diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
index e502c991e..eff4f65c2 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
@@ -896,32 +896,27 @@ class CompactRelPositionalEncoding(torch.nn.Module):
     Args:
         embed_dim: Embedding dimension.
         dropout_rate: Dropout rate.
-        random_shift: standard deviation of random distance by which we shift each time, if
-            training.
         max_len: Maximum input length: just a heuristic for initialization.
     """
     def __init__(
         self, embed_dim: int,
-            dropout_rate: FloatLike = 0.0,
-            random_shift: FloatLike = 1.0,
+            dropout_rate: float,
             max_len: int = 1000
     ) -> None:
         """Construct a CompactRelPositionalEncoding object."""
         super(CompactRelPositionalEncoding, self).__init__()
         self.embed_dim = embed_dim
         assert embed_dim % 2 == 0
-        self.dropout_rate = dropout_rate
-        self.random_shift = random_shift
+        self.dropout = torch.nn.Dropout(dropout_rate)
         self.pe = None
-        self.extend_pe(torch.tensor(0.0).expand(max_len), 0)
+        self.extend_pe(torch.tensor(0.0).expand(max_len))
 
-    def extend_pe(self, x: Tensor, shift: int) -> None:
+    def extend_pe(self, x: Tensor) -> None:
         """Reset the positional encodings."""
-        T = x.size(0) + abs(shift)
         if self.pe is not None:
             # self.pe contains both positive and negative parts
             # the length of self.pe is 2 * input_len - 1
-            if self.pe.size(0) >= T * 2 - 1:
+            if self.pe.size(0) >= x.size(0) * 2 - 1:
                 # Note: TorchScript doesn't implement operator== for torch.Device
                 if self.pe.dtype != x.dtype or str(self.pe.device) != str(
                     x.device
@@ -929,6 +924,7 @@ class CompactRelPositionalEncoding(torch.nn.Module):
                     self.pe = self.pe.to(dtype=x.dtype, device=x.device)
                 return
 
+        T = x.size(0)
         # if T == 4, x would contain [ -3, -2, 1, 0, 1, 2, 3 ]
         x = torch.arange(-(T-1), T,
                          device=x.device).to(torch.float32).unsqueeze(1)
@@ -983,22 +979,16 @@ class CompactRelPositionalEncoding(torch.nn.Module):
             torch.Tensor: Encoded tensor (batch, 2*time-1, `*`).
 
         """
-
-        shift = int(round(random.normalvariate(0, 1) * float(self.random_shift))) if self.training else 0
-        self.extend_pe(x, shift)
-
+        self.extend_pe(x)
         pos_emb = self.pe[
             self.pe.size(0) // 2
-            - x.size(0) + shift
+            - x.size(0)
             + 1 : self.pe.size(0) // 2  # noqa E203
-            + x.size(0) + shift,
+            + x.size(0),
             :
         ]
         pos_emb = pos_emb.unsqueeze(0)
-        pos_emb = torch.nn.functional.dropout(pos_emb,
-                                              p=float(self.dropout_rate),
-                                              training=self.training)
-        return pos_emb
+        return self.dropout(pos_emb)