diff --git a/encodec/modules/conv.py b/encodec/modules/conv.py
index e83ae84..cc15459 100644
--- a/encodec/modules/conv.py
+++ b/encodec/modules/conv.py
@@ -28,10 +28,9 @@ def apply_parametrization_norm(module: nn.Module, norm: str = 'none') -> nn.Modu
         return weight_norm(module)
     elif norm == 'spectral_norm':
         return spectral_norm(module)
-    else:
-        # We already check was in CONV_NORMALIZATION, so any other choice
-        # doesn't need reparametrization.
-        return module
+    # We already check was in CONV_NORMALIZATION, so any other choice
+    # doesn't need reparametrization.
+    return module
 
 
 def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none', **norm_kwargs) -> nn.Module:
@@ -47,8 +46,8 @@ def get_norm_module(module: nn.Module, causal: bool = False, norm: str = 'none',
             raise ValueError("GroupNorm doesn't support causal evaluation.")
         assert isinstance(module, nn.modules.conv._ConvNd)
         return nn.GroupNorm(1, module.out_channels, **norm_kwargs)
-    else:
-        return nn.Identity()
+
+    return nn.Identity()
 
 
 def get_extra_padding_for_conv1d(x: torch.Tensor, kernel_size: int, stride: int,
@@ -83,18 +82,21 @@ def pad1d(x: torch.Tensor, paddings: tp.Tuple[int, int], mode: str = 'zero', val
     length = x.shape[-1]
     padding_left, padding_right = paddings
     assert padding_left >= 0 and padding_right >= 0, (padding_left, padding_right)
-    if mode == 'reflect':
-        max_pad = max(padding_left, padding_right)
-        extra_pad = 0
-        if length <= max_pad:
-            extra_pad = max_pad - length + 1
-            x = F.pad(x, (0, extra_pad))
-        padded = F.pad(x, paddings, mode, value)
-        end = padded.shape[-1] - extra_pad
-        return padded[..., :end]
-    else:
+
+    # Constant padding
+    if mode != 'reflect':
         return F.pad(x, paddings, mode, value)
 
+    # Reflect padding
+    max_pad = max(padding_left, padding_right)
+    extra_pad = 0
+    if length <= max_pad:
+        extra_pad = max_pad - length + 1
+        x = F.pad(x, (0, extra_pad))
+    padded = F.pad(x, paddings, mode, value)
+    end = padded.shape[-1] - extra_pad
+    return padded[..., :end]
+
 
 def unpad1d(x: torch.Tensor, paddings: tp.Tuple[int, int]):
     """Remove padding from x, handling properly zero padding. Only for 1d!"""
@@ -193,7 +195,6 @@ def __init__(self, in_channels: int, out_channels: int,
         self.pad_mode = pad_mode
 
     def forward(self, x):
-        B, C, T = x.shape
         kernel_size = self.conv.conv.kernel_size[0]
         stride = self.conv.conv.stride[0]
         dilation = self.conv.conv.dilation[0]
@@ -244,10 +245,10 @@ def forward(self, x):
             # if trim_right_ratio = 1.0, trim everything from right
             padding_right = math.ceil(padding_total * self.trim_right_ratio)
             padding_left = padding_total - padding_right
-            y = unpad1d(y, (padding_left, padding_right))
         else:
             # Asymmetric padding required for odd strides
             padding_right = padding_total // 2
             padding_left = padding_total - padding_right
-            y = unpad1d(y, (padding_left, padding_right))
+
+        y = unpad1d(y, (padding_left, padding_right))
         return y