def get_pipeline(melody_encoder_decoder): """Returns the Pipeline instance which creates the RNN dataset. Args: melody_encoder_decoder: A melodies_lib.MelodyEncoderDecoder object. Returns: A pipeline.Pipeline instance. """ quantizer = pipelines_common.Quantizer(steps_per_beat=4) melody_extractor = pipelines_common.MonophonicMelodyExtractor( min_bars=7, min_unique_pitches=5, gap_bars=1.0, ignore_polyphonic_notes=False) encoder_pipeline = EncoderPipeline(melody_encoder_decoder) partitioner = pipelines_common.RandomPartition( tf.train.SequenceExample, ['eval_melodies', 'training_melodies'], [FLAGS.eval_ratio]) dag = { quantizer: dag_pipeline.Input(music_pb2.NoteSequence), melody_extractor: quantizer, encoder_pipeline: melody_extractor, partitioner: encoder_pipeline, dag_pipeline.Output(): partitioner } return dag_pipeline.DAGPipeline(dag)
def testMelodyRNNPipeline(self): FLAGS.eval_ratio = 0.0 note_sequence = testing_lib.parse_test_proto( music_pb2.NoteSequence, """ time_signatures: { numerator: 4 denominator: 4} tempos: { qpm: 120}""") testing_lib.add_track(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 = pipelines_common.Quantizer(steps_per_quarter=4) melody_extractor = pipelines_common.MonophonicMelodyExtractor( min_bars=7, min_unique_pitches=5, gap_bars=1.0, ignore_polyphonic_notes=False) one_hot_encoder = melodies_lib.OneHotEncoderDecoder(0, 127, 0) quantized = quantizer.transform(note_sequence)[0] print quantized.tracks melody = melody_extractor.transform(quantized)[0] one_hot = one_hot_encoder.encode(melody) print one_hot expected_result = {'training_melodies': [one_hot], 'eval_melodies': []} pipeline_inst = melody_rnn_create_dataset.get_pipeline(one_hot_encoder) 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 = pipelines_common.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_create_dataset.get_pipeline(self.config, eval_ratio=0.0) result = pipeline_inst.transform(note_sequence) self.assertEqual(expected_result, result)
def get_pipeline(config, eval_ratio): """Returns the Pipeline instance which creates the RNN dataset. Args: config: A DrumsRnnConfig object. eval_ratio: Fraction of input to set aside for evaluation set. Returns: A pipeline.Pipeline instance. """ partitioner = pipelines_common.RandomPartition( music_pb2.NoteSequence, ['eval_drum_tracks', 'training_drum_tracks'], [eval_ratio]) dag = {partitioner: dag_pipeline.DagInput(music_pb2.NoteSequence)} for mode in ['eval', 'training']: time_change_splitter = pipelines_common.TimeChangeSplitter( name='TimeChangeSplitter_' + mode) quantizer = pipelines_common.Quantizer( steps_per_quarter=config.steps_per_quarter, name='Quantizer_' + mode) drums_extractor = drum_pipelines.DrumsExtractor( min_bars=7, max_steps=512, gap_bars=1.0, name='DrumsExtractor_' + mode) encoder_pipeline = encoder_decoder.EncoderPipeline( magenta.music.DrumTrack, config.encoder_decoder, name='EncoderPipeline_' + mode) dag[time_change_splitter] = partitioner[mode + '_drum_tracks'] dag[quantizer] = time_change_splitter dag[drums_extractor] = quantizer dag[encoder_pipeline] = drums_extractor dag[dag_pipeline.DagOutput(mode + '_drum_tracks')] = encoder_pipeline return dag_pipeline.DAGPipeline(dag)
def testQuantizer(self): steps_per_quarter = 4 note_sequence = common_testing_lib.parse_test_proto( music_pb2.NoteSequence, """ time_signatures: { numerator: 4 denominator: 4} tempos: { qpm: 60}""") testing_lib.add_track_to_sequence(note_sequence, 0, [(12, 100, 0.01, 10.0), (11, 55, 0.22, 0.50), (40, 45, 2.50, 3.50), (55, 120, 4.0, 4.01), (52, 99, 4.75, 5.0)]) expected_quantized_sequence = sequences_lib.QuantizedSequence() expected_quantized_sequence.qpm = 60.0 expected_quantized_sequence.steps_per_quarter = steps_per_quarter testing_lib.add_quantized_track_to_sequence( expected_quantized_sequence, 0, [(12, 100, 0, 40), (11, 55, 1, 2), (40, 45, 10, 14), (55, 120, 16, 17), (52, 99, 19, 20)]) unit = pipelines_common.Quantizer(steps_per_quarter) self._unit_transform_test(unit, note_sequence, [expected_quantized_sequence])
