diff --git a/frigate/embeddings/functions/onnx.py b/frigate/embeddings/functions/onnx.py
index c82b60517..454fe3faf 100644
--- a/frigate/embeddings/functions/onnx.py
+++ b/frigate/embeddings/functions/onnx.py
@@ -119,27 +119,21 @@ class GenericONNXEmbedding:
             )
 
     def _load_tokenizer(self):
-        if not self.tokenizer:
-            tokenizer_path = os.path.join(
-                f"{MODEL_CACHE_DIR}/{self.model_name}/tokenizer"
-            )
-            self.tokenizer = AutoTokenizer.from_pretrained(
-                self.model_name,
-                cache_dir=tokenizer_path,
-                trust_remote_code=True,
-                clean_up_tokenization_spaces=True,
-            )
+        tokenizer_path = os.path.join(f"{MODEL_CACHE_DIR}/{self.model_name}/tokenizer")
+        return AutoTokenizer.from_pretrained(
+            self.model_name,
+            cache_dir=tokenizer_path,
+            trust_remote_code=True,
+            clean_up_tokenization_spaces=True,
+        )
 
     def _load_feature_extractor(self):
-        if not self.feature_extractor:
-            feature_extractor_path = os.path.join(
-                f"{MODEL_CACHE_DIR}/{self.model_name}/feature_extractor"
-            )
-            self.feature_extractor = AutoFeatureExtractor.from_pretrained(
-                self.model_name,
-                trust_remote_code=True,
-                cache_dir=feature_extractor_path,
-            )
+        feature_extractor_path = os.path.join(
+            f"{MODEL_CACHE_DIR}/{self.model_name}/feature_extractor"
+        )
+        return AutoFeatureExtractor.from_pretrained(
+            self.model_name, trust_remote_code=True, cache_dir=feature_extractor_path
+        )
 
     def _load_model(self, path: str, providers: List[str]):
         if os.path.exists(path):