| | |
| | | ngram_weight: float = 0.9, |
| | | nbest: int = 1, |
| | | num_workers: int = 1, |
| | | param_dict: dict = None, |
| | | **kwargs, |
| | | ): |
| | | assert check_argument_types() |
| | |
| | | def _forward(data_path_and_name_and_type, |
| | | raw_inputs: Union[np.ndarray, torch.Tensor] = None, |
| | | output_dir_v2: Optional[str] = None, |
| | | fs: dict = None, |
| | | param_dict: dict = None, |
| | | ): |
| | | # 3. Build data-iterator |
| | | if data_path_and_name_and_type is None and raw_inputs is not None: |
| | |
| | | loader = ASRTask.build_streaming_iterator( |
| | | data_path_and_name_and_type, |
| | | dtype=dtype, |
| | | fs=fs, |
| | | batch_size=batch_size, |
| | | key_file=key_file, |
| | | num_workers=num_workers, |