funasr/bin/diar_infer.py
@@ -235,8 +235,11 @@ new_seq.append(x) else: idx_list = np.where(seq < 2 ** vec_dim)[0] idx = np.abs(idx_list - i).argmin() new_seq.append(seq[idx_list[idx]]) if len(idx_list) > 0: idx = np.abs(idx_list - i).argmin() new_seq.append(seq[idx_list[idx]]) else: new_seq.append(0) return np.row_stack([int2vec(x, vec_dim) for x in new_seq]) def post_processing(self, raw_logits: torch.Tensor, spk_num: int, output_format: str = "speaker_turn"):
