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 prime_model(self): """Primes the model with its default midi primer.""" with self.graph.as_default(): tf.logging.debug('Priming the model with MIDI file %s', self.midi_primer) # Convert primer Melody to model inputs. encoder = note_seq.OneHotEventSequenceEncoderDecoder( note_seq.MelodyOneHotEncoding(min_note=rl_tuner_ops.MIN_NOTE, max_note=rl_tuner_ops.MAX_NOTE)) primer_input, _ = encoder.encode(self.primer) # Run model over primer sequence. primer_input_batch = np.tile([primer_input], (self.batch_size, 1, 1)) self.state_value, softmax = self.session.run( [self.state_tensor, self.softmax], feed_dict={ self.initial_state: self.state_value, self.melody_sequence: primer_input_batch, self.lengths: np.full(self.batch_size, len(self.primer), dtype=int) }) priming_output = softmax[-1, :] self.priming_note = self.get_note_from_softmax(priming_output)
def setUp(self): super().setUp() self.config = events_rnn_model.EventSequenceRnnConfig( None, note_seq.OneHotEventSequenceEncoderDecoder( note_seq.MultiDrumOneHotEncoding()), contrib_training.HParams())
def setUp(self): super(PerformancePipelineTest, self).setUp() self.config = performance_model.PerformanceRnnConfig( None, note_seq.OneHotEventSequenceEncoderDecoder( note_seq.PerformanceOneHotEncoding()), contrib_training.HParams())
def setUp(self): super().setUp() self.config = events_rnn_model.EventSequenceRnnConfig( None, note_seq.OneHotEventSequenceEncoderDecoder( polyphony_encoder_decoder.PolyphonyOneHotEncoding()), contrib_training.HParams())
def testDrumsRNNPipeline(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, [(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 = note_sequence_pipelines.Quantizer(steps_per_quarter=4) drums_extractor = drum_pipelines.DrumsExtractor(min_bars=7, gap_bars=1.0) one_hot_encoding = note_seq.OneHotEventSequenceEncoderDecoder( note_seq.MultiDrumOneHotEncoding()) quantized = quantizer.transform(note_sequence)[0] drums = drums_extractor.transform(quantized)[0] one_hot = pipelines_common.make_sequence_example( *one_hot_encoding.encode(drums)) expected_result = {'training_drum_tracks': [one_hot], 'eval_drum_tracks': []} pipeline_inst = drums_rnn_pipeline.get_pipeline( self.config, eval_ratio=0.0) result = pipeline_inst.transform(note_sequence) self.assertEqual(expected_result, result)
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 setUp(self): super().setUp() self.config = melody_rnn_model.MelodyRnnConfig( None, note_seq.OneHotEventSequenceEncoderDecoder( note_seq.MelodyOneHotEncoding(0, 127)), contrib_training.HParams(), min_note=0, max_note=127, transpose_to_key=0)
def one_hot_melody_encoder_decoder(min_note, max_note): """Return a OneHotEventSequenceEncoderDecoder for melodies. Args: min_note: The minimum midi pitch the encoded melodies can have. max_note: The maximum midi pitch (exclusive) the encoded melodies can have. Returns: A melody OneHotEventSequenceEncoderDecoder. """ return note_seq.OneHotEventSequenceEncoderDecoder( note_seq.MelodyOneHotEncoding(min_note, max_note))
def setUp(self): self._sequence_file = tempfile.NamedTemporaryFile( prefix='EventSequenceRNNGraphTest') self.config = events_rnn_model.EventSequenceRnnConfig( None, note_seq.OneHotEventSequenceEncoderDecoder( testing_lib.TrivialOneHotEncoding(12)), contrib_training.HParams(batch_size=128, rnn_layer_sizes=[128, 128], dropout_keep_prob=0.5, clip_norm=5, learning_rate=0.01))
