Miscellaneous Fixes (#21005)

* update live view docs

* use swr as single source of truth for searchDetail

rather than maintaining a separate state, derive the selected item from swr cache. fixes websocket sync when regenerating descriptions or fetching transcriptions

* fix key warning in console

* don't try to fetch event from review item for audio events

* update audio transcription toast wording

* Add a community supported badge to specific detectors in the info summaries to better separate

* Make object classification publish to tracked object update and add examples for state classification

* Add item to advanced docs about tensorflow limiting

* Don't show submission for in progress objects

* fix for ios not reporting video dimensions on initial metadata load

in testing, polling with requestAnimationFrame finds the dimensions within 2 frames

* Catch jetson nvidia device tree

---------

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>

docs/docs/configuration/object_detectors.md

@@ -3,6 +3,8 @@ id: object_detectors
 title: Object Detectors
 ---
 
+import CommunityBadge from '@site/src/components/CommunityBadge';
+
 # Supported Hardware
 
 :::info
@@ -13,8 +15,8 @@ Frigate supports multiple different detectors that work on different types of ha
 
 - [Coral EdgeTPU](#edge-tpu-detector): The Google Coral EdgeTPU is available in USB and m.2 format allowing for a wide range of compatibility with devices.
 - [Hailo](#hailo-8): The Hailo8 and Hailo8L AI Acceleration module is available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
-- [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
-- [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
+- <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
+- <CommunityBadge /> [DeGirum](#degirum): Service for using hardware devices in the cloud or locally. Hardware and models provided on the cloud on [their website](https://hub.degirum.com).
 
 **AMD**
 
@@ -34,16 +36,16 @@ Frigate supports multiple different detectors that work on different types of ha
 
 - [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt` Frigate image when a supported ONNX model is configured.
 
-**Nvidia Jetson**
+**Nvidia Jetson** <CommunityBadge />
 
 - [TensortRT](#nvidia-tensorrt-detector): TensorRT can run on Jetson devices, using one of many default models.
 - [ONNX](#onnx): TensorRT will automatically be detected and used as a detector in the `-tensorrt-jp6` Frigate image when a supported ONNX model is configured.
 
-**Rockchip**
+**Rockchip** <CommunityBadge />
 
 - [RKNN](#rockchip-platform): RKNN models can run on Rockchip devices with included NPUs.
 
-**Synaptics**
+**Synaptics** <CommunityBadge />
 
 - [Synaptics](#synaptics): synap models can run on Synaptics devices(e.g astra machina) with included NPUs.
 
@@ -988,7 +990,7 @@ model:
   # Optional: The model is normally fetched through the runtime, so 'path' can be omitted unless you want to use a custom or local model.
   # path: /config/yolox.zip
   # The .zip file must contain:
-  # ├── yolox.dfp          (a file ending with .dfp) 
+  # ├── yolox.dfp          (a file ending with .dfp)
 ```
 
 #### SSDLite MobileNet v2