def run(args): config = UserConfig(DEFAULT_YAML, args.config, learning=True) speech_featurizer = TFSpeechFeaturizer(config["speech_config"]) text_featurizer = TextFeaturizer(config["decoder_config"]) tf.random.set_seed(0) assert args.saved_model if args.tfrecords: test_dataset = ASRTFRecordDataset( config["learning_config"]["dataset_config"]["test_paths"], config["learning_config"]["dataset_config"]["tfrecords_dir"], speech_featurizer, text_featurizer, "test", augmentations=config["learning_config"]["augmentations"], shuffle=False).create( config["learning_config"]["running_config"]["batch_size"]) else: test_dataset = ASRSliceDataset( stage="test", speech_featurizer=speech_featurizer, text_featurizer=text_featurizer, data_paths=config["learning_config"]["dataset_config"] ["eval_paths"], shuffle=False).create( config["learning_config"]["running_config"]["batch_size"]) # build model f, c = speech_featurizer.compute_feature_dim() conformer = Conformer(vocabulary_size=text_featurizer.num_classes, **config["model_config"]) conformer._build([1, 50, f, c]) conformer.summary(line_length=100) conformer_tester = BaseTester( config=config["learning_config"]["running_config"], saved_path=args.saved_model, from_weights=args.from_weights) conformer_tester.compile(conformer, speech_featurizer, text_featurizer) conformer_tester.run(test_dataset)
help="TFLite file path to be exported") args = parser.parse_args() assert args.saved and args.output config = UserConfig(DEFAULT_YAML, args.config, learning=True) speech_featurizer = TFSpeechFeaturizer(config["speech_config"]) text_featurizer = CharFeaturizer(config["decoder_config"]) # build model conformer = Conformer(**config["model_config"], vocabulary_size=text_featurizer.num_classes) conformer._build(speech_featurizer.shape) conformer.load_weights(args.saved) conformer.summary(line_length=150) conformer.add_featurizers(speech_featurizer, text_featurizer) concrete_func = conformer.make_tflite_function( greedy=True).get_concrete_function() converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func]) converter.optimizations = [tf.lite.Optimize.DEFAULT] converter.target_spec.supported_ops = [ tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS ] tflite_model = converter.convert() if not os.path.exists(os.path.dirname(args.output)): os.makedirs(os.path.dirname(args.output)) with open(args.output, "wb") as tflite_out: tflite_out.write(tflite_model)