diff --git a/wenet/transformer/embedding.py b/wenet/transformer/embedding.py
index 2efa2f5fd..db8a41333 100644
--- a/wenet/transformer/embedding.py
+++ b/wenet/transformer/embedding.py
@@ -208,9 +208,9 @@ def __init__(self,
                  rope_theta=10000.0):
         super().__init__(d_model, dropout_rate=dropout_rate, max_len=max_len)
         delattr(self, 'pe')
-
-        pe = precompute_freqs_cis(head_dim, max_len * 2, rope_theta)
-        self.register_buffer("pe", pe.unsqueeze(0))
+        self.max_len = max_len * 2
+        pe = precompute_freqs_cis(head_dim, self.max_len, rope_theta)
+        self.register_buffer("pe", torch.view_as_real(pe.unsqueeze(0)))
         self.dropout_rate = dropout_rate
 
     def forward(
@@ -219,13 +219,34 @@ def forward(
         offset: Union[int,
                       torch.Tensor] = 0) -> Tuple[torch.Tensor, torch.Tensor]:
 
-        pos_emb = self.position_encoding(offset, x.size(1), False)
+        pos_emb = self.position_encoding(offset, x.size(1), True)
         pos_emb = pos_emb.unsqueeze(1)  # [1, 1, seq, head_dim//2]
         # NOTE(Mddct): some model don't scale
         # TODO(Mddct): fix
         x = x * self.xscale
-        # NOTE(Mddct) dropout don't suuport complex float for pos_emb
-        return self.dropout(x), self.dropout_complex(pos_emb)
+        return self.dropout(x), pos_emb
+
+    def position_encoding(self,
+                          offset: Union[int, torch.Tensor],
+                          size: int,
+                          apply_dropout: bool = True) -> torch.Tensor:
+
+        pe = torch.view_as_complex(self.pe)
+        if isinstance(offset, int):
+            assert offset + size <= self.max_len
+            pos_emb = pe[:, offset:offset + size]
+        else:
+            assert torch.max(offset) + size <= self.max_len
+            index = offset.unsqueeze(1) + torch.arange(0, size).to(
+                offset.device)  # B X T
+            flag = index > 0
+            # remove negative offset
+            index = index * flag
+            pos_emb = F.embedding(index, pe[0])  # B X T X head_dim//2
+        if apply_dropout:
+            # NOTE(Mddct) dropout don't suuport complex float for pos_emb
+            pos_emb = self.dropout_complex(pos_emb)
+        return pos_emb
 
     def dropout_complex(self, x):
         mask = torch.nn.functional.dropout(