nargs="*",
type=str,
default=[],
help="Transcript files for generating subwords")
parser.add_argument("--saved",
type=str,
default=None,
help="Path to saved model")
args = parser.parse_args()
tf.config.optimizer.set_experimental_options(
{"auto_mixed_precision": args.mxp})
strategy = setup_tpu(args.tpu_address)
from tensorflow_asr.configs.config import Config
from tensorflow_asr.datasets.keras import ASRTFRecordDatasetKeras
from tensorflow_asr.featurizers.speech_featurizers import TFSpeechFeaturizer
from tensorflow_asr.featurizers.text_featurizers import SubwordFeaturizer, SentencePieceFeaturizer
from tensorflow_asr.models.keras.contextnet import ContextNet
from tensorflow_asr.optimizers.schedules import TransformerSchedule
config = Config(args.config)
speech_featurizer = TFSpeechFeaturizer(config.speech_config)
if args.sentence_piece:
print("Loading SentencePiece model ...")
text_featurizer = SentencePieceFeaturizer.load_from_file(
config.decoder_config, args.subwords)
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_tpu(None)
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.transducer_runners import TransducerTrainer
from tensorflow_asr.models.conformer import Conformer
from tensorflow_asr.optimizers.schedules import TransformerSchedule
config = Config(args.config)
speech_featurizer = TFSpeechFeaturizer(config.speech_config)
text_featurizer = CharFeaturizer(config.decoder_config)
if args.tfrecords:
train_dataset = ASRTFRecordDataset(