diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
index 75ea8a5d8..87b5fd4f2 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
@@ -1845,6 +1845,13 @@ class Conv2dSubsampling(nn.Module):
         self.scale_min = ScheduledFloat((0.0, 0.9), (4000.0, 0.1))
 
         self.out = nn.Linear(out_width * layer3_channels, out_channels)
+        # use a much larger than normal grad_scale on this whitening module;
+        # there is only one such module, so there is not a concern about adding
+        # together many copies of this extra gradient term.
+        self.out_whiten = Whiten(num_groups=1,
+                                 whitening_limit=_whitening_schedule(4.0),
+                                 prob=(0.025, 0.25),
+                                 grad_scale=0.05)
 
         self.out_norm = BasicNorm(out_channels)
         self.dropout = Dropout2(dropout)
@@ -1889,9 +1896,10 @@ class Conv2dSubsampling(nn.Module):
 
         # Now x is of shape (N, ((T-1)//2 - 1))//2, odim)
         x = self.out(x)
+        x = self.out_whiten(x)
         x = self.out_norm(x)
         x = self.dropout(x)
-        return x
+        return 4.0 * x
 
 class AttentionCombine(nn.Module):
     """