| | |
|---|
| | | event_bg_token: List[int] = field(default_factory=lambda: [58946, 58948, 58950, 58952]), |
|---|
| | | event_ed_token: List[int] = field(default_factory=lambda: [58947, 58949, 58951, 58953]), |
|---|
| | | event_score_ga: List[float] = field(default_factory=lambda: [1, 1, 5, 25]), |
|---|
| | | |
|---|
| | | token_list: List[str] = None, |
|---|
| | | pre_beam_ratio: float = 1.5, |
|---|
| | | pre_beam_score_key: str = None, |
|---|
| | |
|---|
| | | |
|---|
| | | last_token = yseq[-1] |
|---|
| | | if last_token in self.emo_tokens + [self.emo_unk]: |
|---|
| | | # prevent output event after emotation token |
|---|
| | | # prevent output event after emotation token |
|---|
| | | score[self.event_bg_token] = -np.inf |
|---|
| | | |
|---|
| | | for eve_bg, eve_ed, eve_ga in zip(self.event_bg_token, self.event_ed_token, self.event_score_ga): |
|---|
| | | for eve_bg, eve_ed, eve_ga in zip( |
|---|
| | | self.event_bg_token, self.event_ed_token, self.event_score_ga |
|---|
| | | ): |
|---|
| | | score_offset = get_score(yseq, eve_bg, eve_ed) |
|---|
| | | score[eve_bg] += score_offset[0] |
|---|
| | | score[eve_ed] += score_offset[1] |
|---|
| | | score[eve_bg] += math.log(eve_ga) |
|---|
| | | |
|---|
| | | |
|---|
| | | score[self.emo_unk] += math.log(self.emo_unk_score) |
|---|
| | | for emo, emo_th in zip(self.emo_tokens, self.emo_scores): |
|---|
| | |
|---|
| | | scores[k] = struct_score(hyp.yseq, scores[k]) |
|---|
| | | |
|---|
| | | return scores, states |
|---|
| | | |
|---|
| | | |
|---|
| | | def score_partial( |
|---|
| | | self, hyp: Hypothesis, ids: torch.Tensor, x: torch.Tensor |
|---|
