def main( config: str = DEFAULT_YAML, saved: str = None, mxp: bool = False, bs: int = None, sentence_piece: bool = False, subwords: bool = False, device: int = 0, cpu: bool = False, output: str = "test.tsv", ): assert saved and output tf.random.set_seed(0) tf.keras.backend.clear_session() tf.config.optimizer.set_experimental_options({"auto_mixed_precision": mxp}) env_util.setup_devices([device], cpu=cpu) config = Config(config) speech_featurizer, text_featurizer = featurizer_helpers.prepare_featurizers( config=config, subwords=subwords, sentence_piece=sentence_piece, ) jasper = Jasper(**config.model_config, vocabulary_size=text_featurizer.num_classes) jasper.make(speech_featurizer.shape) jasper.load_weights(saved, by_name=True) jasper.summary(line_length=100) jasper.add_featurizers(speech_featurizer, text_featurizer) test_dataset = dataset_helpers.prepare_testing_datasets( config=config, speech_featurizer=speech_featurizer, text_featurizer=text_featurizer) batch_size = bs or config.learning_config.running_config.batch_size test_data_loader = test_dataset.create(batch_size) exec_helpers.run_testing(model=jasper, test_dataset=test_dataset, test_data_loader=test_data_loader, output=output)
def test_jasper(): config = Config(DEFAULT_YAML) text_featurizer = CharFeaturizer(config.decoder_config) speech_featurizer = TFSpeechFeaturizer(config.speech_config) model = Jasper(vocabulary_size=text_featurizer.num_classes, **config.model_config) model.make(speech_featurizer.shape) model.summary(line_length=150) model.add_featurizers(speech_featurizer=speech_featurizer, text_featurizer=text_featurizer) concrete_func = model.make_tflite_function( greedy=False).get_concrete_function() converter = tf.lite.TFLiteConverter.from_concrete_functions( [concrete_func]) converter.optimizations = [tf.lite.Optimize.DEFAULT] converter.experimental_new_converter = True converter.target_spec.supported_ops = [ tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS ] converter.convert() print("Converted successfully with beam search") concrete_func = model.make_tflite_function( greedy=True).get_concrete_function() converter = tf.lite.TFLiteConverter.from_concrete_functions( [concrete_func]) converter.optimizations = [tf.lite.Optimize.DEFAULT] converter.experimental_new_converter = True converter.target_spec.supported_ops = [ tf.lite.OpsSet.TFLITE_BUILTINS, tf.lite.OpsSet.SELECT_TF_OPS ] converter.convert() print("Converted successfully with greedy")
test_dataset = ASRTFRecordDataset( speech_featurizer=speech_featurizer, text_featurizer=text_featurizer, **vars(config.learning_config.test_dataset_config)) else: test_dataset = ASRSliceDataset( speech_featurizer=speech_featurizer, text_featurizer=text_featurizer, **vars(config.learning_config.test_dataset_config)) # build model jasper = Jasper(**config.model_config, vocabulary_size=text_featurizer.num_classes) jasper.make(speech_featurizer.shape) jasper.load_weights(args.saved) jasper.summary(line_length=100) jasper.add_featurizers(speech_featurizer, text_featurizer) batch_size = args.bs or config.learning_config.running_config.batch_size test_data_loader = test_dataset.create(batch_size) with file_util.save_file(file_util.preprocess_paths(args.output)) as filepath: overwrite = True if tf.io.gfile.exists(filepath): overwrite = input( f"Overwrite existing result file {filepath} ? (y/n): ").lower( ) == "y" if overwrite: results = jasper.predict(test_data_loader, verbose=1) print(f"Saving result to {args.output} ...") with open(filepath, "w") as openfile:
def main( config: str = DEFAULT_YAML, tfrecords: bool = False, sentence_piece: bool = False, subwords: bool = False, bs: int = None, spx: int = 1, metadata: str = None, static_length: bool = False, devices: list = [0], mxp: bool = False, pretrained: str = None, ): tf.keras.backend.clear_session() tf.config.optimizer.set_experimental_options({"auto_mixed_precision": mxp}) strategy = env_util.setup_strategy(devices) config = Config(config) speech_featurizer, text_featurizer = featurizer_helpers.prepare_featurizers( config=config, subwords=subwords, sentence_piece=sentence_piece, ) train_dataset, eval_dataset = dataset_helpers.prepare_training_datasets( config=config, speech_featurizer=speech_featurizer, text_featurizer=text_featurizer, tfrecords=tfrecords, metadata=metadata, ) if not static_length: speech_featurizer.reset_length() text_featurizer.reset_length() train_data_loader, eval_data_loader, global_batch_size = dataset_helpers.prepare_training_data_loaders( config=config, train_dataset=train_dataset, eval_dataset=eval_dataset, strategy=strategy, batch_size=bs, ) with strategy.scope(): jasper = Jasper(**config.model_config, vocabulary_size=text_featurizer.num_classes) jasper.make(speech_featurizer.shape, batch_size=global_batch_size) if pretrained: jasper.load_weights(pretrained, by_name=True, skip_mismatch=True) jasper.summary(line_length=100) jasper.compile( optimizer=config.learning_config.optimizer_config, experimental_steps_per_execution=spx, global_batch_size=global_batch_size, blank=text_featurizer.blank, ) callbacks = [ tf.keras.callbacks.ModelCheckpoint(**config.learning_config.running_config.checkpoint), tf.keras.callbacks.experimental.BackupAndRestore(config.learning_config.running_config.states_dir), tf.keras.callbacks.TensorBoard(**config.learning_config.running_config.tensorboard), ] jasper.fit( train_data_loader, epochs=config.learning_config.running_config.num_epochs, validation_data=eval_data_loader, callbacks=callbacks, steps_per_epoch=train_dataset.total_steps, validation_steps=eval_dataset.total_steps if eval_data_loader else None, )