websocket 2pass bugfix

游雁

2023-05-19 219c2482ab755fbd4e49dfbdee91bf1a8a4ec49a

 funasr/bin/asr_infer.py

@@ -488,15 +488,20 @@

                nbest_hyps = nbest_hyps[: self.nbest]
            else:
                yseq = am_scores.argmax(dim=-1)
                score = am_scores.max(dim=-1)[0]
                score = torch.sum(score, dim=-1)
                # pad with mask tokens to ensure compatibility with sos/eos tokens
                yseq = torch.tensor(
                    [self.asr_model.sos] + yseq.tolist() + [self.asr_model.eos], device=yseq.device
                )
                if pre_token_length[i] == 0:
                    yseq = torch.tensor(
                        [self.asr_model.sos] + [self.asr_model.eos], device=yseq.device
                    )
                    score = torch.tensor(0.0, device=yseq.device)
                else:
                    yseq = am_scores.argmax(dim=-1)
                    score = am_scores.max(dim=-1)[0]
                    score = torch.sum(score, dim=-1)
                    # pad with mask tokens to ensure compatibility with sos/eos tokens
                    yseq = torch.tensor(
                        [self.asr_model.sos] + yseq.tolist() + [self.asr_model.eos], device=yseq.device
                    )
                nbest_hyps = [Hypothesis(yseq=yseq, score=score)]

            for hyp in nbest_hyps:
                assert isinstance(hyp, (Hypothesis)), type(hyp)

@@ -749,10 +754,13 @@
            feats = cache_en["feats"]
            feats_len = torch.tensor([feats.shape[1]])
            self.asr_model.frontend = None
            self.frontend.cache_reset()
            results = self.infer(feats, feats_len, cache)
            return results
        else:
            if self.frontend is not None:
                if cache_en["start_idx"] == 0:
                    self.frontend.cache_reset()
                feats, feats_len = self.frontend.forward(speech, speech_lengths, cache_en["is_final"])
                feats = to_device(feats, device=self.device)
                feats_len = feats_len.int()
@@ -1581,7 +1589,7 @@
            d = ModelDownloader()
            kwargs.update(**d.download_and_unpack(model_tag))
        
        return Speech2Text(**kwargs)
        return Speech2TextTransducer(**kwargs)


class Speech2TextSAASR: