update timestamp pipelien

shixian.shi

2023-03-08 31cda51dfa667bf58c06e35610a2f8b0fcffbd44

update timestamp pipelien

 funasr/bin/tp_inference.py

@@ -87,10 +87,14 @@
    else:
        timestamp_list[-1][1] = num_frames*TIME_RATE
    assert len(new_char_list) == len(timestamp_list)
    res = ""
    res_str = ""
    for char, timestamp in zip(new_char_list, timestamp_list):
        res += "{} {} {};".format(char, timestamp[0], timestamp[1])
    return res
        res_str += "{} {} {};".format(char, str(timestamp[0]+0.0005)[:5], str(timestamp[1]+0.0005)[:5])
    res = []
    for char, timestamp in zip(char_list, timestamp_list):
        if char != '<sil>':
            res.append([int(timestamp[0] * 1000), int(timestamp[1] * 1000)])
    return res_str, res


class SpeechText2Timestamp:
@@ -256,7 +260,8 @@
            raw_inputs: Union[np.ndarray, torch.Tensor] = None,
            output_dir_v2: Optional[str] = None,
            fs: dict = None,
            param_dict: dict = None
            param_dict: dict = None,
            **kwargs
    ):
        # 3. Build data-iterator
        if data_path_and_name_and_type is None and raw_inputs is not None:
@@ -295,11 +300,9 @@
            for batch_id in range(_bs):
                key = keys[batch_id]
                token = speechtext2timestamp.converter.ids2tokens(batch['text'][batch_id])
                timestamp = time_stamp_lfr6_advance(us_alphas[batch_id], us_cif_peak[batch_id], token)
                logging.warning(timestamp)
                import pdb; pdb.set_trace()
                tp_result_list.append({'text':"".join([i for i in token if i != '<sil>']), 'timestamp': timestamp})

                ts_str, ts_list = time_stamp_lfr6_advance(us_alphas[batch_id], us_cif_peak[batch_id], token)
                logging.warning(ts_str)
                tp_result_list.append({'text':"".join([i for i in token if i != '<sil>']), 'timestamp': ts_list})
        return tp_result_list

    return _forward