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 = pipelines_common.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_create_dataset.get_pipeline(self.config, eval_ratio=0.0) result = pipeline_inst.transform(note_sequence) self.assertEqual(expected_result, result)
def testMelodyRNNPipeline(self): note_sequence = testing_lib.parse_test_proto( music_pb2.NoteSequence, """ time_signatures: { numerator: 4 denominator: 4} tempos: { bpm: 120}""") testing_lib.add_track( note_sequence, 0, [(12, 100, 0.01, 10.0), (11, 55, 0.22, 0.50), (40, 45, 2.50, 3.50), (55, 120, 4.0, 4.01), (52, 99, 4.75, 5.0)]) quantizer = pipelines_common.Quantizer(steps_per_beat=4) melody_extractor = pipelines_common.MonophonicMelodyExtractor( min_bars=7, min_unique_pitches=5, gap_bars=1.0) one_hot_encoder = melody_rnn_create_dataset.OneHotEncoder() quantized = quantizer.transform(note_sequence)[0] melody = melody_extractor.transform(quantized)[0] one_hot = one_hot_encoder.transform(melody)[0] expected_result = {'melody_rnn_train': [one_hot], 'melody_rnn_eval': []} pipeline_inst = melody_rnn_create_dataset.MelodyRNNPipeline(eval_ratio=0) result = pipeline_inst.transform(note_sequence) self.assertEqual(expected_result, result)
def get_pipeline(config, eval_ratio): """Returns the Pipeline instance which creates the RNN dataset. Args: config: A DrumsRnnConfig object. eval_ratio: Fraction of input to set aside for evaluation set. Returns: A pipeline.Pipeline instance. """ quantizer = pipelines_common.Quantizer(steps_per_quarter=4) drums_extractor_train = drum_pipelines.DrumsExtractor( min_bars=7, max_steps=512, gap_bars=1.0, name='DrumsExtractorTrain') drums_extractor_eval = drum_pipelines.DrumsExtractor( min_bars=7, max_steps=512, gap_bars=1.0, name='DrumsExtractorEval') encoder_pipeline_train = EncoderPipeline(config, name='EncoderPipelineTrain') encoder_pipeline_eval = EncoderPipeline(config, name='EncoderPipelineEval') partitioner = pipelines_common.RandomPartition( music_pb2.NoteSequence, ['eval_drum_tracks', 'training_drum_tracks'], [eval_ratio]) dag = { quantizer: dag_pipeline.Input(music_pb2.NoteSequence), partitioner: quantizer, drums_extractor_train: partitioner['training_drum_tracks'], drums_extractor_eval: partitioner['eval_drum_tracks'], encoder_pipeline_train: drums_extractor_train, encoder_pipeline_eval: drums_extractor_eval, dag_pipeline.Output('training_drum_tracks'): encoder_pipeline_train, dag_pipeline.Output('eval_drum_tracks'): encoder_pipeline_eval } return dag_pipeline.DAGPipeline(dag)
def get_pipeline(config, eval_ratio): """Returns the Pipeline instance which creates the RNN dataset. Args: config: A MelodyRnnConfig object. eval_ratio: Fraction of input to set aside for evaluation set. Returns: A pipeline.Pipeline instance. A id_pipeline.IDPipeline instance. """ quantizer = pipelines_common.Quantizer(steps_per_quarter=4) melody_extractor = melody_pipelines.MelodyExtractor( min_bars=7, max_steps=512, min_unique_pitches=5, gap_bars=1.0, ignore_polyphonic_notes=False) id_pipeline = pipelines_common.IDPipeline() encoder_pipeline = EncoderPipeline(config) partitioner = pipelines_common.RandomPartition( tf.train.SequenceExample, ['eval_melodies', 'training_melodies'], [eval_ratio]) dag = { quantizer: dag_pipeline.DagInput(music_pb2.NoteSequence), melody_extractor: quantizer, id_pipeline: melody_extractor, encoder_pipeline: id_pipeline, partitioner: encoder_pipeline, dag_pipeline.DagOutput(): partitioner } return dag_pipeline.DAGPipeline(dag), id_pipeline
