speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
augmentations=config.learning_config.augmentations,
stage="train",
cache=args.cache,
shuffle=True)
eval_dataset = ASRSliceDataset(
data_paths=config.learning_config.dataset_config.eval_paths,
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
stage="eval",
cache=args.cache,
shuffle=True)
conformer_trainer = TransducerTrainerGA(
config=config.learning_config.running_config,
text_featurizer=text_featurizer,
strategy=strategy)
with conformer_trainer.strategy.scope():
# build model
conformer = Conformer(**config.model_config,
vocabulary_size=text_featurizer.num_classes)
conformer._build(speech_featurizer.shape)
conformer.summary(line_length=120)
optimizer_config = config.learning_config.optimizer_config
optimizer = tf.keras.optimizers.Adam(TransformerSchedule(
d_model=config.model_config["dmodel"],
warmup_steps=optimizer_config["warmup_steps"],
max_lr=(0.05 / math.sqrt(config.model_config["dmodel"]))),
beta_1=optimizer_config["beta1"],
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
augmentations=config["learning_config"]["augmentations"],
stage="train",
cache=args.cache,
shuffle=True)
eval_dataset = ASRSliceDataset(
data_paths=config["learning_config"]["dataset_config"]["eval_paths"],
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
stage="eval",
cache=args.cache,
shuffle=True)
streaming_transducer_trainer = TransducerTrainerGA(
config=config["learning_config"]["running_config"],
text_featurizer=text_featurizer,
strategy=strategy)
with streaming_transducer_trainer.strategy.scope():
# build model
streaming_transducer = StreamingTransducer(
**config["model_config"], vocabulary_size=text_featurizer.num_classes)
streaming_transducer._build(speech_featurizer.shape)
streaming_transducer.summary(line_length=150)
optimizer = tf.keras.optimizers.Adam()
streaming_transducer_trainer.compile(model=streaming_transducer,
optimizer=optimizer,
max_to_keep=args.max_ckpts)