def prepare_testing_datasets( config: Config, speech_featurizer: SpeechFeaturizer, text_featurizer: TextFeaturizer, ): test_dataset = asr_dataset.ASRSliceDataset( speech_featurizer=speech_featurizer, text_featurizer=text_featurizer, **vars(config.learning_config.test_dataset_config) ) return test_dataset
def prepare_training_datasets( config: Config, speech_featurizer: SpeechFeaturizer, text_featurizer: TextFeaturizer, tfrecords: bool = False, metadata: str = None, ): if tfrecords: train_dataset = asr_dataset.ASRTFRecordDataset( speech_featurizer=speech_featurizer, text_featurizer=text_featurizer, **vars(config.learning_config.train_dataset_config), indefinite=True ) eval_dataset = asr_dataset.ASRTFRecordDataset( speech_featurizer=speech_featurizer, text_featurizer=text_featurizer, **vars(config.learning_config.eval_dataset_config), indefinite=True ) else: train_dataset = asr_dataset.ASRSliceDataset( speech_featurizer=speech_featurizer, text_featurizer=text_featurizer, **vars(config.learning_config.train_dataset_config), indefinite=True ) eval_dataset = asr_dataset.ASRSliceDataset( speech_featurizer=speech_featurizer, text_featurizer=text_featurizer, **vars(config.learning_config.eval_dataset_config), indefinite=True ) train_dataset.load_metadata(metadata) eval_dataset.load_metadata(metadata) return train_dataset, eval_dataset
if args.tfrecords: train_dataset = asr_dataset.ASRTFRecordDataset( speech_featurizer=speech_featurizer, text_featurizer=text_featurizer, **vars(config.learning_config.train_dataset_config), indefinite=True) eval_dataset = asr_dataset.ASRTFRecordDataset( speech_featurizer=speech_featurizer, text_featurizer=text_featurizer, **vars(config.learning_config.eval_dataset_config), indefinite=True) else: train_dataset = asr_dataset.ASRSliceDataset( speech_featurizer=speech_featurizer, text_featurizer=text_featurizer, **vars(config.learning_config.train_dataset_config), indefinite=True) eval_dataset = asr_dataset.ASRSliceDataset( speech_featurizer=speech_featurizer, text_featurizer=text_featurizer, **vars(config.learning_config.eval_dataset_config), indefinite=True) train_dataset.load_metadata(args.metadata) eval_dataset.load_metadata(args.metadata) if not args.static_length: speech_featurizer.reset_length() text_featurizer.reset_length()