formatting

src/f5_tts/model/backbones/dit.py

@@ -66,18 +66,18 @@ class TextEmbedding(nn.Module):
 
         valid_ind = torch.where(valid_mask)[0]
         valid_data = text[0, valid_ind, :]  # [valid_len, text_dim]
-        
+
         base_repeat = audio_len // valid_len
         remainder = audio_len % valid_len
-        
+
         indices = []
         for j in range(valid_len):
             repeat_count = base_repeat + (1 if j >= valid_len - remainder else 0)
             indices.extend([j] * repeat_count)
-        
+
         indices = torch.tensor(indices[:audio_len], device=text.device, dtype=torch.long)
         upsampled = valid_data[indices]  # [audio_len, text_dim]
-        
+
         upsampled_text[0, :audio_len, :] = upsampled
 
         return upsampled_text
@@ -245,7 +245,7 @@ class DiT(nn.Module):
                 text_embed = self.text_embed(text, x.shape[1], drop_text=drop_text)
             else:
                 batch = x.shape[0]
-                seq_lens = audio_mask.sum(dim=1) # Calculate the actual sequence length for each sample
+                seq_lens = audio_mask.sum(dim=1)  # Calculate the actual sequence length for each sample
                 text_embed_list = []
                 for i in range(batch):
                     text_embed_i = self.text_embed(
@@ -325,4 +325,4 @@ class DiT(nn.Module):
         x = self.norm_out(x, t)
         output = self.proj_out(x)
 
-        return output
+        return output