, note_seq.MAX_MIDI_PITCH + 1))])),
contrib_training.HParams(
batch_size=128,
rnn_layer_sizes=[128, 128],
dropout_keep_prob=0.5,
clip_norm=5,
learning_rate=0.001),
steps_per_quarter=2),
'drum_kit':
events_rnn_model.EventSequenceRnnConfig(
generator_pb2.GeneratorDetails(
id='drum_kit',
description='Drums RNN with multiple drums and binary counters.'
),
note_seq.LookbackEventSequenceEncoderDecoder(