| | |
|---|
| | | else: |
|---|
| | | decoder = asr_model.decoder2 |
|---|
| | | |
|---|
| | | ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos) |
|---|
| | | if asr_model.ctc != None: |
|---|
| | | ctc = CTCPrefixScorer(ctc=asr_model.ctc, eos=asr_model.eos) |
|---|
| | | scorers.update( |
|---|
| | | ctc=ctc |
|---|
| | | ) |
|---|
| | | token_list = asr_model.token_list |
|---|
| | | scorers.update( |
|---|
| | | decoder=decoder, |
|---|
| | | ctc=ctc, |
|---|
| | | length_bonus=LengthBonus(len(token_list)), |
|---|
| | | ) |
|---|
| | | |
|---|
| | |
|---|
| | | token_num_relax: int = 1, |
|---|
| | | decoding_ind: int = 0, |
|---|
| | | decoding_mode: str = "model1", |
|---|
| | | 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, |
|---|
| | | param_dict: dict = None, |
|---|
| | | ): |
|---|
| | | # 3. Build data-iterator |
|---|
| | | if data_path_and_name_and_type is None and raw_inputs is not None: |
|---|
