docs/docs/configuration/audio_detectors.md

@@ -157,19 +157,3 @@ Only one `speech` event may be transcribed at a time. Frigate does not automatic
 :::
 
 Recorded `speech` events will always use a `whisper` model, regardless of the `model_size` config setting. Without a supported Nvidia GPU, generating transcriptions for longer `speech` events may take a fair amount of time, so be patient.
-
-#### FAQ
-
-1. Why doesn't Frigate automatically transcribe all `speech` events?
-
-   Frigate does not implement a queue mechanism for speech transcription, and adding one is not trivial. A proper queue would need backpressure, prioritization, memory/disk buffering, retry logic, crash recovery, and safeguards to prevent unbounded growth when events outpace processing. That’s a significant amount of complexity for a feature that, in most real-world environments, would mostly just churn through low-value noise.
-
-   Because transcription is **serialized (one event at a time)** and speech events can be generated far faster than they can be processed, an auto-transcribe toggle would very quickly create an ever-growing backlog and degrade core functionality. For the amount of engineering and risk involved, it adds **very little practical value** for the majority of deployments, which are often on low-powered, edge hardware.
-
-   If you hear speech that’s actually important and worth saving/indexing for the future, **just press the transcribe button in Explore** on that specific `speech` event - that keeps things explicit, reliable, and under your control.
-
-2. Why don't you save live transcription text and use that for `speech` events?
-
-   There’s no guarantee that a `speech` event is even created from the exact audio that went through the transcription model. Live transcription and `speech` event creation are **separate, asynchronous processes**. Even when both are correctly configured, trying to align the **precise start and end time of a speech event** with whatever audio the model happened to be processing at that moment is unreliable.
-
-   Automatically persisting that data would often result in **misaligned, partial, or irrelevant transcripts**, while still incurring all of the CPU, storage, and privacy costs of transcription. That’s why Frigate treats transcription as an **explicit, user-initiated action** rather than an automatic side-effect of every `speech` event.

frigate/data_processing/real_time/custom_classification.py

@@ -99,42 +99,6 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         if self.inference_speed:
             self.inference_speed.update(duration)
 
-    def _should_save_image(
-        self, camera: str, detected_state: str, score: float = 1.0
-    ) -> bool:
-        """
-        Determine if we should save the image for training.
-        Save when:
-        - State is changing or being verified (regardless of score)
-        - Score is less than 100% (even if state matches, useful for training)
-        Don't save when:
-        - State is stable (matches current_state) AND score is 100%
-        """
-        if camera not in self.state_history:
-            # First detection for this camera, save it
-            return True
-
-        verification = self.state_history[camera]
-        current_state = verification.get("current_state")
-        pending_state = verification.get("pending_state")
-
-        # Save if there's a pending state change being verified
-        if pending_state is not None:
-            return True
-
-        # Save if the detected state differs from the current verified state
-        # (state is changing)
-        if current_state is not None and detected_state != current_state:
-            return True
-
-        # If score is less than 100%, save even if state matches
-        # (useful for training to improve confidence)
-        if score < 1.0:
-            return True
-
-        # Don't save if state is stable (detected_state == current_state) AND score is 100%
-        return False
-
     def verify_state_change(self, camera: str, detected_state: str) -> str | None:
         """
         Verify state change requires 3 consecutive identical states before publishing.
@@ -248,16 +212,14 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
                 return
 
         if self.interpreter is None:
-            # When interpreter is None, always save (score is 0.0, which is < 1.0)
+            write_classification_attempt(
-            if self._should_save_image(camera, "unknown", 0.0):
+                self.train_dir,
-                write_classification_attempt(
+                cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
-                    self.train_dir,
+                "none-none",
-                    cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
+                now,
-                    "none-none",
+                "unknown",
-                    now,
+                0.0,
-                    "unknown",
+            )
-                    0.0,
-                )
             return
 
         input = np.expand_dims(resized_frame, axis=0)
@@ -274,17 +236,14 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
         score = round(probs[best_id], 2)
         self.__update_metrics(datetime.datetime.now().timestamp() - now)
 
-        detected_state = self.labelmap[best_id]
+        write_classification_attempt(
-
+            self.train_dir,
-        if self._should_save_image(camera, detected_state, score):
+            cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
-            write_classification_attempt(
+            "none-none",
-                self.train_dir,
+            now,
-                cv2.cvtColor(frame, cv2.COLOR_RGB2BGR),
+            self.labelmap[best_id],
-                "none-none",
+            score,
-                now,
+        )
-                detected_state,
-                score,
-            )
 
         if score < self.model_config.threshold:
             logger.debug(
@@ -292,6 +251,7 @@ class CustomStateClassificationProcessor(RealTimeProcessorApi):
             )
             return
 
