diff --git a/egs/librispeech/ASR/incremental_transf/.conformer.py.swp b/egs/librispeech/ASR/incremental_transf/.conformer.py.swp
index d8c62d0cc..c70f2b921 100644
Binary files a/egs/librispeech/ASR/incremental_transf/.conformer.py.swp and b/egs/librispeech/ASR/incremental_transf/.conformer.py.swp differ
diff --git a/egs/librispeech/ASR/incremental_transf/conformer.py b/egs/librispeech/ASR/incremental_transf/conformer.py
index 04f3b78ed..ca1137f31 100644
--- a/egs/librispeech/ASR/incremental_transf/conformer.py
+++ b/egs/librispeech/ASR/incremental_transf/conformer.py
@@ -510,67 +510,11 @@ class Tempformer(EncoderInterface):
     def forward(
         self, x
     ):
-        """
-        Args:
-          x:
-            The input tensor. Its shape is (batch_size, seq_len, feature_dim).
-          x_lens:
-            A tensor of shape (batch_size,) containing the number of frames in
-            `x` before padding.
-        Returns:
-          Return a tuple containing 2 tensors:
-            - embeddings: its shape is (batch_size, output_seq_len, d_model)
-            - lengths, a tensor of shape (batch_size,) containing the number
-              of frames in `embeddings` before padding.
-        """
-        x = self.encoder_embed(x)
-        x, pos_emb = self.encoder_pos(x)
-        x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
-
-        lengths = (((x_lens - 1) >> 1) - 1) >> 1
-        assert x.size(0) == lengths.max().item()
-        src_key_padding_mask = make_pad_mask(lengths)
-
-        if self.dynamic_chunk_training:
-            assert (
-                self.causal
-            ), "Causal convolution is required for streaming conformer."
-            max_len = x.size(0)
-            chunk_size = torch.randint(1, max_len, (1,)).item()
-            if chunk_size > (max_len * self.short_chunk_threshold):
-                chunk_size = max_len
-            else:
-                chunk_size = chunk_size % self.short_chunk_size + 1
-
-            mask = ~subsequent_chunk_mask(
-                size=x.size(0),
-                chunk_size=chunk_size,
-                num_left_chunks=self.num_left_chunks,
-                device=x.device,
-            )
-            x = self.encoder(
-                x,
-                pos_emb,
-                mask=mask,
-                src_key_padding_mask=src_key_padding_mask,
-                warmup=warmup,
-            )  # (T, N, C)
-        else:
-            x, layer_outputs = self.encoder(
-                x,
-                pos_emb,
-                src_key_padding_mask=src_key_padding_mask,
-                warmup=warmup,
-            )  # (T, N, C)
-
-        x = x.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
-        layer_outputs = [x.permute(1, 0, 2) for x in layer_outputs]
-
-        if get_layer_output:
-            return x, lengths, layer_outputs
-        else:
-            return x, lengths
+        layer_outputs = []
+        for enum, encoder in enumerate(self.encoder_layers):
+            layer_outputs.append(encoder(x[enum]))
 
+        return layer_outputs
 
 
 class ConformerEncoderLayer(nn.Module):