def testTranspositionPipelineIgnoreOutOfRangeNotes(self):
   note_sequence = common_testing_lib.parse_test_proto(
       music_pb2.NoteSequence,
       """
       time_signatures: {
         numerator: 4
         denominator: 4}
       tempos: {
         qpm: 60}""")
   tp = note_sequence_pipelines.TranspositionPipeline(
       range(-1, 2), ignore_out_of_range_notes=True, min_pitch=0, max_pitch=12)
   testing_lib.add_track_to_sequence(
       note_sequence, 0,
       [(10, 100, 1.0, 2.0), (12, 100, 2.0, 4.0), (13, 100, 4.0, 5.0)])
   transposed = tp.transform(note_sequence)
   self.assertEqual(3, len(transposed))
   self.assertEqual(3, len(transposed[0].notes))
   self.assertEqual(2, len(transposed[1].notes))
   self.assertEqual(1, len(transposed[2].notes))
   self.assertEqual(9, transposed[0].notes[0].pitch)
   self.assertEqual(10, transposed[1].notes[0].pitch)
   self.assertEqual(11, transposed[2].notes[0].pitch)
   self.assertEqual(11, transposed[0].notes[1].pitch)
   self.assertEqual(12, transposed[1].notes[1].pitch)
   self.assertEqual(12, transposed[0].notes[2].pitch)
 def testTranspositionPipeline(self):
   note_sequence = common_testing_lib.parse_test_proto(
       music_pb2.NoteSequence,
       """
       time_signatures: {
         numerator: 4
         denominator: 4}
       tempos: {
         qpm: 60}""")
   tp = note_sequence_pipelines.TranspositionPipeline(range(0, 2))
   testing_lib.add_track_to_sequence(
       note_sequence, 0,
       [(12, 100, 1.0, 4.0)])
   testing_lib.add_track_to_sequence(
       note_sequence, 1,
       [(36, 100, 2.0, 2.01)],
       is_drum=True)
   transposed = tp.transform(note_sequence)
   self.assertEqual(2, len(transposed))
   self.assertEqual(2, len(transposed[0].notes))
   self.assertEqual(2, len(transposed[1].notes))
   self.assertEqual(12, transposed[0].notes[0].pitch)
   self.assertEqual(13, transposed[1].notes[0].pitch)
   self.assertEqual(36, transposed[0].notes[1].pitch)
   self.assertEqual(36, transposed[1].notes[1].pitch)
Beispiel #3
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def get_pipeline(config, min_events, max_events, eval_ratio):
    """Returns the Pipeline instance which creates the RNN dataset.

  Args:
    config: A PerformanceRnnConfig.
    min_events: Minimum number of events for an extracted sequence.
    max_events: Maximum number of events for an extracted sequence.
    eval_ratio: Fraction of input to set aside for evaluation set.

  Returns:
    A pipeline.Pipeline instance.
  """
    # Stretch by -5%, -2.5%, 0%, 2.5%, and 5%.
    stretch_factors = [0.95, 0.975, 1.0, 1.025, 1.05]

    # Transpose no more than a major third.
    transposition_range = range(-3, 4)

    partitioner = pipelines_common.RandomPartition(
        music_pb2.NoteSequence, ['eval_performances', 'training_performances'],
        [eval_ratio])
    dag = {partitioner: dag_pipeline.DagInput(music_pb2.NoteSequence)}

    for mode in ['eval', 'training']:
        sustain_pipeline = note_sequence_pipelines.SustainPipeline(
            name='SustainPipeline_' + mode)
        stretch_pipeline = note_sequence_pipelines.StretchPipeline(
            stretch_factors, name='StretchPipeline_' + mode)
        splitter = note_sequence_pipelines.Splitter(hop_size_seconds=30.0,
                                                    name='Splitter_' + mode)
        quantizer = note_sequence_pipelines.Quantizer(
            steps_per_second=config.steps_per_second, name='Quantizer_' + mode)
        transposition_pipeline = note_sequence_pipelines.TranspositionPipeline(
            transposition_range, name='TranspositionPipeline_' + mode)
        perf_extractor = PerformanceExtractor(
            min_events=min_events,
            max_events=max_events,
            num_velocity_bins=config.num_velocity_bins,
            name='PerformanceExtractor_' + mode)
        encoder_pipeline = EncoderPipeline(config,
                                           name='EncoderPipeline_' + mode)

        dag[sustain_pipeline] = partitioner[mode + '_performances']
        if mode == 'eval':
            # No stretching in eval.
            dag[splitter] = sustain_pipeline
        else:
            dag[stretch_pipeline] = sustain_pipeline
            dag[splitter] = stretch_pipeline
        dag[quantizer] = splitter
        if mode == 'eval':
            # No transposition in eval.
            dag[perf_extractor] = quantizer
        else:
            dag[transposition_pipeline] = quantizer
            dag[perf_extractor] = transposition_pipeline
        dag[encoder_pipeline] = perf_extractor
        dag[dag_pipeline.DagOutput(mode + '_performances')] = encoder_pipeline

