show runtime overrides in the settings form

The settings form read a camera section's saved config value, but its dependent warnings (audio transcription requiring audio detection, snapshots requiring detect, etc.) read the live config instead. A runtime toggle from the live view, MQTT, or an active profile can turn a section off without touching yaml, and that override persists across restarts, so the Enable switch showed on while the warning said the feature wasn't enabled. This adds an "Overridden (Live)" badge to any field whose live value differs from what's saved, and swaps the affected warnings to runtime-specific wording when a runtime override is the actual cause instead of the config.
fix restart failing under non-root
2026-10-14 18:52:47 +03:00 · 2026-09-17 20:42:41 -05:00 · 2026-09-17 20:42:15 -05:00 · 2026-09-17 16:55:39 -05:00 · 2026-09-17 16:55:39 -05:00 · 2026-09-17 16:55:31 -05:00
597 changed files with 7645 additions and 37779 deletions
@@ -17,14 +17,9 @@ runs:
      shell: bash
    # This creates a virtual volume at /var/lib/docker to maximize the size
    # As of 2/14/2024, this results in 97G for docker images
    # Runners no longer have a separate /mnt disk, so the temp PV is also carved
    # from root and temp-reserve-mb is what actually stays free on root. Keep 4G
    # there for setup-qemu/buildx caches in ~/.docker and the tool cache
    - name: Maximize build space
      uses: easimon/maximize-build-space@master
      with:
        root-reserve-mb: 8192
        temp-reserve-mb: 4096
        remove-dotnet: 'true'
        remove-android: 'true'
        remove-haskell: 'true'
@@ -34,15 +29,15 @@ runs:
      run: sudo systemctl start docker
      shell: bash
    - id: lowercaseRepo
-      uses: ASzc/change-string-case-action@v8
+      uses: ASzc/change-string-case-action@v5
      with:
        string: ${{ github.repository }}
    - name: Set up QEMU
-      uses: docker/setup-qemu-action@v4
+      uses: docker/setup-qemu-action@v3
    - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v4
+      uses: docker/setup-buildx-action@v3
    - name: Log in to the Container registry
-      uses: docker/login-action@dbcb813823bdd20940b903addbd779551569679f
+      uses: docker/login-action@465a07811f14bebb1938fbed4728c6a1ff8901fc
      with:
        registry: ghcr.io
        username: ${{ github.actor }}
@@ -1,157 +1,40 @@
 version: 2
 updates:
  - package-ecosystem: "github-actions"
-    # Local composite actions are only scanned when their directory is listed
+    directory: "/"
    directories:
      - "/"
      - "/.github/actions/setup"
    schedule:
-      interval: weekly
+      interval: daily
-    cooldown:
+    open-pull-requests-limit: 10
-      default-days: 3
+    target-branch: dev
-    open-pull-requests-limit: 5
+  - package-ecosystem: "docker"
-    groups:
+    directory: "/docker"
-      actions:
+    schedule:
-        patterns: ["*"]
+      interval: daily
-      actions-security:
+    open-pull-requests-limit: 10
-        applies-to: security-updates
+    target-branch: dev
        patterns: ["*"]
  - package-ecosystem: "pip"
    directory: "/docker/main"
    schedule:
-      interval: weekly
+      interval: daily
-    cooldown:
+    open-pull-requests-limit: 10
-      default-days: 3
+    target-branch: dev
    open-pull-requests-limit: 5
    groups:
      # Sets that fail pip resolution unless they move together
      peewee:
        patterns: ["peewee", "peewee-migrate"]
      crypto:
        patterns: ["cryptography", "joserfc", "pywebpush", "py-vapid"]
      pydantic-genai:
        patterns: ["pydantic", "google-genai"]
      pip-main-minor-patch:
        patterns: ["*"]
        update-types: ["minor", "patch"]
      pip-main-security:
        applies-to: security-updates
        patterns: ["*"]
    ignore:
      # numpy 2.5 needs Python 3.12; norfair, tflite_runtime and rknn-toolkit2
      # cap numpy below 2
      - dependency-name: "numpy"
        versions: [">=2"]
      # Every OpenCV wheel from 4.12 requires numpy 2
      - dependency-name: "opencv-python-headless"
        versions: [">=4.12"]
      - dependency-name: "opencv-contrib-python"
        versions: [">=4.12"]
