diff --git a/egs/librispeech/ASR/lstm_transducer_stateless3/lstm.py b/egs/librispeech/ASR/lstm_transducer_stateless3/lstm.py
index cb67fffe4..59a835d35 100644
--- a/egs/librispeech/ASR/lstm_transducer_stateless3/lstm.py
+++ b/egs/librispeech/ASR/lstm_transducer_stateless3/lstm.py
@@ -121,6 +121,8 @@ class RNN(EncoderInterface):
         Period of auxiliary layers used for random combiner during training.
         If set to 0, will not use the random combiner (Default).
         You can set a positive integer to use the random combiner, e.g., 3.
+      is_pnnx:
+        True to make this class exportable via PNNX.
     """
 
     def __init__(
@@ -149,7 +151,13 @@ class RNN(EncoderInterface):
         # That is, it does two things simultaneously:
         #   (1) subsampling: T -> T//subsampling_factor
         #   (2) embedding: num_features -> d_model
-        self.encoder_embed = Conv2dSubsampling(num_features, d_model)
+        self.encoder_embed = Conv2dSubsampling(
+            num_features,
+            d_model,
+            is_pnnx=is_pnnx,
+        )
+
+        self.is_pnnx = is_pnnx
 
         self.num_encoder_layers = num_encoder_layers
         self.d_model = d_model
@@ -177,8 +185,6 @@ class RNN(EncoderInterface):
             else None,
         )
 
-        self.is_pnnx = is_pnnx
-
     def forward(
         self,
         x: torch.Tensor,
@@ -226,7 +232,6 @@ class RNN(EncoderInterface):
             lengths = torch.floor((lengths1 - 1) / 2)
             lengths = lengths.to(x_lens)
 
-
         if not torch.jit.is_tracing():
             assert x.size(0) == lengths.max().item()
 
@@ -387,7 +392,7 @@ class RNNEncoderLayer(nn.Module):
                 # for cell state
                 assert states[1].shape == (1, src.size(1), self.rnn_hidden_size)
             src_lstm, new_states = self.lstm(src, states)
-        src = src + self.dropout(src_lstm)
+        src = self.dropout(src_lstm) + src
 
         # feed forward module
         src = src + self.dropout(self.feed_forward(src))
@@ -533,6 +538,7 @@ class Conv2dSubsampling(nn.Module):
         layer1_channels: int = 8,
         layer2_channels: int = 32,
         layer3_channels: int = 128,
+        is_pnnx: bool = False,
     ) -> None:
         """
         Args:
@@ -545,6 +551,9 @@ class Conv2dSubsampling(nn.Module):
             Number of channels in layer1
           layer1_channels:
             Number of channels in layer2
+          is_pnnx:
+            True if we are converting the model to PNNX format.
+            False otherwise.
         """
         assert in_channels >= 9
         super().__init__()
@@ -587,6 +596,10 @@ class Conv2dSubsampling(nn.Module):
             channel_dim=-1, min_positive=0.45, max_positive=0.55
         )
 
+        # ncnn supports only batch size == 1
+        self.is_pnnx = is_pnnx
+        self.conv_out_dim = self.out.weight.shape[1]
+
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """Subsample x.
 
@@ -600,9 +613,15 @@ class Conv2dSubsampling(nn.Module):
         # On entry, x is (N, T, idim)
         x = x.unsqueeze(1)  # (N, T, idim) -> (N, 1, T, idim) i.e., (N, C, H, W)
         x = self.conv(x)
-        # Now x is of shape (N, odim, ((T-3)//2-1)//2, ((idim-3)//2-1)//2)
-        b, c, t, f = x.size()
-        x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
+
+        if torch.jit.is_tracing() and self.is_pnnx:
+            x = x.permute(0, 2, 1, 3).reshape(1, -1, self.conv_out_dim)
+            x = self.out(x)
+        else:
+            # Now x is of shape (N, odim, ((T-3)//2-1)//2, ((idim-3)//2-1)//2)
+            b, c, t, f = x.size()
+            x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
+
         # Now x is of shape (N, ((T-3)//2-1))//2, odim)
         x = self.out_norm(x)
         x = self.out_balancer(x)