update raw_text related funcs

shixian.shi

2024-02-21 509bc889037a42204ffff9a1c014560c69f676ad

 funasr/auto/auto_model.py

@@ -379,12 +379,14 @@
                            result[k] = restored_data[j][k]
                        else:
                            result[k] += restored_data[j][k]
                            
            
            return_raw_text = kwargs.get('return_raw_text', False)            
            # step.3 compute punc model
            if self.punc_model is not None:
                self.punc_kwargs.update(cfg)
                punc_res = self.inference(result["text"], model=self.punc_model, kwargs=self.punc_kwargs, disable_pbar=True, **cfg)
                raw_text = copy.copy(result["text"])
                if return_raw_text: result['raw_text'] = raw_text
                result["text"] = punc_res[0]["text"]
            else:
                raw_text = None
@@ -403,26 +405,28 @@
                    for res, vadsegment in zip(restored_data, vadsegments):
                        if 'timestamp' not in res:
                            logging.error("Only 'iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch' \
                                and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\
                                can predict timestamp, and speaker diarization relies on timestamps.")
                        sentence_list.append({"start": vadsegment[0],\
                                                "end": vadsegment[1],
                                                "sentence": res['text'],
                                                "timestamp": res['timestamp']})
                                           and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\
                                           can predict timestamp, and speaker diarization relies on timestamps.")
                        sentence_list.append({"start": vadsegment[0],
                                              "end": vadsegment[1],
                                              "sentence": res['text'],
                                              "timestamp": res['timestamp']})
                elif self.spk_mode == 'punc_segment':
                    if 'timestamp' not in result:
                        logging.error("Only 'iic/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch' \
                            and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\
                            can predict timestamp, and speaker diarization relies on timestamps.")
                    sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
                                                        result['timestamp'], \
                                                        raw_text)
                                       and 'iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'\
                                       can predict timestamp, and speaker diarization relies on timestamps.")
                    sentence_list = timestamp_sentence(punc_res[0]['punc_array'],
                                                       result['timestamp'],
                                                       raw_text,
                                                       return_raw_text=return_raw_text)
                distribute_spk(sentence_list, sv_output)
                result['sentence_info'] = sentence_list
            elif kwargs.get("sentence_timestamp", False):
                sentence_list = timestamp_sentence(punc_res[0]['punc_array'], \
                                                        result['timestamp'], \
                                                        raw_text)
                sentence_list = timestamp_sentence(punc_res[0]['punc_array'],
                                                   result['timestamp'],
                                                   raw_text,
                                                   return_raw_text=return_raw_text)
                result['sentence_info'] = sentence_list
            if "spk_embedding" in result: del result['spk_embedding']