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update param list

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This commit is contained in:
jmwang66 2023-02-03 11:03:42 +08:00
parent 65b35ca423
commit 67b9af91d2
4 changed files with 8 additions and 0 deletions
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2
funasr/bin/asr_inference.py
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@ -483,6 +483,7 @@ def inference_modelscope(
ngram_weight: float = 0.9,
nbest: int = 1,
num_workers: int = 1,
param_dict: dict = None,
**kwargs,
):
assert check_argument_types()
@ -533,6 +534,7 @@ def inference_modelscope(
def _forward(data_path_and_name_and_type,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
output_dir_v2: Optional[str] = None,
param_dict: dict = None,
):
# 3. Build data-iterator
if data_path_and_name_and_type is None and raw_inputs is not None:

2
funasr/bin/asr_inference_paraformer.py
View File

@ -527,6 +527,7 @@ def inference_modelscope(
nbest: int = 1,
num_workers: int = 1,
output_dir: Optional[str] = None,
param_dict: dict = None,
**kwargs,
):
assert check_argument_types()
@ -576,6 +577,7 @@ def inference_modelscope(
data_path_and_name_and_type,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
output_dir_v2: Optional[str] = None,
param_dict: dict = None,
):
# 3. Build data-iterator
if data_path_and_name_and_type is None and raw_inputs is not None:

2
funasr/bin/asr_inference_paraformer_vad_punc.py
View File

@ -479,6 +479,7 @@ def inference_modelscope(
punc_infer_config: Optional[str] = None,
punc_model_file: Optional[str] = None,
outputs_dict: Optional[bool] = True,
param_dict: dict = None,
**kwargs,
):
assert check_argument_types()
@ -544,6 +545,7 @@ def inference_modelscope(
def _forward(data_path_and_name_and_type,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
output_dir_v2: Optional[str] = None,
param_dict: dict = None,
):
# 3. Build data-iterator
if data_path_and_name_and_type is None and raw_inputs is not None:

2
funasr/bin/asr_inference_uniasr.py
View File

@ -521,6 +521,7 @@ def inference_modelscope(
token_num_relax: int = 1,
decoding_ind: int = 0,
decoding_mode: str = "model1",
param_dict: dict = None,
**kwargs,
):
assert check_argument_types()
@ -574,6 +575,7 @@ def inference_modelscope(
def _forward(data_path_and_name_and_type,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
output_dir_v2: Optional[str] = None,
param_dict: dict = None,
):
# 3. Build data-iterator
if data_path_and_name_and_type is None and raw_inputs is not None: