diff --git a/egs/librispeech/ASR/conv_emformer_transducer_stateless2/emformer.py b/egs/librispeech/ASR/conv_emformer_transducer_stateless2/emformer.py
index c5d862ad8..45ca03dd2 100644
--- a/egs/librispeech/ASR/conv_emformer_transducer_stateless2/emformer.py
+++ b/egs/librispeech/ASR/conv_emformer_transducer_stateless2/emformer.py
@@ -542,7 +542,7 @@ class EmformerAttention(nn.Module):
         padding_mask: Optional[torch.Tensor] = None,
         left_context_key: Optional[torch.Tensor] = None,
         left_context_val: Optional[torch.Tensor] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """Underlying chunk-wise attention implementation."""
         U, B, _ = utterance.size()
         R = right_context.size(0)
@@ -671,7 +671,7 @@ class EmformerAttention(nn.Module):
         left_context_key: torch.Tensor,
         left_context_val: torch.Tensor,
         padding_mask: Optional[torch.Tensor] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
         """Forward pass for inference.
 
         B: batch size;
@@ -1388,7 +1388,7 @@ class EmformerEncoder(nn.Module):
         output_lengths = torch.clamp(lengths - self.right_context_length, min=0)
         attention_mask = self._gen_attention_mask(utterance)
 
-        M = right_context.size(0) // self.chunk_length - 1
+        M = right_context.size(0) // self.right_context_length - 1
         padding_mask = make_pad_mask(M + right_context.size(0) + output_lengths)
 
         output = utterance