def midi_files_to_sequence_proto(midi_files, batch_size, input_size): all_sequences = filter(lambda x: x != None, [ midi_file_to_sequence_proto(midi_file, batch_size, input_size) for midi_file in midi_files ]) examples = [] #print("All our events are: ", all_events) unit = melody_pipelines.MelodyExtractor(min_bars=1, min_unique_pitches=1, gap_bars=1) melody_extractor = melody_pipelines.MelodyExtractor( min_bars=7, min_unique_pitches=5, gap_bars=1.0, ignore_polyphonic_notes=False) quantizer = pipelines_common.Quantizer(steps_per_quarter=4) one_hot_encoding = magenta.music.OneHotEventSequenceEncoderDecoder( magenta.music.MelodyOneHotEncoding(0, 128)) for note_sequence in all_sequences: #quantized_sequence = quantize_note_sequence( # note_sequence, steps_per_quarter=1) #quantized_sequence = mm.quantize_note_sequence(note_sequence, steps_per_quarter=1) #outputs = unit.transform(quantized_sequence) #single_quant = quantizer.transform(note_sequence) #print("single quant seq is: ", single_quant) # quantized = quantizer.transform(note_sequence) #print("single quant is: ", single_quant) single_quant = note_sequence melody = melody_extractor.transform(single_quant) print("outputs/melody is ", outputs) #melody = outputs # self.config.min_note, # self.config.max_note, # self.config.transpose_to_key) melody.squash(0, 128, 0) one_hot = one_hot_encoding.encode(melody) examples.append(one_hot) #seq_example = #seq_example = make_sequence_example(inputs, labels) #examples.append(seq_example) # melody = melodies_lib.Melody(events.notes) # melody.squash(0,128,0) # examples.append(self.med.encode(melody)) print("done with a batch!!!") return get_padded_batch(examples, batch_size, input_size)
def get_pipeline(config, steps_per_quarter, min_steps, max_steps, eval_ratio): """Returns the Pipeline instance which creates the RNN dataset. Args: config: An EventSequenceRnnConfig. steps_per_quarter: How many steps per quarter to use when quantizing. min_steps: Minimum number of steps for an extracted sequence. max_steps: Maximum number of steps for an extracted sequence. eval_ratio: Fraction of input to set aside for evaluation set. Returns: A pipeline.Pipeline instance. """ quantizer = pipelines_common.Quantizer(steps_per_quarter=steps_per_quarter) # Transpose up to a major third in either direction. # Because our current dataset is Bach chorales, transposing more than a major # third in either direction probably doesn't makes sense (e.g., because it is # likely to exceed normal singing range). transposition_range = range(-4, 5) transposition_pipeline_train = sequences_lib.TranspositionPipeline( transposition_range, name='TranspositionPipelineTrain') transposition_pipeline_eval = sequences_lib.TranspositionPipeline( transposition_range, name='TranspositionPipelineEval') poly_extractor_train = PolyphonicSequenceExtractor( min_steps=min_steps, max_steps=max_steps, name='PolyExtractorTrain') poly_extractor_eval = PolyphonicSequenceExtractor(min_steps=min_steps, max_steps=max_steps, name='PolyExtractorEval') encoder_pipeline_train = encoder_decoder.EncoderPipeline( polyphony_lib.PolyphonicSequence, config.encoder_decoder, name='EncoderPipelineTrain') encoder_pipeline_eval = encoder_decoder.EncoderPipeline( polyphony_lib.PolyphonicSequence, config.encoder_decoder, name='EncoderPipelineEval') partitioner = pipelines_common.RandomPartition( music_pb2.NoteSequence, ['eval_poly_tracks', 'training_poly_tracks'], [eval_ratio]) dag = { quantizer: dag_pipeline.DagInput(music_pb2.NoteSequence), partitioner: quantizer, transposition_pipeline_train: partitioner['training_poly_tracks'], transposition_pipeline_eval: partitioner['eval_poly_tracks'], poly_extractor_train: transposition_pipeline_train, poly_extractor_eval: transposition_pipeline_eval, encoder_pipeline_train: poly_extractor_train, encoder_pipeline_eval: poly_extractor_eval, dag_pipeline.DagOutput('training_poly_tracks'): encoder_pipeline_train, dag_pipeline.DagOutput('eval_poly_tracks'): encoder_pipeline_eval } return dag_pipeline.DAGPipeline(dag)
