| | |
|---|
| | | if asr_model.frontend is None and frontend_conf is not None: |
|---|
| | | frontend = WavFrontend(**frontend_conf) |
|---|
| | | asr_model.frontend = frontend |
|---|
| | | logging.info("asr_model: {}".format(asr_model)) |
|---|
| | | logging.info("asr_train_args: {}".format(asr_train_args)) |
|---|
| | | # logging.info("asr_model: {}".format(asr_model)) |
|---|
| | | # 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) |
|---|
| | |
|---|
| | | 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 |
|---|
| | |
|---|
| | | |
|---|
| | | # data: (Nsamples,) -> (1, Nsamples) |
|---|
| | | # lengths: (1,) |
|---|
| | | if len(speech.size()) < 3: |
|---|
| | | speech = speech.unsqueeze(0).to(getattr(torch, self.dtype)) |
|---|
| | | speech_lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1)) |
|---|
| | | # if len(speech.size()) < 3: |
|---|
| | | # speech = speech.unsqueeze(0).to(getattr(torch, self.dtype)) |
|---|
| | | # speech_lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1)) |
|---|
| | | lfr_factor = max(1, (speech.size()[-1]//80)-1) |
|---|
| | | |
|---|
| | | batch = {"speech": speech, "speech_lengths": speech_lengths} |
|---|
| | |
|---|
| | | assert len(keys) == _bs, f"{len(keys)} != {_bs}" |
|---|
| | | # batch = {k: v for k, v in batch.items() if not k.endswith("_lengths")} |
|---|
| | | |
|---|
| | | logging.info("decoding, utt_id: {}".format(keys)) |
|---|
| | | # logging.info("decoding, utt_id: {}".format(keys)) |
|---|
| | | # N-best list of (text, token, token_int, hyp_object) |
|---|
| | | |
|---|
| | | time_beg = time.time() |
|---|
