moved imports to top

loaders.py

@@ -1,40 +1,41 @@
-import torch
-from transformers import HubertModel, HubertConfig
-
-class HubertModelWithFinalProj(HubertModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        # The final projection layer is only used for backward compatibility.
-        # Following https://github.com/auspicious3000/contentvec/issues/6
-        # Remove this layer is necessary to achieve the desired outcome.
-        self.final_proj = torch.nn.Linear(config.hidden_size, config.classifier_proj_size)
-
-    @staticmethod
-    def load_safetensors(path: str, device="cpu"):
-        assert path.endswith(".safetensors"), f"{path} must end with '.safetensors'"
-        from safetensors import safe_open
-        import json
-        with safe_open(path, framework="pt", device="cpu") as f:
-            metadata = f.metadata()
-            state_dict = {}
-            for key in f.keys():
-                state_dict[key] = f.get_tensor(key)
-        model = HubertModelWithFinalProj(HubertConfig.from_dict(json.loads(metadata["config"])))
-        model.load_state_dict(state_dict=state_dict)
-        return model.to(device)
-    
-    def save_safetensors(self, path: str):
-        assert path.endswith(".safetensors"), f"{path} must end with '.safetensors'"
-        import safetensors.torch as st
-        import json
-        with open(path,"wb") as f:
-            state_dict = self.state_dict()
-            f.write(st.save(state_dict,dict(config=json.dumps(self.config.to_dict()))))
-
-    def extract_features(self, source: torch.Tensor, version="v2", **kwargs):
-        with torch.no_grad():
-            output_layer = 9 if version == "v1" else 12
-            output = self(source.to(self.config.torch_dtype), output_hidden_states=True)["hidden_states"][output_layer]
-            features = self.final_proj(output) if version == "v1" else output
+import torch
+from transformers import HubertModel, HubertConfig
+import safetensors.torch as st
+from safetensors import safe_open
+import json
+
+class HubertModelWithFinalProj(HubertModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        # The final projection layer is only used for backward compatibility.
+        # Following https://github.com/auspicious3000/contentvec/issues/6
+        # Remove this layer is necessary to achieve the desired outcome.
+        self.final_proj = torch.nn.Linear(config.hidden_size, config.classifier_proj_size)
+
+    @staticmethod
+    def load_safetensors(path: str, device="cpu"):
+        assert path.endswith(".safetensors"), f"{path} must end with '.safetensors'"
+        
+        with safe_open(path, framework="pt", device="cpu") as f:
+            metadata = f.metadata()
+            state_dict = {}
+            for key in f.keys():
+                state_dict[key] = f.get_tensor(key)
+        model = HubertModelWithFinalProj(HubertConfig.from_dict(json.loads(metadata["config"])))
+        model.load_state_dict(state_dict=state_dict)
+        return model.to(device)
+    
+    def save_safetensors(self, path: str):
+        assert path.endswith(".safetensors"), f"{path} must end with '.safetensors'"
+        
+        with open(path,"wb") as f:
+            state_dict = self.state_dict()
+            f.write(st.save(state_dict,dict(config=json.dumps(self.config.to_dict()))))
+
+    def extract_features(self, source: torch.Tensor, version="v2", **kwargs):
+        with torch.no_grad():
+            output_layer = 9 if version == "v1" else 12
+            output = self(source.to(self.config.torch_dtype), output_hidden_states=True)["hidden_states"][output_layer]
+            features = self.final_proj(output) if version == "v1" else output
         return features