prediction_shape=text_featurizer.prepand_shape,
batch_size=global_batch_size)
if args.pretrained:
contextnet.load_weights(args.pretrained,
by_name=True,
skip_mismatch=True)
contextnet.summary(line_length=100)
optimizer = tf.keras.optimizers.Adam(
TransformerSchedule(
d_model=contextnet.dmodel,
warmup_steps=config.learning_config.optimizer_config.pop(
"warmup_steps", 10000),
max_lr=(0.05 / math.sqrt(contextnet.dmodel))),
**config.learning_config.optimizer_config)
contextnet.compile(optimizer=optimizer,
experimental_steps_per_execution=args.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)
]
contextnet.fit(
train_data_loader,
epochs=config.learning_config.running_config.num_epochs,
validation_data=eval_data_loader,
def main(
config: str = DEFAULT_YAML,
tfrecords: bool = False,
sentence_piece: bool = False,
subwords: bool = True,
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():
contextnet = ContextNet(**config.model_config, vocabulary_size=text_featurizer.num_classes)
contextnet.make(speech_featurizer.shape, prediction_shape=text_featurizer.prepand_shape, batch_size=global_batch_size)
if pretrained:
contextnet.load_weights(pretrained, by_name=True, skip_mismatch=True)
contextnet.summary(line_length=100)
optimizer = tf.keras.optimizers.Adam(
TransformerSchedule(
d_model=contextnet.dmodel,
warmup_steps=config.learning_config.optimizer_config.pop("warmup_steps", 10000),
max_lr=(0.05 / math.sqrt(contextnet.dmodel)),
),
**config.learning_config.optimizer_config
)
contextnet.compile(
optimizer=optimizer,
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),
]
contextnet.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,
)