| | |
| | | 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}") |
| | | # 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: |
| | |
| | | else: |
| | | tokenizer = build_tokenizer(token_type=token_type) |
| | | converter = TokenIDConverter(token_list=token_list) |
| | | logging.info(f"Text tokenizer: {tokenizer}") |
| | | # logging.info(f"Text tokenizer: {tokenizer}") |
| | | |
| | | self.asr_model = asr_model |
| | | self.asr_train_args = asr_train_args |