def setUp(self): super().setUp() self.config = improv_rnn_model.ImprovRnnConfig( None, note_seq.ConditionalEventSequenceEncoderDecoder( note_seq.OneHotEventSequenceEncoderDecoder( note_seq.MajorMinorChordOneHotEncoding()), note_seq.OneHotEventSequenceEncoderDecoder( note_seq.MelodyOneHotEncoding(0, 127))), contrib_training.HParams(), min_note=0, max_note=127, transpose_to_key=0)
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 __init__(self, details, encoder_decoder, hparams, num_velocity_bins=0, control_signals=None, optional_conditioning=False, note_performance=False): if control_signals is not None: control_encoder = note_seq.MultipleEventSequenceEncoder( [control.encoder for control in control_signals]) if optional_conditioning: control_encoder = note_seq.OptionalEventSequenceEncoder(control_encoder) encoder_decoder = note_seq.ConditionalEventSequenceEncoderDecoder( control_encoder, encoder_decoder) super(PerformanceRnnConfig, self).__init__( details, encoder_decoder, hparams) self.num_velocity_bins = num_velocity_bins self.control_signals = control_signals self.optional_conditioning = optional_conditioning self.note_performance = note_performance
self.min_note = min_note self.max_note = max_note self.transpose_to_key = transpose_to_key # Default configurations. default_configs = { 'basic_improv': ImprovRnnConfig( generator_pb2.GeneratorDetails( id='basic_improv', description='Basic melody-given-chords RNN with one-hot triad ' 'encoding for chords.'), note_seq.ConditionalEventSequenceEncoderDecoder( note_seq.OneHotEventSequenceEncoderDecoder( note_seq.TriadChordOneHotEncoding()), note_seq.OneHotEventSequenceEncoderDecoder( note_seq.MelodyOneHotEncoding(min_note=DEFAULT_MIN_NOTE, max_note=DEFAULT_MAX_NOTE))), contrib_training.HParams(batch_size=128, rnn_layer_sizes=[64, 64], dropout_keep_prob=0.5, clip_norm=5, learning_rate=0.001)), 'attention_improv': ImprovRnnConfig( generator_pb2.GeneratorDetails( id='attention_improv', description= 'Melody-given-chords RNN with one-hot triad encoding for ' 'chords, attention, and binary counters.'), note_seq.ConditionalEventSequenceEncoderDecoder(