| | |
|---|
| | | if param_dict is not None: |
|---|
| | | hotword_list_or_file = param_dict.get('hotword') |
|---|
| | | export_mode = param_dict.get("export_mode", False) |
|---|
| | | clas_scale = param_dict.get('clas_scale', 1.0) |
|---|
| | | else: |
|---|
| | | hotword_list_or_file = None |
|---|
| | | clas_scale = param_dict.get('clas_scale', 1.0) |
|---|
| | | clas_scale = 1.0 |
|---|
| | | |
|---|
| | | if kwargs.get("device", None) == "cpu": |
|---|
| | | ngpu = 0 |
|---|
| | |
|---|
| | | left_context=left_context, |
|---|
| | | right_context=right_context, |
|---|
| | | ) |
|---|
| | | speech2text = Speech2TextTransducer.from_pretrained( |
|---|
| | | model_tag=model_tag, |
|---|
| | | **speech2text_kwargs, |
|---|
| | | ) |
|---|
| | | speech2text = Speech2TextTransducer(**speech2text_kwargs) |
|---|
| | | |
|---|
| | | def _forward(data_path_and_name_and_type, |
|---|
| | | raw_inputs: Union[np.ndarray, torch.Tensor] = None, |
|---|
