diff --git a/egs/librispeech/ASR/transducer_stateless2/joiner.py b/egs/librispeech/ASR/transducer_stateless2/joiner.py
index b0ba7fd83..e56ba859d 100644
--- a/egs/librispeech/ASR/transducer_stateless2/joiner.py
+++ b/egs/librispeech/ASR/transducer_stateless2/joiner.py
@@ -30,8 +30,6 @@ class Joiner(nn.Module):
         self,
         encoder_out: torch.Tensor,
         decoder_out: torch.Tensor,
-        encoder_out_len: torch.Tensor,
-        decoder_out_len: torch.Tensor,
     ) -> torch.Tensor:
         """
         Args:
@@ -39,40 +37,17 @@ class Joiner(nn.Module):
             Output from the encoder. Its shape is (N, T, self.input_dim).
           decoder_out:
             Output from the decoder. Its shape is (N, U, self.input_dim).
-          encoder_out_len:
-            A 1-D tensor of shape (N,) containing valid number of frames
-            before padding in `encoder_out`.
-          decoder_out_len:
-            A 1-D tensor of shape (N,) containing valid number of frames
-            before padding in `decoder_out`.
         Returns:
-          Return a tensor of shape (sum_all_TU, self.output_dim).
+          Return a tensor of shape (N, T, U, self.output_dim).
         """
         assert encoder_out.ndim == decoder_out.ndim == 3
         assert encoder_out.size(0) == decoder_out.size(0)
         assert encoder_out.size(2) == self.input_dim
         assert decoder_out.size(2) == self.input_dim
 
-        N = encoder_out.size(0)
-
-        encoder_out_len = encoder_out_len.tolist()
-        decoder_out_len = decoder_out_len.tolist()
-
-        encoder_out_list = [
-            encoder_out[i, : encoder_out_len[i], :] for i in range(N)
-        ]
-
-        decoder_out_list = [
-            decoder_out[i, : decoder_out_len[i], :] for i in range(N)
-        ]
-
-        x = [
-            e.unsqueeze(1) + d.unsqueeze(0)
-            for e, d in zip(encoder_out_list, decoder_out_list)
-        ]
-
-        x = [p.reshape(-1, self.input_dim) for p in x]
-        x = torch.cat(x)
+        encoder_out = encoder_out.unsqueeze(2)  # (N, T, 1, C)
+        decoder_out = decoder_out.unsqueeze(1)  # (N, 1, U, C)
+        x = encoder_out + decoder_out  # (N, T, U, C)
 
         activations = torch.tanh(x)