      # aarch64 only. Rockchip rknn-toolkit2 caps protobuf at 4.25.4 and
      # TensorFlow 2.21 needs protobuf 6.31 or newer.
      - dependency-name: "tensorflow"
        versions: [">=2.20"]
      # Needs Python 3.12 and numpy 2
      - dependency-name: "librosa"
        versions: [">=1.0"]
      # Needs Python 3.12
      - dependency-name: "scipy"
        versions: [">=1.18"]
      # Newer stubs report false errors against peewee 4
      - dependency-name: "types-peewee"
        versions: [">=4.1"]
      # sdist only and imported by HailoRT; needs a Hailo device to test
      - dependency-name: "netifaces"
        versions: [">=0.11"]
  - package-ecosystem: "pip"
    directory: "/docker/tensorrt"
    schedule:
-      interval: weekly
+      interval: daily
-    # No pull request check builds these images, so version updates are off.
+    open-pull-requests-limit: 10
-    # Security updates still open.
+    target-branch: dev
    open-pull-requests-limit: 0
    groups:
      pip-trt-security:
        applies-to: security-updates
        patterns: ["*"]
    ignore:
      # onnx 1.17 and newer write a newer IR and opset than TensorRT 8.6 on
      # JetPack 6 is known to parse
      - dependency-name: "onnx"
        versions: [">=1.17"]
      # 1.27 moved to CUDA 13, and the image ships the CUDA 12.8 wheel set
      - dependency-name: "onnxruntime-gpu"
        versions: [">=1.27"]
      # Hand-matched CUDA 12.8 set (2c9a25e67); move as a block
      - dependency-name: "nvidia-*"
      # Must match the JetPack CUDA runtime
      - dependency-name: "cuda-python"
        versions: [">=12.9"]
  - package-ecosystem: "npm"
    directory: "/web"
    schedule:
-      interval: weekly
+      interval: daily
-    cooldown:
+    open-pull-requests-limit: 10
-      default-days: 3
+    target-branch: dev
    open-pull-requests-limit: 5
    groups:
      radix:
        patterns: ["@radix-ui/*"]
      rjsf:
        patterns: ["@rjsf/*"]
      web-minor-patch:
        patterns: ["*"]
        update-types: ["minor", "patch"]
      web-security:
        applies-to: security-updates
        patterns: ["*"]
    ignore:
      # typescript-eslint peers typescript below 6.1
      - dependency-name: "typescript"
        versions: [">=6.1"]
      # Tailwind 4 is a config rewrite; scrollbar 4 and merge 3 depend on it
      - dependency-name: "tailwindcss"
        update-types: ["version-update:semver-major"]
      - dependency-name: "tailwind-scrollbar"
        update-types: ["version-update:semver-major"]
      - dependency-name: "tailwind-merge"
        update-types: ["version-update:semver-major"]
      # Intermittent failure in the UI settings import spec, cause not found
      - dependency-name: "zod"
        update-types: ["version-update:semver-major"]
      # Needs an on-device pinch test before moving
      - dependency-name: "react-zoom-pan-pinch"
        update-types: ["version-update:semver-major"]
      # Build fails until monaco-worker-manager releases its PR 4
      - dependency-name: "monaco-editor"
        versions: [">=0.55.0"]
      # Later versions need Node 22 and extract false keys
      - dependency-name: "i18next-cli"
        versions: [">1.5.11"]
      # 6.11.0 refills the saved model in the GenAI settings form
      - dependency-name: "@rjsf/*"
        versions: ["6.11.0"]
  - package-ecosystem: "npm"
    directory: "/docs"
    schedule:
-      interval: weekly
+      interval: daily
    cooldown:
      default-days: 3
    open-pull-requests-limit: 5
    allow:
      - dependency-name: "@docusaurus/*"
-    groups:
+    open-pull-requests-limit: 10
-      docusaurus:
+    target-branch: dev
        patterns: ["@docusaurus/*"]
      docs-security:
        applies-to: security-updates
        patterns: ["*"]
    ignore:
      # A Docusaurus major needs the openapi plugin and theme moved in the
      # same commit
      - dependency-name: "@docusaurus/*"
        update-types: ["version-update:semver-major"]
@@ -586,7 +586,7 @@ jobs:
      - arm64_build
    steps:
      - id: lowercaseRepo
-        uses: ASzc/change-string-case-action@v8
+        uses: ASzc/change-string-case-action@v6
        with:
          string: ${{ github.repository }}
      - name: Log in to the Container registry
@@ -14,7 +14,7 @@ jobs:
        with:
          persist-credentials: false
      - id: lowercaseRepo
-        uses: ASzc/change-string-case-action@v8
+        uses: ASzc/change-string-case-action@v6
        with:
