def main():
parser = argparse.ArgumentParser(prog="Conformer Training")
parser.add_argument("--config",
type=str,
default=DEFAULT_YAML,
help="The file path of model configuration file")
parser.add_argument("--max_ckpts",
type=int,
default=10,
help="Max number of checkpoints to keep")
parser.add_argument("--tbs",
type=int,
default=None,
help="Train batch size per replica")
parser.add_argument("--ebs",
type=int,
default=None,
help="Evaluation batch size per replica")
parser.add_argument("--acs",
type=int,
default=None,
help="Train accumulation steps")
parser.add_argument("--devices",
type=int,
nargs="*",
default=[0],
help="Devices' ids to apply distributed training")
parser.add_argument("--mxp",
default=False,
action="store_true",
help="Enable mixed precision")
parser.add_argument("--subwords",
type=str,
default=None,
help="Path to file that stores generated subwords")
parser.add_argument("--subwords_corpus",
nargs="*",
type=str,
default=[],
help="Transcript files for generating subwords")
parser.add_argument("--train-dir", '-td', nargs='*', required=True)
parser.add_argument("--dev-dir", '-dd', nargs='*', required=True)
args = parser.parse_args()
tf.config.optimizer.set_experimental_options(
{"auto_mixed_precision": args.mxp})
strategy = setup_strategy(args.devices)
config = Config(args.config, learning=True)
with open(config.speech_config) as f:
speech_config = yaml.load(f, Loader=yaml.Loader)
speech_featurizer = TFSpeechFeaturizer(speech_config)
if args.subwords and os.path.exists(args.subwords):
print("Loading subwords ...")
text_featurizer = SubwordFeaturizer.load_from_file(
config.decoder_config, args.subwords)
else:
print("Generating subwords ...")
text_featurizer = SubwordFeaturizer.build_from_corpus(
config.decoder_config, corpus_files=args.subwords_corpus)
text_featurizer.save_to_file(args.subwords)
train_dataset = Dataset(data_paths=args.train_dir,
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
augmentations=config.learning_config.augmentations,
stage="train",
cache=False,
shuffle=False)
eval_dataset = Dataset(data_paths=args.dev_dir,
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
stage="eval",
cache=False,
shuffle=False)
conformer_trainer = TransducerTrainer(
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 = tf.keras.optimizers.Adam(
TransformerSchedule(d_model=conformer.dmodel,
warmup_steps=config.learning_config.
optimizer_config["warmup_steps"],
max_lr=(0.05 / math.sqrt(conformer.dmodel))),
beta_1=config.learning_config.optimizer_config["beta1"],
beta_2=config.learning_config.optimizer_config["beta2"],
epsilon=config.learning_config.optimizer_config["epsilon"])
conformer_trainer.compile(model=conformer,
optimizer=optimizer,
max_to_keep=args.max_ckpts)
conformer_trainer.fit(train_dataset,
eval_dataset,
train_bs=args.tbs,
eval_bs=args.ebs,
train_acs=args.acs)
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 = TrainerWithMaskingGA(
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 = tf.keras.optimizers.Adam(
TransformerSchedule(
d_model=config.model_config["encoder_dmodel"],
warmup_steps=config.learning_config.optimizer_config["warmup_steps"],
max_lr=(0.05 / math.sqrt(config.model_config["encoder_dmodel"]))
),
beta_1=config.learning_config.optimizer_config["beta1"],
beta_2=config.learning_config.optimizer_config["beta2"],
epsilon=config.learning_config.optimizer_config["epsilon"]
)
conformer_trainer.compile(model=conformer, optimizer=optimizer,
max_to_keep=args.max_ckpts)
def test_conformer():
config = Config(DEFAULT_YAML, learning=False)
text_featurizer = CharFeaturizer(config.decoder_config)
speech_featurizer = TFSpeechFeaturizer(config.speech_config)
model = Conformer(vocabulary_size=text_featurizer.num_classes,
**config.model_config)
model._build(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(
timestamp=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
]
tflite = converter.convert()
print("Converted successfully with no timestamp")
concrete_func = model.make_tflite_function(
timestamp=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 timestamp")
tflitemodel = tf.lite.Interpreter(model_content=tflite)
signal = tf.random.normal([4000])
input_details = tflitemodel.get_input_details()
output_details = tflitemodel.get_output_details()
tflitemodel.resize_tensor_input(input_details[0]["index"], [4000])
tflitemodel.allocate_tensors()
tflitemodel.set_tensor(input_details[0]["index"], signal)
tflitemodel.set_tensor(input_details[1]["index"],
tf.constant(text_featurizer.blank, dtype=tf.int32))
tflitemodel.set_tensor(
input_details[2]["index"],
tf.zeros([
config.model_config["prediction_num_rnns"], 2, 1,
config.model_config["prediction_rnn_units"]
],
dtype=tf.float32))
tflitemodel.invoke()
hyp = tflitemodel.get_tensor(output_details[0]["index"])
print(hyp)
