feat:onnx friendly loop with same function

GPT_SoVITS/module/core_vq.py

@@ -357,9 +357,17 @@ class ResidualVectorQuantization(nn.Module):
         return out_indices
 
     def decode(self, q_indices: torch.Tensor, st: int = 0) -> torch.Tensor:
-        quantized_out = torch.tensor(0.0, device=q_indices.device)
-        for i, indices in enumerate(q_indices):
-            layer = self.layers[st + i]
-            quantized = layer.decode(indices)
-            quantized_out = quantized_out + quantized
+        # ONNX-friendly approach: use unbind instead of enumerate loop
+        indices_list = torch.unbind(q_indices, dim=0)
+        quantized_list = []
+        
+        for i, indices in enumerate(indices_list):
+            if st + i < len(self.layers):
+                layer = self.layers[st + i]
+                quantized = layer.decode(indices)
+                quantized_list.append(quantized)
+        
+        # Stack and sum instead of iterative addition
+        quantized_out = torch.stack(quantized_list, dim=0).sum(dim=0)
+            
         return quantized_out