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)]) quantizer = note_sequence_pipelines.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 = note_seq.OneHotEventSequenceEncoderDecoder( note_seq.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 = pipelines_common.make_sequence_example( *one_hot_encoding.encode(melody)) expected_result = {'training_melodies': [one_hot], 'eval_melodies': []} pipeline_inst = melody_rnn_pipeline.get_pipeline(self.config, eval_ratio=0.0) result = pipeline_inst.transform(note_sequence) self.assertEqual(expected_result, result)
# open hi-hat [46, 67, 72, 74, 79, 81, 26, 49, 52, 55, 57, 58, # crash 51, 53, 59, 82], # ride ] # Default configurations. default_configs = { 'one_drum': events_rnn_model.EventSequenceRnnConfig( generator_pb2.GeneratorDetails( id='one_drum', description='Drums RNN with 2-state encoding.'), note_seq.OneHotEventSequenceEncoderDecoder( note_seq.MultiDrumOneHotEncoding( [[39] + # use hand clap as default when decoding list(range(note_seq.MIN_MIDI_PITCH, 39)) + list(range(39, note_seq.MAX_MIDI_PITCH + 1))])), contrib_training.HParams( batch_size=128, rnn_layer_sizes=[128, 128], dropout_keep_prob=0.5, clip_norm=5, learning_rate=0.001), steps_per_quarter=2), 'drum_kit': events_rnn_model.EventSequenceRnnConfig( generator_pb2.GeneratorDetails( id='drum_kit', description='Drums RNN with multiple drums and binary counters.' ),
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 default_configs = { 'performance': PerformanceRnnConfig( generator_pb2.GeneratorDetails( id='performance', description='Performance RNN'), note_seq.OneHotEventSequenceEncoderDecoder( note_seq.PerformanceOneHotEncoding()), contrib_training.HParams( batch_size=64, rnn_layer_sizes=[512, 512, 512], dropout_keep_prob=1.0, clip_norm=3, learning_rate=0.001)), 'performance_with_dynamics': PerformanceRnnConfig( generator_pb2.GeneratorDetails( id='performance_with_dynamics', description='Performance RNN with dynamics'), note_seq.OneHotEventSequenceEncoderDecoder( note_seq.PerformanceOneHotEncoding(num_velocity_bins=32)), contrib_training.HParams( batch_size=64,
branch_factor, steps_per_iteration, modify_events_callback=modify_events_callback) def polyphonic_sequence_log_likelihood(self, sequence): """Evaluate the log likelihood of a polyphonic sequence. Args: sequence: The PolyphonicSequence object for which to evaluate the log likelihood. Returns: The log likelihood of `sequence` under this model. """ return self._evaluate_log_likelihood([sequence])[0] default_configs = { 'polyphony': events_rnn_model.EventSequenceRnnConfig( generator_pb2.GeneratorDetails(id='polyphony', description='Polyphonic RNN'), note_seq.OneHotEventSequenceEncoderDecoder( polyphony_encoder_decoder.PolyphonyOneHotEncoding()), contrib_training.HParams(batch_size=64, rnn_layer_sizes=[256, 256, 256], dropout_keep_prob=0.5, clip_norm=5, learning_rate=0.001)), }
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.'),
raise ValueError('transpose_to_key must be >= 0 and <= 11. ' 'transpose_to_key is %d.' % transpose_to_key) self.min_note = min_note self.max_note = max_note self.transpose_to_key = transpose_to_key # Default configurations. default_configs = { 'basic_rnn': MelodyRnnConfig( generator_pb2.GeneratorDetails( id='basic_rnn', description='Melody RNN with one-hot encoding.'), 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=[128, 128], dropout_keep_prob=0.5, clip_norm=5, learning_rate=0.001)), 'mono_rnn': MelodyRnnConfig(generator_pb2.GeneratorDetails( id='mono_rnn', description='Monophonic RNN with one-hot encoding.'), note_seq.OneHotEventSequenceEncoderDecoder( note_seq.MelodyOneHotEncoding(min_note=0, max_note=128)), contrib_training.HParams(batch_size=128, rnn_layer_sizes=[128, 128], dropout_keep_prob=0.5,