Merge pull request #1212 from QingyuLiu0521/fix/AverageUpsampling

Fix Average Upsampling conflict logic, introduced from the previous batch inference fix.

src/f5_tts/model/backbones/dit.py

@@ -51,43 +51,38 @@ class TextEmbedding(nn.Module):
         else:
             self.extra_modeling = False
 
-    def average_upsample_text_by_mask(self, text, text_mask, audio_mask):
+    def average_upsample_text_by_mask(self, text, text_mask):
         batch, text_len, text_dim = text.shape
+        assert batch == 1
 
-        if audio_mask is None:
-            audio_mask = torch.ones_like(text_mask, dtype=torch.bool)
-        valid_mask = audio_mask & text_mask
-        audio_lens = audio_mask.sum(dim=1)  # [batch]
-        valid_lens = valid_mask.sum(dim=1)  # [batch]
+        valid_mask = text_mask[0]
+        audio_len = text_len
+        valid_len = valid_mask.sum().item()
+
+        if valid_len == 0:
+            return torch.zeros_like(text)
 
         upsampled_text = torch.zeros_like(text)
 
-        for i in range(batch):
-            audio_len = audio_lens[i].item()
-            valid_len = valid_lens[i].item()
-
-            if valid_len == 0:
-                continue
-
-            valid_ind = torch.where(valid_mask[i])[0]
-            valid_data = text[i, 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[i, :audio_len, :] = upsampled
+        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
 
-    def forward(self, text: int["b nt"], seq_len, drop_text=False, audio_mask: bool["b n"] | None = None):
+    def forward(self, text: int["b nt"], seq_len, drop_text=False):
         text = text + 1  # use 0 as filler token. preprocess of batch pad -1, see list_str_to_idx()
         text = text[:, :seq_len]  # curtail if character tokens are more than the mel spec tokens
         text = F.pad(text, (0, seq_len - text.shape[1]), value=0)  # (opt.) if not self.average_upsampling:
@@ -114,7 +109,7 @@ class TextEmbedding(nn.Module):
                 text = self.text_blocks(text)
 
         if self.average_upsampling:
-            text = self.average_upsample_text_by_mask(text, ~text_mask, audio_mask)
+            text = self.average_upsample_text_by_mask(text, ~text_mask)
 
         return text
 
@@ -247,17 +242,16 @@ class DiT(nn.Module):
     ):
         if self.text_uncond is None or self.text_cond is None or not cache:
             if audio_mask is None:
-                text_embed = self.text_embed(text, x.shape[1], drop_text=drop_text, audio_mask=audio_mask)
+                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)
+                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(
                         text[i].unsqueeze(0),
-                        seq_lens[i].item(),
+                        seq_len=seq_lens[i].item(),
                         drop_text=drop_text,
-                        audio_mask=audio_mask,
                     )
                     text_embed_list.append(text_embed_i[0])
                 text_embed = pad_sequence(text_embed_list, batch_first=True, padding_value=0)
@@ -331,4 +325,4 @@ class DiT(nn.Module):
         x = self.norm_out(x, t)
         output = self.proj_out(x)
 
-        return output
+        return output