    return dag_pipeline.DAGPipeline(dag)
def get_pipeline(config, eval_ratio=0.0):
    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 = note_sequence_pipelines.TimeChangeSplitter(
            name='TimeChangeSplitter_' + mode)
        repeat_sequence = RepeatSequence(min_duration=16,
                                         name='RepeatSequence_' + mode)
        transposition_pipeline = note_sequence_pipelines.TranspositionPipeline(
            (0, ), name='TranspositionPipeline_' + mode)
        quantizer = note_sequence_pipelines.Quantizer(
            steps_per_quarter=config.steps_per_quarter,
            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=True,
            name='MelodyExtractor_' + mode)
        encoder_pipeline = EncoderPipeline(config,
                                           name='EncoderPipeline_' + mode)

        dag[time_change_splitter] = partitioner[mode + '_melodies']
        dag[repeat_sequence] = time_change_splitter
        dag[quantizer] = repeat_sequence
        dag[transposition_pipeline] = quantizer
        dag[melody_extractor] = transposition_pipeline
        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.
  """
    # 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 = note_sequence_pipelines.TimeChangeSplitter(
            name='TimeChangeSplitter_' + mode)
        quantizer = note_sequence_pipelines.Quantizer(
            steps_per_quarter=config.steps_per_quarter,
            name='Quantizer_' + mode)
        transposition_pipeline = note_sequence_pipelines.TranspositionPipeline(
            transposition_range, name='TranspositionPipeline_' + mode)
        poly_extractor = PolyphonicSequenceExtractor(min_steps=min_steps,
                                                     max_steps=max_steps,
                                                     name='PolyExtractor_' +
                                                     mode)
        encoder_pipeline = event_sequence_pipeline.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)
Beispiel #6
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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.
  """
    # Transpose up to a major third in either direction.
    transposition_range = list(range(-4, 5))

    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 = note_sequence_pipelines.TimeChangeSplitter(
            name='TimeChangeSplitter_' + mode)
        quantizer = note_sequence_pipelines.Quantizer(
            steps_per_quarter=config.steps_per_quarter,
            name='Quantizer_' + mode)
        transposition_pipeline = note_sequence_pipelines.TranspositionPipeline(
            transposition_range, name='TranspositionPipeline_' + mode)
        pianoroll_extractor = PianorollSequenceExtractor(
            min_steps=min_steps,
            max_steps=max_steps,
            name='PianorollExtractor_' + mode)
        encoder_pipeline = event_sequence_pipeline.EncoderPipeline(
            mm.PianorollSequence,
            config.encoder_decoder,
            name='EncoderPipeline_' + mode)

        dag[time_change_splitter] = partitioner[mode + '_pianoroll_tracks']
        dag[quantizer] = time_change_splitter
        dag[transposition_pipeline] = quantizer
        dag[pianoroll_extractor] = transposition_pipeline
        dag[encoder_pipeline] = pianoroll_extractor
        dag[dag_pipeline.DagOutput(mode +
                                   '_pianoroll_tracks')] = encoder_pipeline

    return dag_pipeline.DAGPipeline(dag)
def get_pipeline(config, transposition_range=(0, ), eval_ratio=0.0):
    """Returns the Pipeline instance which creates the RNN dataset.

  Args:
    config: A MelodyRnnConfig object.
    transposition_range: Collection of integer pitch steps to transpose.
    eval_ratio: Fraction of input to set aside for evaluation set.

  Returns:
    A pipeline.Pipeline instance.
  """
    partitioner = pipelines_common.RandomPartition(
        note_seq.NoteSequence, ['eval_melodies', 'training_melodies'],
        [eval_ratio])
    dag = {partitioner: dag_pipeline.DagInput(note_seq.NoteSequence)}

    for mode in ['eval', 'training']:
        time_change_splitter = note_sequence_pipelines.TimeChangeSplitter(
            name='TimeChangeSplitter_' + mode)
        transposition_pipeline = note_sequence_pipelines.TranspositionPipeline(
            transposition_range, name='TranspositionPipeline_' + mode)
        quantizer = note_sequence_pipelines.Quantizer(
            steps_per_quarter=config.steps_per_quarter,
            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[transposition_pipeline] = quantizer
        dag[melody_extractor] = transposition_pipeline
        dag[encoder_pipeline] = melody_extractor
        dag[dag_pipeline.DagOutput(mode + '_melodies')] = encoder_pipeline

    return dag_pipeline.DAGPipeline(dag)