diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
index fc30e4f53..bd506310f 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
@@ -967,20 +967,16 @@ class RelPositionalEncoding(torch.nn.Module):
 
     Args:
         embed_dim: Embedding dimension.
-        max_offset: determines the largest offset that can be coded distinctly from smaller
-            offsets.  If you change this, the embedding will change.
         dropout_rate: Dropout rate.
         max_len: Maximum input length: just a heuristic for initialization.
     """
     def __init__(
         self, embed_dim: int,
             dropout_rate: float,
-            max_offset: float = 1000.0,
-            max_len: int = 10
+            max_len: int = 1000
     ) -> None:
         """Construct a PositionalEncoding object."""
         super(RelPositionalEncoding, self).__init__()
-        self.max_offset = max_offset
         self.embed_dim = embed_dim
         assert embed_dim % 2 == 0
         self.dropout = torch.nn.Dropout(dropout_rate)
@@ -1005,27 +1001,24 @@ class RelPositionalEncoding(torch.nn.Module):
         x = torch.arange(-(T-1), T,
                          device=x.device).to(torch.float32).unsqueeze(1)
 
-        # e.g. ratio might be 1.1
-        ratio = self.max_offset ** (2.0 / self.embed_dim)
-        # e.g. width_factor == 0.1, if ratio is 1.1.  determines steepness of sigmoid.
-        width_factor = ratio - 1.0
 
-        # centers of tanh functions (positive)
-        centers = (1.0 / (ratio - 1.0)) * (ratio ** torch.arange(self.embed_dim // 2,
-                                                                 device=x.device))
-        # adjust centers so the 1st value is 0.5, the 2nd value is about 1.5, and so on.
-        centers = centers + (0.5 - centers[0])
-        centers = centers.reshape(self.embed_dim // 2, 1).expand(self.embed_dim // 2, 2).contiguous()
-        centers[:, 1] *= -1.0
-        centers = centers.reshape(self.embed_dim)
+        length_factor =  self.embed_dim / (2.0*math.pi) # todo: test this.
 
-        widths = (centers.abs() * width_factor) + 1.0
+        freqs = 1 + torch.arange(self.embed_dim // 2, device=x.device)
+
+        x_tanh = (x / length_factor).tanh() # results between -pi and pi
+
+        cosines = (x_tanh * freqs).cos()
+        sines = (x_tanh * freqs).sin()
+
+        pe = torch.zeros(x.shape[0], self.embed_dim, device=x.device)
+        pe[:, 0::2] = cosines
+        pe[:, 1::2] = sines
+        pe[:, -1] = 1.0  # for bias.
+
+        #print("cosines = ", cosines[T-r:T+r,:5])
+        #print("sines = ", sines[T-r:T+r,:5])
 
-        # shape: (2*T - 1, embed_dim)
-        pe = ((x - centers) / widths).tanh()
-        # Let the last dimension of the embedding be a constant 1.0, to provide a bias term
-        # so we don't have to give a bias to the projection.
-        pe[:, -1] = 1.0
 
         self.pe = pe.to(dtype=x.dtype)