support BiCifParaformer timestamp output without vad

北念

2023-03-03 af3fb57fa9f3e635c8e3f16804a19ada6d83f5b2

support BiCifParaformer timestamp output without vad

 funasr/bin/asr_inference_paraformer.py

@@ -42,6 +42,7 @@
from funasr.models.frontend.wav_frontend import WavFrontend
from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer
from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export
from funasr.utils.timestamp_tools import time_stamp_lfr6_pl, time_stamp_sentence


class Speech2Text:
@@ -190,7 +191,8 @@

    @torch.no_grad()
    def __call__(
            self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None
            self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
            begin_time: int = 0, end_time: int = None,
    ):
        """Inference

@@ -242,6 +244,10 @@
            decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length, hw_list=self.hotword_list)
            decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]

        if isinstance(self.asr_model, BiCifParaformer):
            _, _, us_alphas, us_cif_peak = self.asr_model.calc_predictor_timestamp(enc, enc_len,
                                                                                   pre_token_length)  # test no bias cif2

        results = []
        b, n, d = decoder_out.size()
        for i in range(b):
@@ -284,7 +290,11 @@
                else:
                    text = None

                results.append((text, token, token_int, hyp, enc_len_batch_total, lfr_factor))
                if isinstance(self.asr_model, BiCifParaformer):
                    timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time)
                    results.append((text, token, token_int, hyp, timestamp, enc_len_batch_total, lfr_factor))
                else:
                    results.append((text, token, token_int, hyp, enc_len_batch_total, lfr_factor))

        # assert check_return_type(results)
        return results
@@ -683,6 +693,11 @@
            inference=True,
        )

        if param_dict is not None:
            use_timestamp = param_dict.get('use_timestamp', True)
        else:
            use_timestamp = True

        forward_time_total = 0.0
        length_total = 0.0
        finish_count = 0
@@ -724,7 +739,9 @@
                result = [results[batch_id][:-2]]

                key = keys[batch_id]
                for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), result):
                for n, result in zip(range(1, nbest + 1), result):
                    text, token, token_int, hyp = result[0], result[1], result[2], result[3]
                    time_stamp = None if len(result) < 5 else result[4]
                    # Create a directory: outdir/{n}best_recog
                    if writer is not None:
                        ibest_writer = writer[f"{n}best_recog"]
@@ -736,8 +753,20 @@
                        ibest_writer["rtf"][key] = rtf_cur

                    if text is not None:
                        text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
                        if use_timestamp and time_stamp is not None:
                            postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
                        else:
                            postprocessed_result = postprocess_utils.sentence_postprocess(token)
                        time_stamp_postprocessed = ""
                        if len(postprocessed_result) == 3:
                            text_postprocessed, time_stamp_postprocessed, word_lists = postprocessed_result[0], \
                                                                                       postprocessed_result[1], \
                                                                                       postprocessed_result[2]
                        else:
                            text_postprocessed, word_lists = postprocessed_result[0], postprocessed_result[1]
                        item = {'key': key, 'value': text_postprocessed}
                        if time_stamp_postprocessed != "":
                            item['time_stamp'] = time_stamp_postprocessed
                        asr_result_list.append(item)
                        finish_count += 1
                        # asr_utils.print_progress(finish_count / file_count)