diff --git a/frigate/embeddings/onnx/jina_v2_embedding.py b/frigate/embeddings/onnx/jina_v2_embedding.py
index fd4323f85..1abd968c9 100644
--- a/frigate/embeddings/onnx/jina_v2_embedding.py
+++ b/frigate/embeddings/onnx/jina_v2_embedding.py
@@ -3,6 +3,7 @@
 import io
 import logging
 import os
+import threading
 
 import numpy as np
 from PIL import Image
@@ -53,6 +54,11 @@ class JinaV2Embedding(BaseEmbedding):
         self.tokenizer = None
         self.image_processor = None
         self.runner = None
+
+        # Lock to prevent concurrent calls (text and vision share this instance)
+        self._call_lock = threading.Lock()
+
+        # download the model and tokenizer
         files_names = list(self.download_urls.keys()) + [self.tokenizer_file]
         if not all(
             os.path.exists(os.path.join(self.download_path, n)) for n in files_names
@@ -200,37 +206,40 @@ class JinaV2Embedding(BaseEmbedding):
     def __call__(
         self, inputs: list[str] | list[Image.Image] | list[str], embedding_type=None
     ) -> list[np.ndarray]:
-        self.embedding_type = embedding_type
-        if not self.embedding_type:
-            raise ValueError(
-                "embedding_type must be specified either in __init__ or __call__"
-            )
+        # Lock the entire call to prevent race conditions when text and vision
+        # embeddings are called concurrently from different threads
+        with self._call_lock:
+            self.embedding_type = embedding_type
+            if not self.embedding_type:
+                raise ValueError(
+                    "embedding_type must be specified either in __init__ or __call__"
+                )
 
-        self._load_model_and_utils()
-        processed = self._preprocess_inputs(inputs)
-        batch_size = len(processed)
+            self._load_model_and_utils()
+            processed = self._preprocess_inputs(inputs)
+            batch_size = len(processed)
 
-        # Prepare ONNX inputs with matching batch sizes
-        onnx_inputs = {}
-        if self.embedding_type == "text":
-            onnx_inputs["input_ids"] = np.stack([x[0] for x in processed])
-            onnx_inputs["pixel_values"] = np.zeros(
-                (batch_size, 3, 512, 512), dtype=np.float32
-            )
-        elif self.embedding_type == "vision":
-            onnx_inputs["input_ids"] = np.zeros((batch_size, 16), dtype=np.int64)
-            onnx_inputs["pixel_values"] = np.stack([x[0] for x in processed])
-        else:
-            raise ValueError("Invalid embedding type")
+            # Prepare ONNX inputs with matching batch sizes
+            onnx_inputs = {}
+            if self.embedding_type == "text":
+                onnx_inputs["input_ids"] = np.stack([x[0] for x in processed])
+                onnx_inputs["pixel_values"] = np.zeros(
+                    (batch_size, 3, 512, 512), dtype=np.float32
+                )
+            elif self.embedding_type == "vision":
+                onnx_inputs["input_ids"] = np.zeros((batch_size, 16), dtype=np.int64)
+                onnx_inputs["pixel_values"] = np.stack([x[0] for x in processed])
+            else:
+                raise ValueError("Invalid embedding type")
 
-        # Run inference
-        outputs = self.runner.run(onnx_inputs)
-        if self.embedding_type == "text":
-            embeddings = outputs[2]  # text embeddings
-        elif self.embedding_type == "vision":
-            embeddings = outputs[3]  # image embeddings
-        else:
-            raise ValueError("Invalid embedding type")
+            # Run inference
+            outputs = self.runner.run(onnx_inputs)
+            if self.embedding_type == "text":
+                embeddings = outputs[2]  # text embeddings
+            elif self.embedding_type == "vision":
+                embeddings = outputs[3]  # image embeddings
+            else:
+                raise ValueError("Invalid embedding type")
 
-        embeddings = self._postprocess_outputs(embeddings)
-        return [embedding for embedding in embeddings]
+            embeddings = self._postprocess_outputs(embeddings)
+            return [embedding for embedding in embeddings]
diff --git a/web/src/components/filter/SearchFilterGroup.tsx b/web/src/components/filter/SearchFilterGroup.tsx
index 3c44cad0c..fe9a70e18 100644
--- a/web/src/components/filter/SearchFilterGroup.tsx
+++ b/web/src/components/filter/SearchFilterGroup.tsx
@@ -251,11 +251,30 @@ function GeneralFilterButton({
   updateLabelFilter,
 }: GeneralFilterButtonProps) {
   const { t } = useTranslation(["components/filter"]);
+  const { data: config } = useSWR<FrigateConfig>("config", {
+    revalidateOnFocus: false,
+  });
   const [open, setOpen] = useState(false);
   const [currentLabels, setCurrentLabels] = useState<string[] | undefined>(
     selectedLabels,
   );
 