+        detected_state = self.labelmap[best_id]
         verified_state = self.verify_state_change(camera, detected_state)
 
         if verified_state is not None:

frigate/ptz/onvif.py

@@ -190,11 +190,7 @@ class OnvifController:
         ptz: ONVIFService = await onvif.create_ptz_service()
         self.cams[camera_name]["ptz"] = ptz
 
-        try:
+        imaging: ONVIFService = await onvif.create_imaging_service()
-            imaging: ONVIFService = await onvif.create_imaging_service()
-        except (Fault, ONVIFError, TransportError, Exception) as e:
-            logger.debug(f"Imaging service not supported for {camera_name}: {e}")
-            imaging = None
         self.cams[camera_name]["imaging"] = imaging
         try:
             video_sources = await media.GetVideoSources()
@@ -385,10 +381,7 @@ class OnvifController:
                             f"Disabling autotracking zooming for {camera_name}: Absolute zoom not supported. Exception: {e}"
                         )
 
-        if (
+        if self.cams[camera_name]["video_source_token"] is not None:
-            self.cams[camera_name]["video_source_token"] is not None
-            and imaging is not None
-        ):
             try:
                 imaging_capabilities = await imaging.GetImagingSettings(
                     {"VideoSourceToken": self.cams[camera_name]["video_source_token"]}
@@ -428,7 +421,6 @@ class OnvifController:
         if (
             "focus" in self.cams[camera_name]["features"]
             and self.cams[camera_name]["video_source_token"]
-            and self.cams[camera_name]["imaging"] is not None
         ):
             try:
                 stop_request = self.cams[camera_name]["imaging"].create_type("Stop")
@@ -656,7 +648,6 @@ class OnvifController:
         if (
             "focus" not in self.cams[camera_name]["features"]
             or not self.cams[camera_name]["video_source_token"]
-            or self.cams[camera_name]["imaging"] is None
         ):
             logger.error(f"{camera_name} does not support ONVIF continuous focus.")
             return

web/src/views/settings/AuthenticationView.tsx

@@ -478,32 +478,33 @@ export default function AuthenticationView({
                       <TableCell className="text-right">
                         <TooltipProvider>
                           <div className="flex items-center justify-end gap-2">
-                            {user.username !== "admin" && (
+                            {user.username !== "admin" &&
-                              <Tooltip>
+                              user.username !== "viewer" && (
-                                <TooltipTrigger asChild>
+                                <Tooltip>
-                                  <Button
+                                  <TooltipTrigger asChild>
-                                    size="sm"
+                                    <Button
-                                    variant="outline"
+                                      size="sm"
-                                    className="h-8 px-2"
+                                      variant="outline"
-                                    onClick={() => {
+                                      className="h-8 px-2"
-                                      setSelectedUser(user.username);
+                                      onClick={() => {
-                                      setSelectedUserRole(
+                                        setSelectedUser(user.username);
-                                        user.role || "viewer",
+                                        setSelectedUserRole(
-                                      );
+                                          user.role || "viewer",
-                                      setShowRoleChange(true);
+                                        );
-                                    }}
+                                        setShowRoleChange(true);
-                                  >
+                                      }}
-                                    <LuUserCog className="size-3.5" />
+                                    >
-                                    <span className="ml-1.5 hidden sm:inline-block">
+                                      <LuUserCog className="size-3.5" />
-                                      {t("role.title", { ns: "common" })}
+                                      <span className="ml-1.5 hidden sm:inline-block">
-                                    </span>
+                                        {t("role.title", { ns: "common" })}
-                                  </Button>
+                                      </span>
-                                </TooltipTrigger>
+                                    </Button>
-                                <TooltipContent>
+                                  </TooltipTrigger>
-                                  <p>{t("users.table.changeRole")}</p>
+                                  <TooltipContent>
-                                </TooltipContent>
+                                    <p>{t("users.table.changeRole")}</p>
-                              </Tooltip>
+                                  </TooltipContent>
-                            )}
+                                </Tooltip>
+                              )}
 
                             <Tooltip>
                               <TooltipTrigger asChild>