diff --git a/egs/librispeech/ASR/conformer_ctc/conformer.py b/egs/librispeech/ASR/conformer_ctc/conformer.py
index 50a46ae28..2d070d937 100644
--- a/egs/librispeech/ASR/conformer_ctc/conformer.py
+++ b/egs/librispeech/ASR/conformer_ctc/conformer.py
@@ -207,7 +207,7 @@ class ConformerEncoderLayer(nn.Module):
         # kernel size=21, self.conv1d_channels=768
         # kernel size=5, self.conv1d_channels=1024
         self.linear1 = nn.Linear(512, self.in_conv1d_channels)
-        self.conv1d_abs = Conv1dAbs(self.in_conv1d_channels, self.out_conv1d_channels, kernel_size=self.kernel_size, padding=self.padding, padding_mode="replicate")
+        self.conv1d_abs = Conv1dAbs(self.in_conv1d_channels, self.out_conv1d_channels, kernel_size=self.kernel_size, padding=self.padding, padding_mode="constant")
         self.linear2 = nn.Linear(self.out_conv1d_channels, 512)
 
     def forward(
@@ -333,7 +333,6 @@ class ConformerEncoder(nn.TransformerEncoder):
         src_key_padding_mask: Optional[Tensor] = None,
     ) -> Tensor:
         r"""Pass the input through the encoder layers in turn.
-
         Args:
             src: the sequence to the encoder (required).
             pos_emb: Positional embedding tensor (required).
diff --git a/egs/librispeech/ASR/conformer_ctc/conv1d_abs_attention.py b/egs/librispeech/ASR/conformer_ctc/conv1d_abs_attention.py
index 84ff0e9fb..e3a35779c 100644
--- a/egs/librispeech/ASR/conformer_ctc/conv1d_abs_attention.py
+++ b/egs/librispeech/ASR/conformer_ctc/conv1d_abs_attention.py
@@ -153,10 +153,6 @@ class _ConvNd(Module):
         if not hasattr(self, "padding_mode"):
             self.padding_mode = "zeros"
 
-import torch
-import torch.nn as nn
-m = nn.Tanh()
-
 def padding(input, padding_length):
     # input shape : (B, T, D)
     device = input.device
@@ -201,12 +197,13 @@ class Conv1dAbs(_ConvNd):
     def forward(self, input: Tensor) -> Tensor:
         if self.padding_mode != "zeros":
             return F.conv1d(
-                # F.pad(
-                #    input,
-                #    self._reversed_padding_repeated_twice,
-                #    mode=self.padding_mode,
-                # ),
-                padding(input, self.padding),
+                F.pad(
+                   input,
+                   (0, 0, self.padding, self.padding),
+                   self.padding_mode,
+                   1,
+                ).permute(0, 2, 1),
+                #padding(input, self.padding),
                 torch.exp(self.weight),
                 torch.exp(self.bias),
                 self.stride,