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")
args = parser.parse_args()
tf.config.optimizer.set_experimental_options(
{"auto_mixed_precision": args.mxp})
strategy = setup_strategy(args.devices)
from tensorflow_asr.configs.config import Config
from tensorflow_asr.datasets.asr_dataset import ASRTFRecordDataset, ASRSliceDataset
from tensorflow_asr.featurizers.speech_featurizers import TFSpeechFeaturizer
from tensorflow_asr.featurizers.text_featurizers import CharFeaturizer
from tensorflow_asr.runners.ctc_runners import CTCTrainer
from tensorflow_asr.models.deepspeech2 import DeepSpeech2
config = Config(args.config)
speech_featurizer = TFSpeechFeaturizer(config.speech_config)
text_featurizer = CharFeaturizer(config.decoder_config)
if args.tfrecords:
train_dataset = ASRTFRecordDataset(
speech_featurizer=speech_featurizer,
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='*',
default=["en_ng_male_train.tsv", "en_ng_female_train.tsv"])
parser.add_argument("--train-reg-dir",
'-trd',
nargs='*',
default=[
"libritts_train-clean-100.tsv",
"libritts_train-clean-360.tsv",
"libritts_train-other-500.tsv"
])
parser.add_argument(
"--dev-dir",
'-dd',
nargs='*',
default=["en_ng_male_eval.tsv", "en_ng_female_eval.tsv"])
parser.add_argument("--dev-reg-dir",
'-drd',
nargs='*',
default=["libritts_test-other.tsv"])
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)
config.train_dir = args.train_dir
config.dev_dir = args.dev_dir
config.train_reg_dir = args.train_reg_dir
config.dev_reg_dir = args.dev_reg_dir
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=config.train_dir,
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
augmentations=config.learning_config.augmentations,
stage="train",
cache=False,
shuffle=False)
train_reg_dataset = DatasetInf(
data_paths=config.train_reg_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=config.dev_dir,
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
stage="eval",
cache=False,
shuffle=False)
eval_reg_dataset = DatasetInf(
data_paths=config.dev_reg_dir,
speech_featurizer=speech_featurizer,
text_featurizer=text_featurizer,
augmentations=config.learning_config.augmentations,
stage="eval",
cache=False,
shuffle=False)
conformer_trainer = MultiReaderTransducerTrainer(
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,
train_reg_dataset,
# alpha for regularising dataset; alpha = 1 for training dataset
1.,
eval_dataset,
eval_reg_dataset,
train_bs=args.tbs,
eval_bs=args.ebs,
train_acs=args.acs)