| | |
|---|
| | | logging.info("asr_train_args: {}".format(asr_train_args)) |
|---|
| | | asr_model.to(dtype=getattr(torch, dtype)).eval() |
|---|
| | | |
|---|
| | | 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( |
|---|
| | | ctc=ctc, |
|---|
| | | length_bonus=LengthBonus(len(token_list)), |
|---|
| | | ) |
|---|
| | | |
|---|
| | |
|---|
| | | self.converter = converter |
|---|
| | | self.tokenizer = tokenizer |
|---|
| | | is_use_lm = lm_weight != 0.0 and lm_file is not None |
|---|
| | | if ctc_weight == 0.0 and not is_use_lm: |
|---|
| | | if (ctc_weight == 0.0 or asr_model.ctc == None) and not is_use_lm: |
|---|
| | | beam_search = None |
|---|
| | | self.beam_search = beam_search |
|---|
| | | logging.info(f"Beam_search: {self.beam_search}") |
|---|
| | |
|---|
| | | nbest: int = 1, |
|---|
| | | num_workers: int = 1, |
|---|
| | | output_dir: Optional[str] = None, |
|---|
| | | param_dict: dict = None, |
|---|
| | | **kwargs, |
|---|
| | | ): |
|---|
| | | assert check_argument_types() |
|---|
| | |
|---|
| | | 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: |
|---|