def get_pipeline(config, steps_per_quarter, min_steps, max_steps, eval_ratio): """Returns the Pipeline instance which creates the RNN dataset. Args: config: An EventSequenceRnnConfig. steps_per_quarter: How many steps per quarter to use when quantizing. min_steps: Minimum number of steps for an extracted sequence. max_steps: Maximum number of steps for an extracted sequence. eval_ratio: Fraction of input to set aside for evaluation set. Returns: A pipeline.Pipeline instance. """ # Transpose up to a major third in either direction. # Because our current dataset is Bach chorales, transposing more than a major # third in either direction probably doesn't makes sense (e.g., because it is # likely to exceed normal singing range). transposition_range = range(-4, 5) partitioner = pipelines_common.RandomPartition( music_pb2.NoteSequence, ['eval_poly_tracks', 'training_poly_tracks'], [eval_ratio]) dag = {partitioner: dag_pipeline.DagInput(music_pb2.NoteSequence)} for mode in ['eval', 'training']: time_change_splitter = pipelines_common.TimeChangeSplitter( name='TimeChangeSplitter_' + mode) quantizer = pipelines_common.Quantizer( steps_per_quarter=steps_per_quarter, name='Quantizer_' + mode) transposition_pipeline = sequences_lib.TranspositionPipeline( transposition_range, name='TranspositionPipeline_' + mode) poly_extractor = PolyphonicSequenceExtractor(min_steps=min_steps, max_steps=max_steps, name='PolyExtractor_' + mode) encoder_pipeline = encoder_decoder.EncoderPipeline( polyphony_lib.PolyphonicSequence, config.encoder_decoder, name='EncoderPipeline_' + mode) dag[time_change_splitter] = partitioner[mode + '_poly_tracks'] dag[quantizer] = time_change_splitter dag[transposition_pipeline] = quantizer dag[poly_extractor] = transposition_pipeline dag[encoder_pipeline] = poly_extractor dag[dag_pipeline.DagOutput(mode + '_poly_tracks')] = 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( 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)]) magenta.music.testing_lib.add_chords_to_sequence( note_sequence, [('N.C.', 0.0), ('Am9', 5.0), ('D7', 10.0)]) quantizer = pipelines_common.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 = magenta.music.ConditionalEventSequenceEncoderDecoder( magenta.music.OneHotEventSequenceEncoderDecoder( magenta.music.MajorMinorChordOneHotEncoding()), magenta.music.OneHotEventSequenceEncoderDecoder( magenta.music.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 = conditional_encoding.encode(lead_sheet.chords, lead_sheet.melody) expected_result = { 'training_lead_sheets': [encoded], 'eval_lead_sheets': [] } pipeline_inst = improv_rnn_create_dataset.get_pipeline(self.config, eval_ratio=0.0) result = pipeline_inst.transform(note_sequence) self.assertEqual(expected_result, result)
def __init__(self, melody_encoder_decoder, eval_ratio): self.training_set_name = 'training_melodies' self.eval_set_name = 'eval_melodies' super(MelodyRNNPipeline, self).__init__(input_type=music_pb2.NoteSequence, output_type={ self.training_set_name: tf.train.SequenceExample, self.eval_set_name: tf.train.SequenceExample }) self.eval_ratio = eval_ratio self.quantizer = pipelines_common.Quantizer(steps_per_beat=4) self.melody_extractor = pipelines_common.MonophonicMelodyExtractor( min_bars=7, min_unique_pitches=5, gap_bars=1.0, ignore_polyphonic_notes=False) self.encoder_unit = EncoderPipeline(melody_encoder_decoder) self.stats_dict = {}
