diff --git a/frigate/embeddings/functions/onnx.py b/frigate/embeddings/functions/onnx.py
index 454fe3faf..c82b60517 100644
--- a/frigate/embeddings/functions/onnx.py
+++ b/frigate/embeddings/functions/onnx.py
@@ -119,21 +119,27 @@ class GenericONNXEmbedding:
             )
 
     def _load_tokenizer(self):
-        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,
-        )
+        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,
+            )
 
     def _load_feature_extractor(self):
-        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
-        )
+        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,
+            )
 
     def _load_model(self, path: str, providers: List[str]):
         if os.path.exists(path):