def build_lm(args, save_path=None, wordlm=False, lm_dict_path=None, asr_dict_path=None):
"""Select LM class.
Args:
args ():
save_path (str):
wordlm (bool):
lm_dict_path (dict):
asr_dict_path (dict):
Returns:
lm ():
"""
if 'gated_conv' in args.lm_type:
from neural_sp.models.lm.gated_convlm import GatedConvLM
lm = GatedConvLM(args, save_path)
elif args.lm_type == 'transformer':
from neural_sp.models.lm.transformerlm import TransformerLM
lm = TransformerLM(args, save_path)
elif args.lm_type == 'transformer_xl':
from neural_sp.models.lm.transformer_xl import TransformerXL
lm = TransformerXL(args, save_path)
else:
from neural_sp.models.lm.rnnlm import RNNLM
lm = RNNLM(args, save_path)
return lm
def build_lm(args, save_path=None, wordlm=False, lm_dict_path=None, asr_dict_path=None):
"""Select LM class.
Args:
args ():
save_path (str):
wordlm (bool):
lm_dict_path (dict):
asr_dict_path (dict):
Returns:
lm ():
"""
if 'gated_conv' in args.lm_type:
lm = GatedConvLM(args, save_path)
elif args.lm_type == 'transformer':
lm = TransformerLM(args, save_path)
else:
lm = RNNLM(args, save_path)
# Word-level RNNLM
# if wordlm:
# lm = LookAheadWordLM(lm, lm_dict_path, asr_dict_path)
return lm
def select_lm(args, save_path=None):
if args.lm_type == 'gated_cnn':
lm = GatedConvLM(args, save_path)
elif args.lm_type == 'transformer':
lm = TransformerLM(args, save_path)
else:
lm = RNNLM(args, save_path)
return lm
def build_lm(args, save_path=None):
"""Select LM class.
Args:
args ():
save_path (str):
wordlm (bool):
lm_dict_path (dict):
asr_dict_path (dict):
Returns:
lm ():
"""
if 'gated_conv' in args.lm_type:
lm = GatedConvLM(args, save_path)
elif args.lm_type == 'transformer':
lm = TransformerLM(args, save_path)
else:
lm = RNNLM(args, save_path)
return lm