diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
index 1c181b1ac..0d4f44d14 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
@@ -1463,12 +1463,19 @@ class ModifiedSEModule(nn.Module):
             max_factor=0.01,
             min_prob=0.2,
         )
+        #self.bottleneck_norm = BasicNorm(bottleneck_dim)
+
         self.from_bottleneck_proj =  ScaledLinear(bottleneck_dim, d_model)
-        self.sigmoid = nn.Sigmoid()  # make it a submodule for diagnostics purposes.
 
         self.out_proj = ScaledLinear(d_model, d_model,
                                      bias=False, initial_scale=0.1)
 
+        self.out_whiten = Whiten(num_groups=1,
+                                 whitening_limit=10.0,
+                                 prob=(0.025, 0.25),
+                                 grad_scale=0.01)
+
+
 
 
     def forward(self,
@@ -1497,16 +1504,16 @@ class ModifiedSEModule(nn.Module):
         squeezed = self.activation(squeezed)
         squeezed = self.to_bottleneck_proj(squeezed)
         squeezed = self.bottleneck_balancer(squeezed)
+        #squeezed = self.bottleneck_norm(squeezed)
         squeezed = self.from_bottleneck_proj(squeezed)
         if random.random() < 0.05:
             # to stop a hopefully-unlikely failure mode where the inputs to the sigmoid
             # get too large and the grads get mostly too small.
             squeezed = penalize_abs_values_gt(squeezed, limit=10.0, penalty=1.0e-04)
-        scales = self.sigmoid(squeezed)
 
         x = self.in_proj(x)
         x = x * squeezed
-        return self.out_proj(x)
+        return self.out_whiten(self.out_proj(x))
 
 
 class FeedforwardModule(nn.Module):