Rename 'res' in line 514 to avoid with naming conflict with line 365

liugz18

2024-07-18 d80ac2fd2df4e7fb8a28acfa512bb11472b5cc99

 funasr/models/transformer/search.py

@@ -13,6 +13,7 @@
from funasr.models.transformer.scorers.scorer_interface import PartialScorerInterface
from funasr.models.transformer.scorers.scorer_interface import ScorerInterface


class Hypothesis(NamedTuple):
    """Hypothesis data type."""

@@ -28,7 +29,6 @@
            score=float(self.score),
            scores={k: float(v) for k, v in self.scores.items()},
        )._asdict()



class BeamSearch(torch.nn.Module):
@@ -276,9 +276,7 @@
            new_states[k] = d.select_state(part_states[k], part_idx)
        return new_states

    def search(
        self, running_hyps: List[Hypothesis], x: torch.Tensor
    ) -> List[Hypothesis]:
    def search(self, running_hyps: List[Hypothesis], x: torch.Tensor) -> List[Hypothesis]:
        """Search new tokens for running hypotheses and encoded speech x.

        Args:
@@ -318,9 +316,7 @@
                    Hypothesis(
                        score=weighted_scores[j],
                        yseq=self.append_token(hyp.yseq, j),
                        scores=self.merge_scores(
                            hyp.scores, scores, j, part_scores, part_j
                        ),
                        scores=self.merge_scores(hyp.scores, scores, j, part_scores, part_j),
                        states=self.merge_states(states, part_states, part_j),
                    )
                )
@@ -383,8 +379,7 @@
        # check the number of hypotheses reaching to eos
        if len(nbest_hyps) == 0:
            logging.warning(
                "there is no N-best results, perform recognition "
                "again with smaller minlenratio."
                "there is no N-best results, perform recognition " "again with smaller minlenratio."
            )
            return (
                []
@@ -395,17 +390,13 @@
        # report the best result
        best = nbest_hyps[0]
        for k, v in best.scores.items():
            logging.info(
                f"{v:6.2f} * {self.weights[k]:3} = {v * self.weights[k]:6.2f} for {k}"
            )
            logging.info(f"{v:6.2f} * {self.weights[k]:3} = {v * self.weights[k]:6.2f} for {k}")
        logging.info(f"total log probability: {best.score:.2f}")
        logging.info(f"normalized log probability: {best.score / len(best.yseq):.2f}")
        logging.info(f"total number of ended hypotheses: {len(nbest_hyps)}")
        if self.token_list is not None:
            logging.info(
                "best hypo: "
                + "".join([self.token_list[x] for x in best.yseq[1:-1]])
                + "\n"
                "best hypo: " + "".join([self.token_list[x] for x in best.yseq[1:-1]]) + "\n"
            )
        return nbest_hyps

@@ -433,15 +424,13 @@
        logging.debug(f"the number of running hypotheses: {len(running_hyps)}")
        if self.token_list is not None:
            logging.debug(
                "best hypo: "
                + "".join([self.token_list[x] for x in running_hyps[0].yseq[1:]])
                "best hypo: " + "".join([self.token_list[x] for x in running_hyps[0].yseq[1:]])
            )
        # add eos in the final loop to avoid that there are no ended hyps
        if i == maxlen - 1:
            logging.info("adding <eos> in the last position in the loop")
            running_hyps = [
                h._replace(yseq=self.append_token(h.yseq, self.eos))
                for h in running_hyps
                h._replace(yseq=self.append_token(h.yseq, self.eos)) for h in running_hyps
            ]

        # add ended hypotheses to a final list, and removed them from current hypotheses