Update modeling_reguler.py

modeling_reguler.py

@@ -385,14 +385,25 @@ class JointCTCAttentionEncoderDecoder(SpeechEncoderDecoderModel):
 
     def _get_logits_processor(
         self,
-        generation_config: GenerationConfigWithCTC,
+        generation_config: GenerationConfig,
         input_ids_seq_length: int,
         encoder_input_ids: torch.LongTensor,
         prefix_allowed_tokens_fn: Callable[[int, torch.Tensor], List[int]],
         logits_processor: Optional[LogitsProcessorList],
+        model_kwargs: Optional[Dict[str, Any]] = None,
+        negative_prompt_ids: Optional[torch.Tensor] = None,
+        negative_prompt_attention_mask: Optional[torch.Tensor] = None,
     ) -> LogitsProcessorList:
+        # pylint: disable=no-member
         processors = super()._get_logits_processor(
-            generation_config, input_ids_seq_length, encoder_input_ids, prefix_allowed_tokens_fn, logits_processor
+            generation_config,
+            input_ids_seq_length,
+            encoder_input_ids,
+            prefix_allowed_tokens_fn,
+            logits_processor,
+            model_kwargs,
+            negative_prompt_ids,
+            negative_prompt_attention_mask,
         )
         if hasattr(generation_config, "ctc_weight") and generation_config.ctc_weight > 0:
             if generation_config.num_beams <= 1:
@@ -405,8 +416,17 @@ class JointCTCAttentionEncoderDecoder(SpeechEncoderDecoderModel):
                 self.generation_config.ctc_margin,
                 self.generation_config.ctc_weight,
                 self.generation_config.num_beams,
+                self.generation_config.space_token_id,
+                self.generation_config.apply_eos_space_trick,
+                self.generation_config.eos_space_trick_weight,
             )
             processors.append(self.ctc_rescorer)
+        if hasattr(generation_config, "lm_weight") and generation_config.lm_weight > 0:
+            if not hasattr(generation_config, "lm_model"):
+                raise ValueError("If `lm_weight` is specified, make sure that `lm_model` is defined.")
+            processors.append(
+                LMRescorerLogitsProcessor(generation_config.lm_weight, generation_config.lm_model, device=self.device)
+            )
         return processors
 
     def _prepare_encoder_decoder_kwargs_for_generation(