def get_pipeline(config, eval_ratio):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: An ImprovRnnConfig object.
eval_ratio: Fraction of input to set aside for evaluation set.
Returns:
A pipeline.Pipeline instance.
"""
all_transpositions = config.transpose_to_key is None
partitioner = pipelines_common.RandomPartition(
music_pb2.NoteSequence,
['eval_lead_sheets', 'training_lead_sheets'],
[eval_ratio])
dag = {partitioner: dag_pipeline.DagInput(music_pb2.NoteSequence)}
for mode in ['eval', 'training']:
time_change_splitter = note_sequence_pipelines.TimeChangeSplitter(
name='TimeChangeSplitter_' + mode)
quantizer = note_sequence_pipelines.Quantizer(
steps_per_quarter=config.steps_per_quarter, name='Quantizer_' + mode)
lead_sheet_extractor = lead_sheet_pipelines.LeadSheetExtractor(
min_bars=7, max_steps=512, min_unique_pitches=3, gap_bars=1.0,
ignore_polyphonic_notes=False, all_transpositions=all_transpositions,
name='LeadSheetExtractor_' + mode)
encoder_pipeline = EncoderPipeline(config, name='EncoderPipeline_' + mode)
dag[time_change_splitter] = partitioner[mode + '_lead_sheets']
dag[quantizer] = time_change_splitter
dag[lead_sheet_extractor] = quantizer
dag[encoder_pipeline] = lead_sheet_extractor
dag[dag_pipeline.DagOutput(mode + '_lead_sheets')] = encoder_pipeline
return dag_pipeline.DAGPipeline(dag)
def get_pipeline(config, eval_ratio):
"""Returns the Pipeline instance which creates the RNN dataset.
Args:
config: An ImprovRnnConfig object.
eval_ratio: Fraction of input to set aside for evaluation set.
Returns:
A pipeline.Pipeline instance.
"""
all_transpositions = config.transpose_to_key is None
quantizer = pipelines_common.Quantizer(steps_per_quarter=4)
lead_sheet_extractor_train = lead_sheet_pipelines.LeadSheetExtractor(
min_bars=7,
max_steps=512,
min_unique_pitches=3,
gap_bars=1.0,
ignore_polyphonic_notes=False,
all_transpositions=all_transpositions,
name='LeadSheetExtractorTrain')
lead_sheet_extractor_eval = lead_sheet_pipelines.LeadSheetExtractor(
min_bars=7,
max_steps=512,
min_unique_pitches=3,
gap_bars=1.0,
ignore_polyphonic_notes=False,
all_transpositions=all_transpositions,
name='LeadSheetExtractorEval')
encoder_pipeline_train = EncoderPipeline(config,
name='EncoderPipelineTrain')
encoder_pipeline_eval = EncoderPipeline(config, name='EncoderPipelineEval')
partitioner = pipelines_common.RandomPartition(
music_pb2.NoteSequence, ['eval_lead_sheets', 'training_lead_sheets'],
[eval_ratio])
dag = {
quantizer: dag_pipeline.DagInput(music_pb2.NoteSequence),
partitioner: quantizer,
lead_sheet_extractor_train: partitioner['training_lead_sheets'],
lead_sheet_extractor_eval: partitioner['eval_lead_sheets'],
encoder_pipeline_train: lead_sheet_extractor_train,
encoder_pipeline_eval: lead_sheet_extractor_eval,
dag_pipeline.DagOutput('training_lead_sheets'): encoder_pipeline_train,
dag_pipeline.DagOutput('eval_lead_sheets'): encoder_pipeline_eval
}
return dag_pipeline.DAGPipeline(dag)
def testMelodyRNNPipeline(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,
[(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)])
note_seq.testing_lib.add_chords_to_sequence(note_sequence,
[('N.C.', 0.0),
('Am9', 5.0),
('D7', 10.0)])
quantizer = note_sequence_pipelines.Quantizer(steps_per_quarter=4)
lead_sheet_extractor = lead_sheet_pipelines.LeadSheetExtractor(
min_bars=7,
min_unique_pitches=5,
gap_bars=1.0,
ignore_polyphonic_notes=False,
all_transpositions=False)
conditional_encoding = note_seq.ConditionalEventSequenceEncoderDecoder(
note_seq.OneHotEventSequenceEncoderDecoder(
note_seq.MajorMinorChordOneHotEncoding()),
note_seq.OneHotEventSequenceEncoderDecoder(
note_seq.MelodyOneHotEncoding(self.config.min_note,
self.config.max_note)))
quantized = quantizer.transform(note_sequence)[0]
lead_sheet = lead_sheet_extractor.transform(quantized)[0]
lead_sheet.squash(self.config.min_note, self.config.max_note,
self.config.transpose_to_key)
encoded = pipelines_common.make_sequence_example(
*conditional_encoding.encode(lead_sheet.chords, lead_sheet.melody))
expected_result = {
'training_lead_sheets': [encoded],
'eval_lead_sheets': []
}
pipeline_inst = improv_rnn_pipeline.get_pipeline(self.config,
eval_ratio=0.0)
result = pipeline_inst.transform(note_sequence)
self.assertEqual(expected_result, result)
def testLeadSheetExtractor(self):
note_sequence = common_testing_lib.parse_test_proto(
music_pb2.NoteSequence, """
time_signatures: {
numerator: 4
denominator: 4}
tempos: {
qpm: 60}""")
music_testing_lib.add_track_to_sequence(note_sequence,
0, [(12, 100, 2, 4),
(11, 1, 6, 7)])
music_testing_lib.add_track_to_sequence(note_sequence,
1, [(12, 127, 2, 4),
(14, 50, 6, 8)])
music_testing_lib.add_chords_to_sequence(note_sequence, [('Cm7', 2),
('F9', 4),
('G7b9', 6)])
quantized_sequence = sequences_lib.quantize_note_sequence(
note_sequence, steps_per_quarter=1)
expected_melody_events = [[
NO_EVENT, NO_EVENT, 12, NO_EVENT, NOTE_OFF, NO_EVENT, 11
], [
NO_EVENT, NO_EVENT, 12, NO_EVENT, NOTE_OFF, NO_EVENT, 14, NO_EVENT
]]
expected_chord_events = [[
NO_CHORD, NO_CHORD, 'Cm7', 'Cm7', 'F9', 'F9', 'G7b9'
], [NO_CHORD, NO_CHORD, 'Cm7', 'Cm7', 'F9', 'F9', 'G7b9', 'G7b9']]
expected_lead_sheets = []
for melody_events, chord_events in zip(expected_melody_events,
expected_chord_events):
melody = melodies_lib.Melody(melody_events,
steps_per_quarter=1,
steps_per_bar=4)
chords = chords_lib.ChordProgression(chord_events,
steps_per_quarter=1,
steps_per_bar=4)
lead_sheet = lead_sheets_lib.LeadSheet(melody, chords)
expected_lead_sheets.append(lead_sheet)
unit = lead_sheet_pipelines.LeadSheetExtractor(
min_bars=1,
min_unique_pitches=1,
gap_bars=1,
all_transpositions=False)
self._unit_transform_test(unit, quantized_sequence,
expected_lead_sheets)
