| | |
|---|
| | | frontend = None |
|---|
| | | if asr_train_args.frontend is not None and asr_train_args.frontend_conf is not None: |
|---|
| | | if asr_train_args.frontend=='wav_frontend': |
|---|
| | | frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf).eval() |
|---|
| | | frontend = WavFrontend(cmvn_file=cmvn_file, **asr_train_args.frontend_conf) |
|---|
| | | else: |
|---|
| | | frontend_class=frontend_choices.get_class(asr_train_args.frontend) |
|---|
| | | frontend = frontend_class(**asr_train_args.frontend_conf).eval() |
|---|
| | |
|---|
| | | token_list=token_list, |
|---|
| | | pre_beam_score_key=None if ctc_weight == 1.0 else "full", |
|---|
| | | ) |
|---|
| | | |
|---|
| | | beam_search.to(device=device, dtype=getattr(torch, dtype)).eval() |
|---|
| | | for scorer in scorers.values(): |
|---|
| | | if isinstance(scorer, torch.nn.Module): |
|---|
| | | scorer.to(device=device, dtype=getattr(torch, dtype)).eval() |
|---|
| | | logging.info(f"Beam_search: {beam_search}") |
|---|
| | | logging.info(f"Decoding device={device}, dtype={dtype}") |
|---|
| | | |
|---|
| | | # 5. [Optional] Build Text converter: e.g. bpe-sym -> Text |
|---|
| | | if token_type is None: |
|---|
