diff --git a/frigate/embeddings/functions/onnx.py b/frigate/embeddings/functions/onnx.py
index a116ac9dc..1e8ba2f25 100644
--- a/frigate/embeddings/functions/onnx.py
+++ b/frigate/embeddings/functions/onnx.py
@@ -13,6 +13,7 @@ from PIL import Image
 # https://github.com/huggingface/transformers/issues/27214
 # suppressed by setting env TRANSFORMERS_NO_ADVISORY_WARNINGS=1
 from transformers import AutoFeatureExtractor, AutoTokenizer
+from transformers.utils.logging import disable_progress_bar
 
 from frigate.const import MODEL_CACHE_DIR, UPDATE_MODEL_STATE
 from frigate.types import ModelStatusTypesEnum
@@ -24,7 +25,8 @@ warnings.filterwarnings(
     message="The class CLIPFeatureExtractor is deprecated",
 )
 
-
+# disables the progress bar for downloading tokenizers and feature extractors
+disable_progress_bar()
 logger = logging.getLogger(__name__)
 
 
@@ -69,11 +71,9 @@ class GenericONNXEmbedding:
                 ModelDownloader.download_from_url(self.download_urls[file_name], path)
             elif file_name == self.tokenizer_file:
                 logger.info(path + "/" + self.model_name)
-                if not os.path.exists(path + "/" + self.model_name):
-                    logger.info(
-                        f"Downloading {self.model_name} tokenizer/feature extractor"
-                    )
                 if self.model_type == "text":
+                    if not os.path.exists(path + "/" + self.model_name):
+                        logger.info(f"Downloading {self.model_name} tokenizer")
                     tokenizer = AutoTokenizer.from_pretrained(
                         self.model_name,
                         cache_dir=f"{MODEL_CACHE_DIR}/{self.model_name}/tokenizer",
@@ -81,6 +81,8 @@ class GenericONNXEmbedding:
                     )
                     tokenizer.save_pretrained(path)
                 else:
+                    if not os.path.exists(path + "/" + self.model_name):
+                        logger.info(f"Downloading {self.model_name} feature extractor")
                     feature_extractor = AutoFeatureExtractor.from_pretrained(
                         self.model_name,
                         cache_dir=f"{MODEL_CACHE_DIR}/{self.model_name}/feature_extractor",