+  const allAudioListenLabels = useMemo<Set<string>>(() => {
+    if (!config) {
+      return new Set<string>();
+    }
+
+    const labels = new Set<string>();
+    Object.values(config.cameras).forEach((camera) => {
+      if (camera?.audio?.enabled) {
+        camera.audio.listen.forEach((label) => {
+          labels.add(label);
+        });
+      }
+    });
+    return labels;
+  }, [config]);
+
   const buttonText = useMemo(() => {
     if (isMobile) {
       return t("labels.all.short");
@@ -266,13 +285,17 @@ function GeneralFilterButton({
     }
 
     if (selectedLabels.length == 1) {
-      return getTranslatedLabel(selectedLabels[0]);
+      const label = selectedLabels[0];
+      return getTranslatedLabel(
+        label,
+        allAudioListenLabels.has(label) ? "audio" : "object",
+      );
     }
 
     return t("labels.count", {
       count: selectedLabels.length,
     });
-  }, [selectedLabels, t]);
+  }, [selectedLabels, allAudioListenLabels, t]);
 
   // ui
 
diff --git a/web/src/pages/FaceLibrary.tsx b/web/src/pages/FaceLibrary.tsx
index 628928562..8ad6f70e5 100644
--- a/web/src/pages/FaceLibrary.tsx
+++ b/web/src/pages/FaceLibrary.tsx
@@ -925,11 +925,11 @@ function FaceAttemptGroup({
     [onRefresh, t],
   );
 
-  // Create ClassifiedEvent from Event (face recognition uses sub_label)
   const classifiedEvent: ClassifiedEvent | undefined = useMemo(() => {
-    if (!event || !event.sub_label || event.sub_label === "none") {
+    if (!event) {
       return undefined;
     }
+
     return {
       id: event.id,
       label: event.sub_label,
diff --git a/web/src/utils/i18n.ts b/web/src/utils/i18n.ts
index eba5ff7a5..ca7ad8e25 100644
--- a/web/src/utils/i18n.ts
+++ b/web/src/utils/i18n.ts
@@ -79,6 +79,24 @@ i18n
     parseMissingKeyHandler: (key: string) => {
       const parts = key.split(".");
 
+      // eslint-disable-next-line no-console
+      console.warn(`Missing translation key: ${key}`);
+
+      if (parts[0] === "time" && parts[1]?.includes("formattedTimestamp")) {
+        // Extract the format type from the last part (12hour, 24hour)
+        const formatType = parts[parts.length - 1];
+
+        // Return actual date-fns format strings as fallbacks
+        const formatDefaults: Record<string, string> = {
+          "12hour": "h:mm aaa",
+          "24hour": "HH:mm",
+        };
+
+        if (formatDefaults[formatType]) {
+          return formatDefaults[formatType];
+        }
+      }
+
       // Handle special cases for objects and audio
       if (parts[0] === "object" || parts[0] === "audio") {
         return (
diff --git a/web/src/views/classification/ModelTrainingView.tsx b/web/src/views/classification/ModelTrainingView.tsx
index 31e6f6d53..ec7ce0472 100644
--- a/web/src/views/classification/ModelTrainingView.tsx
+++ b/web/src/views/classification/ModelTrainingView.tsx
@@ -1043,34 +1043,22 @@ function ObjectTrainGrid({
         return undefined;
       }
 
-      const classificationType = model.object_config.classification_type;
+      let label: string | undefined = undefined;
+      let score: number | undefined = undefined;
 
-      if (classificationType === "attribute") {
-        // For attribute type, look at event.data[model.name]
-        const attributeValue = event.data[model.name] as string | undefined;
-        const attributeScore = event.data[`${model.name}_score`] as
-          | number
-          | undefined;
-
-        if (attributeValue && attributeValue !== "none") {
-          return {
-            id: event.id,
-            label: attributeValue,
-            score: attributeScore,
-          };
-        }
+      if (model.object_config.classification_type === "attribute") {
+        label = event.data[model.name] as string | undefined;
+        score = event.data[`${model.name}_score`] as number | undefined;
       } else {
-        // For sub_label type, use event.sub_label
-        if (event.sub_label && event.sub_label !== "none") {
-          return {
-            id: event.id,
-            label: event.sub_label,
-            score: event.data?.sub_label_score,
-          };
-        }
+        label = event.sub_label;
+        score = event.data.sub_label_score;
       }
 
-      return undefined;
+      return {
+        id: event.id,
+        label: label,
+        score: score,
+      };
     },
     [model],
   );