| | |
| | | timestamp_infer_config: Union[Path, str] = None, |
| | | timestamp_model_file: Union[Path, str] = None, |
| | | param_dict: dict = None, |
| | | decoding_ind: int = 0, |
| | | **kwargs, |
| | | ): |
| | | ncpu = kwargs.get("ncpu", 1) |
| | |
| | | nbest=nbest, |
| | | hotword_list_or_file=hotword_list_or_file, |
| | | clas_scale=clas_scale, |
| | | decoding_ind=decoding_ind, |
| | | ) |
| | | |
| | | speech2text = Speech2TextParaformer(**speech2text_kwargs) |
| | |
| | | **kwargs, |
| | | ): |
| | | |
| | | decoding_ind = None |
| | | hotword_list_or_file = None |
| | | if param_dict is not None: |
| | | hotword_list_or_file = param_dict.get('hotword') |
| | |
| | | hotword_list_or_file = kwargs['hotword'] |
| | | if hotword_list_or_file is not None or 'hotword' in kwargs: |
| | | speech2text.hotword_list = speech2text.generate_hotwords_list(hotword_list_or_file) |
| | | if param_dict is not None and "decoding_ind" in param_dict: |
| | | decoding_ind = param_dict["decoding_ind"] |
| | | |
| | | # 3. Build data-iterator |
| | | if data_path_and_name_and_type is None and raw_inputs is not None: |
| | |
| | | # N-best list of (text, token, token_int, hyp_object) |
| | | |
| | | time_beg = time.time() |
| | | batch["decoding_ind"] = decoding_ind |
| | | results = speech2text(**batch) |
| | | if len(results) < 1: |
| | | hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[]) |
| | |
| | | ibest_writer["rtf"][key] = rtf_cur |
| | | |
| | | if text is not None: |
| | | if use_timestamp and timestamp is not None: |
| | | if use_timestamp and timestamp is not None and len(timestamp): |
| | | postprocessed_result = postprocess_utils.sentence_postprocess(token, timestamp) |
| | | else: |
| | | postprocessed_result = postprocess_utils.sentence_postprocess(token) |
| | |
| | | else: |
| | | text_postprocessed, word_lists = postprocessed_result[0], postprocessed_result[1] |
| | | item = {'key': key, 'value': text_postprocessed} |
| | | if timestamp_postprocessed != "": |
| | | if timestamp_postprocessed != "" or len(timestamp) == 0: |
| | | item['timestamp'] = timestamp_postprocessed |
| | | asr_result_list.append(item) |
| | | finish_count += 1 |
| | |
| | | text, token, token_int = result[0], result[1], result[2] |
| | | time_stamp = result[4] if len(result[4]) > 0 else None |
| | | |
| | | if use_timestamp and time_stamp is not None: |
| | | if use_timestamp and time_stamp is not None and len(time_stamp): |
| | | postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp) |
| | | else: |
| | | postprocessed_result = postprocess_utils.sentence_postprocess(token) |
| | |
| | | item = {'key': key, 'value': text_postprocessed_punc} |
| | | if text_postprocessed != "": |
| | | item['text_postprocessed'] = text_postprocessed |
| | | if time_stamp_postprocessed != "": |
| | | if time_stamp_postprocessed != "" or len(time_stamp) == 0: |
| | | item['time_stamp'] = time_stamp_postprocessed |
| | | |
| | | item['sentences'] = time_stamp_sentence(punc_id_list, time_stamp_postprocessed, text_postprocessed) |
| | |
| | | group.add_argument("--ngram_weight", type=float, default=0.9, help="ngram weight") |
| | | group.add_argument("--streaming", type=str2bool, default=False) |
| | | group.add_argument("--simu_streaming", type=str2bool, default=False) |
| | | group.add_argument("--full_utt", type=str2bool, default=False) |
| | | group.add_argument("--chunk_size", type=int, default=16) |
| | | group.add_argument("--left_context", type=int, default=16) |
| | | group.add_argument("--right_context", type=int, default=0) |