diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
index a99ae4f18..9e2d29ab1 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
@@ -1613,6 +1613,49 @@ class ConvolutionModule(nn.Module):
         x = x.permute(2, 0, 1)  # (time, batch, channel)
         return x
 
+
+class SqueezeExcite1d(nn.Module):
+    def __init__(self,
+                 channels: int,
+                 bottleneck_channels: int):
+        super().__init__()
+        self.to_bottleneck_proj = LinearWithAuxLoss(channels,
+                                                    bottleneck_channels)
+
+        self.bottleneck_activation = TanSwish()
+        self.from_bottleneck_proj = nn.Linear(bottleneck_channels,
+                                              channels)
+
+        self.balancer = ActivationBalancer(
+            channels, channel_dim=-1,
+            min_abs=0.05,
+            max_abs=ScheduledFloat((0.0, 0.2),
+                                   (4000.0, 2.0),
+                                   (10000.0, 10.0),
+                                   default=1.0),
+            max_factor=0.02,
+            min_prob=0.1,
+        )
+        self.activation = nn.Sigmoid()
+
+
+
+    def forward(self, x: Tensor):
+        """
+        x: a Tensor of shape (batch_size, T, channels).
+        Returns: something with the same shape as x.
+        """
+        # project before mean, needed for LinearWithAuxLoss (or, at least, better)
+        bottleneck = self.to_bottleneck_proj(x)
+        # would replace this mean with cumsum for a causal model.
+        bottleneck = bottleneck.mean(dim=1, keepdim=True)
+        bottleneck = self.bottleneck_activation(bottleneck)
+        scale = self.from_bottleneck_proj(bottleneck)
+        scale = self.balancer(scale)
+        scale = self.activation(scale)
+        return x * scale
+
+
 class Conv2dSubsampling(nn.Module):
     """Convolutional 2D subsampling (to 1/2 length).
 
@@ -1631,6 +1674,7 @@ class Conv2dSubsampling(nn.Module):
         layer1_channels: int = 8,
         layer2_channels: int = 32,
         layer3_channels: int = 128,
+        bottleneck_channels: int = 64,
         dropout: float = 0.1,
     ) -> None:
         """
@@ -1644,6 +1688,8 @@ class Conv2dSubsampling(nn.Module):
             Number of channels in layer1
           layer1_channels:
             Number of channels in layer2
+          bottleneck:
+            bottleneck dimension for 1d squeeze-excite
         """
         assert in_channels >= 7
         super().__init__()
@@ -1679,6 +1725,10 @@ class Conv2dSubsampling(nn.Module):
             DoubleSwish(),
         )
         out_height = (((in_channels - 1) // 2) - 1) // 2
+
+        self.squeeze_excite = SqueezeExcite1d(out_height * layer3_channels,
+                                              bottleneck_channels)
+
         self.out = ScaledLinear(out_height * layer3_channels, out_channels)
         self.dropout = nn.Dropout(dropout)
 
@@ -1698,7 +1748,11 @@ class Conv2dSubsampling(nn.Module):
         x = self.conv(x)
         # Now x is of shape (N, odim, ((T-3)//2 - 1)//2, ((idim-1)//2 - 1)//2)
         b, c, t, f = x.size()
-        x = self.out(x.transpose(1, 2).reshape(b, t, c * f))
+
+        x = x.transpose(1, 2).reshape(b, t, c * f)
+        # now x: (N, ((T-1)//2 - 1))//2, out_height * layer3_channels))
+        x = self.squeeze_excite(x)
+        x = self.out(x)
         # Now x is of shape (N, ((T-1)//2 - 1))//2, odim)
         x = self.dropout(x)
         return x