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 = pipelines_common.TimeChangeSplitter( name='TimeChangeSplitter_' + mode) quantizer = pipelines_common.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, steps_per_quarter, min_steps, max_steps, eval_ratio): """Returns the Pipeline instance which creates the RNN dataset. Args: config: An EventSequenceRnnConfig. steps_per_quarter: How many steps per quarter to use when quantizing. min_steps: Minimum number of steps for an extracted sequence. max_steps: Maximum number of steps for an extracted sequence. eval_ratio: Fraction of input to set aside for evaluation set. Returns: A pipeline.Pipeline instance. """ quantizer = pipelines_common.Quantizer(steps_per_quarter=steps_per_quarter) poly_extractor_train = PolyphonicSequenceExtractor( min_steps=min_steps, max_steps=max_steps, name='PolyExtractorTrain') poly_extractor_eval = PolyphonicSequenceExtractor( min_steps=min_steps, max_steps=max_steps, name='PolyExtractorEval') encoder_pipeline_train = encoder_decoder.EncoderPipeline( polyphony_lib.PolyphonicSequence, config.encoder_decoder, name='EncoderPipelineTrain') encoder_pipeline_eval = encoder_decoder.EncoderPipeline( polyphony_lib.PolyphonicSequence, config.encoder_decoder, name='EncoderPipelineEval') partitioner = pipelines_common.RandomPartition( music_pb2.NoteSequence, ['eval_poly_tracks', 'training_poly_tracks'], [eval_ratio]) dag = {quantizer: dag_pipeline.DagInput(music_pb2.NoteSequence), partitioner: quantizer, poly_extractor_train: partitioner['training_poly_tracks'], poly_extractor_eval: partitioner['eval_poly_tracks'], encoder_pipeline_train: poly_extractor_train, encoder_pipeline_eval: poly_extractor_eval, dag_pipeline.DagOutput('training_poly_tracks'): encoder_pipeline_train, dag_pipeline.DagOutput('eval_poly_tracks'): encoder_pipeline_eval} return dag_pipeline.DAGPipeline(dag)
def get_pipeline(config, eval_ratio): """Returns the Pipeline instance which creates the RNN dataset. Args: config: A MelodyRnnConfig object. eval_ratio: Fraction of input to set aside for evaluation set. Returns: A pipeline.Pipeline instance. """ partitioner = pipelines_common.RandomPartition( music_pb2.NoteSequence, ['eval_melodies', 'training_melodies'], [eval_ratio]) dag = {partitioner: dag_pipeline.DagInput(music_pb2.NoteSequence)} for mode in ['eval', 'training']: time_change_splitter = pipelines_common.TimeChangeSplitter( name='TimeChangeSplitter_' + mode) quantizer = pipelines_common.Quantizer(steps_per_quarter=4, name='Quantizer_' + mode) melody_extractor = melody_pipelines.MelodyExtractor( min_bars=7, max_steps=512, min_unique_pitches=5, gap_bars=1.0, ignore_polyphonic_notes=False, name='MelodyExtractor_' + mode) encoder_pipeline = EncoderPipeline(config, name='EncoderPipeline_' + mode) dag[time_change_splitter] = partitioner[mode + '_melodies'] dag[quantizer] = time_change_splitter dag[melody_extractor] = quantizer dag[encoder_pipeline] = melody_extractor dag[dag_pipeline.DagOutput(mode + '_melodies')] = encoder_pipeline return dag_pipeline.DAGPipeline(dag)
def get_pipeline(config, min_steps, max_steps, eval_ratio): """Returns the Pipeline instance which creates the RNN dataset. Args: config: An EventSequenceRnnConfig. min_steps: Minimum number of steps for an extracted sequence. max_steps: Maximum number of steps for an extracted sequence. eval_ratio: Fraction of input to set aside for evaluation set. Returns: A pipeline.Pipeline instance. """ partitioner = pipelines_common.RandomPartition( music_pb2.NoteSequence, ['eval_pianoroll_tracks', 'training_pianoroll_tracks'], [eval_ratio]) dag = {partitioner: dag_pipeline.DagInput(music_pb2.NoteSequence)} for mode in ['eval', 'training']: time_change_splitter = pipelines_common.TimeChangeSplitter( name='TimeChangeSplitter_' + mode) quantizer = pipelines_common.Quantizer( steps_per_quarter=config.steps_per_quarter, name='Quantizer_' + mode) pianoroll_extractor = PianorollSequenceExtractor( min_steps=min_steps, max_steps=max_steps, name='PianorollExtractor_' + mode) encoder_pipeline = mm.EncoderPipeline( mm.PianorollSequence, config.encoder_decoder, name='EncoderPipeline_' + mode) dag[time_change_splitter] = partitioner[mode + '_pianoroll_tracks'] dag[quantizer] = time_change_splitter dag[pianoroll_extractor] = quantizer dag[encoder_pipeline] = pianoroll_extractor dag[dag_pipeline.DagOutput(mode + '_pianoroll_tracks')] = encoder_pipeline return dag_pipeline.DAGPipeline(dag)