diff --git a/egs/librispeech/ASR/transducer_stateless/conformer.py b/egs/librispeech/ASR/transducer_stateless/conformer.py
index 5cf869119..7d3c9869d 100644
--- a/egs/librispeech/ASR/transducer_stateless/conformer.py
+++ b/egs/librispeech/ASR/transducer_stateless/conformer.py
@@ -169,8 +169,10 @@ class Conformer(Transformer):
                 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
+                size=x.size(0),
+                chunk_size=chunk_size,
+                num_left_chunks=self.num_left_chunks,
+                device=x.device,
             )
 
         x, _ = self.encoder(
@@ -185,7 +187,6 @@ class Conformer(Transformer):
 
         return logits, lengths
 
-
     def streaming_forward(
         self,
         x: torch.Tensor,
@@ -243,9 +244,16 @@ class Conformer(Transformer):
             ), "Require cache when sending data in streaming mode"
 
             assert (
-                len(states) == 2 and
-                states[0].shape == (self.encoder_layers, left_context, x.size(0), self.d_model) and
-                states[1].shape == (self.encoder_layers, self.cnn_module_kernel - 1, x.size(0), self.d_model)
+                len(states) == 2
+                and states[0].shape
+                == (self.encoder_layers, left_context, x.size(0), self.d_model)
+                and states[1].shape
+                == (
+                    self.encoder_layers,
+                    self.cnn_module_kernel - 1,
+                    x.size(0),
+                    self.d_model,
+                )
             ), f"""The length of states MUST be equal to 2, and the shape of
              first element should be {(self.encoder_layers, left_context, x.size(0), self.d_model)},
              given {states[0].shape}. the shape of second element should be
@@ -285,7 +293,7 @@ class Conformer(Transformer):
                 size=x.size(0),
                 chunk_size=chunk_size,
                 num_left_chunks=num_left_chunks,
-                device=x.device
+                device=x.device,
             )
             x = self.encoder(
                 x,
@@ -544,7 +552,11 @@ class ConformerEncoder(nn.Module):
             assert left_context >= 0
 
         for layer_index, mod in enumerate(self.layers):
-            cache = None if states is None else [states[0][layer_index], states[1][layer_index]]
+            cache = (
+                None
+                if states is None
+                else [states[0][layer_index], states[1][layer_index]]
+            )
             output = mod(
                 output,
                 pos_emb,
@@ -621,10 +633,8 @@ class RelPositionalEncoding(torch.nn.Module):
         self.pe = pe.to(device=x.device, dtype=x.dtype)
 
     def forward(
-            self,
-            x: torch.Tensor,
-            context: int = 0
-        ) -> Tuple[Tensor, Tensor]:
+        self, x: torch.Tensor, context: int = 0
+    ) -> Tuple[Tensor, Tensor]:
         """Add positional encoding.
 
         Args:
@@ -1073,16 +1083,23 @@ class RelPositionMultiheadAttention(nn.Module):
         # the whole column of `attn_output_weights` will be `-inf`
         # (i.e. be `nan` after softmax), so, we fill `0.0` at the masking
         # positions to avoid invalid loss value below.
-        if attn_mask is not None and attn_mask.dtype == torch.bool and \
-                key_padding_mask is not None:
-            combined_mask = attn_mask.unsqueeze(
-                0) | key_padding_mask.unsqueeze(1).unsqueeze(2)
+        if (
+            attn_mask is not None
+            and attn_mask.dtype == torch.bool
+            and key_padding_mask is not None
+        ):
+            combined_mask = attn_mask.unsqueeze(0) | key_padding_mask.unsqueeze(
+                1
+            ).unsqueeze(2)
             attn_output_weights = attn_output_weights.view(
-                bsz, num_heads, tgt_len, src_len)
+                bsz, num_heads, tgt_len, src_len
+            )
             attn_output_weights = attn_output_weights.masked_fill(
-                combined_mask, 0.0)
+                combined_mask, 0.0
+            )
             attn_output_weights = attn_output_weights.view(
-                bsz * num_heads, tgt_len, src_len)
+                bsz * num_heads, tgt_len, src_len
+            )
 
         attn_output_weights = nn.functional.dropout(
             attn_output_weights, p=dropout_p, training=training
@@ -1125,7 +1142,7 @@ class ConvolutionModule(nn.Module):
         channels: int,
         kernel_size: int,
         bias: bool = True,
-        causal: bool = False
+        causal: bool = False,
     ) -> None:
         """Construct an ConvolutionModule object."""
         super(ConvolutionModule, self).__init__()
@@ -1168,10 +1185,8 @@ class ConvolutionModule(nn.Module):
         self.activation = Swish()
 
     def forward(
-            self,
-            x: Tensor,
-            cache: Optional[Tensor] = None
-        ) -> Union[Tensor, Tuple[Tensor, Tensor]]:
+        self, x: Tensor, cache: Optional[Tensor] = None
+    ) -> Union[Tensor, Tuple[Tensor, Tensor]]:
         """Compute convolution module.
 
         Args:
@@ -1195,10 +1210,12 @@ class ConvolutionModule(nn.Module):
                 # manualy padding self.lorder zeros to the left
                 x = nn.functional.pad(x, (self.lorder, 0), "constant", 0.0)
             else:
-                assert not self.training, "Cache should be None in training time"
+                assert (
+                    not self.training
+                ), "Cache should be None in training time"
                 assert cache.size(0) == self.lorder
                 x = torch.cat([cache.permute(1, 2, 0), x], dim=2)
-                cache = x.permute(2, 0, 1)[-self.lorder:,...]
+                cache = x.permute(2, 0, 1)[-self.lorder :, ...]  # noqa
 
         x = self.depthwise_conv(x)
         # x is (batch, channels, time)
@@ -1210,7 +1227,9 @@ class ConvolutionModule(nn.Module):
 
         x = self.pointwise_conv2(x)  # (batch, channel, time)
 
-        return x.permute(2, 0, 1) if cache is None else (x.permute(2, 0, 1), cache)
+        return (
+            x.permute(2, 0, 1) if cache is None else (x.permute(2, 0, 1), cache)
+        )
 
 
 class Swish(torch.nn.Module):