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 testTranspositionPipeline(self): tp = sequences_lib.TranspositionPipeline(range(0, 2)) testing_lib.add_track_to_sequence(self.note_sequence, 0, [(12, 100, 1.0, 4.0)]) transposed = tp.transform(self.note_sequence) self.assertEqual(2, len(transposed)) self.assertEqual(12, transposed[0].notes[0].pitch) self.assertEqual(13, transposed[1].notes[0].pitch)
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)