          string: ${{ github.repository }}
      - name: Log in to the Container registry
@@ -5,4 +5,3 @@
 /docker/rockchip/ @MarcA711
 /docker/rocm/ @harakas
 /docker/hailo8l/ @spanner3003
 /docker/deepx/ @sixfab
@@ -1,131 +0,0 @@
 #!/bin/bash
 # Installs the DEEPX NPU kernel driver and the DX-RT runtime on the Docker host,
 # then enables the vendor's dxrt.service. A container cannot load kernel
 # modules, so this runs outside the image; the driver creates the /dev/dxrt*
 # nodes and the daemon multiplexes the NPU across host and container.
 #
 # Driver, runtime and firmware versions must agree or inference hangs instead
 # of failing at startup. The set this script installs is pinned in
 # driver_version, runtime_version and firmware_version below; move them
 # together, never one at a time.
 #
 # DEEPX NPU support in Frigate is maintained by Sixfab (https://sixfab.com).
 set -euo pipefail
 driver_version="v2.6.0"
 # the commit the tag resolves to, since DEEPX signs neither tags nor releases
 # and this is compiled and installed as root. Update both together
 driver_commit="7074748e7104f470b02f517583abba652b3f05fa"
 firmware_version="v2.7.4"
 sudo apt-get update
 sudo apt-get install -y git build-essential "linux-headers-$(uname -r)" pciutils wget
 if ! lspci -d 1ff4: | grep -q .; then
    echo "No DEEPX device found on the PCIe bus (lspci -d 1ff4:)."
    echo "Check that the module is seated correctly before continuing."
    exit 1
 fi
 # fetch the pinned commit rather than cloning the tag, so a retag cannot swap
 # in different source. The build directory is reused so a second run after a
 # failure does not stop on the directory already being there
 mkdir -p dx_rt_npu_linux_driver
 cd dx_rt_npu_linux_driver
 git init -q
 git remote get-url origin > /dev/null 2>&1 ||
    git remote add origin https://github.com/DEEPX-AI/dx_rt_npu_linux_driver.git
 git fetch --depth 1 origin "${driver_commit}"
 git checkout -q FETCH_HEAD
 fetched_commit=$(git rev-parse HEAD)
 if [[ "${fetched_commit}" != "${driver_commit}" ]]; then
    echo "Fetched commit ${fetched_commit} does not match pinned driver_commit ${driver_commit}."
    echo "Refusing to build unverified driver source."
    exit 1
 fi
 cd modules
 sudo ./build.sh -c install --reload
 sudo depmod -A
 # dx_dma is the PCIe transport, dxrt_driver the NPU driver on top of it
 for module in dx_dma dxrt_driver; do
    if ! sudo modprobe "${module}"; then
        echo "Unable to load the ${module} kernel module, common reasons are:"
        echo "- Secure Boot is enabled and is rejecting the unsigned module."
        echo "- The running kernel does not match the installed linux-headers."
        exit 1
    fi
 done
 if ! compgen -G "/dev/dxrt*" > /dev/null; then
    echo "Modules loaded but no /dev/dxrt* device node appeared."
    echo "Run ./sanity_check.sh from the driver repo to diagnose."
    exit 1
 fi
 runtime_version="v3.4.0"
 declare -A runtime_sha256=(
    [amd64]="736cfef009ce9e974ab1ab610d867239d19d72a426a53e367ddcbd53297b6e20"
    [arm64]="eb6107f5f02f2ad76ae89f414e8b5f346f34fbc6f0888236136853a26be6f6a0"
 )
 runtime_release="${runtime_version#v}"
 deb_arch=$(dpkg --print-architecture)
 deb_file="/tmp/libdxrt-bin_${runtime_release}_${deb_arch}.deb"
 wget -qO "${deb_file}" \
    "https://raw.githubusercontent.com/DEEPX-AI/dx_rt/${runtime_version}/release/${runtime_release}/libdxrt-bin_${runtime_release}_${deb_arch}.deb"
 expected_sha256="${runtime_sha256[${deb_arch}]:-}"
 if [[ -z "${expected_sha256}" ]]; then
    echo "No pinned SHA-256 for architecture ${deb_arch}; refusing to install."
    exit 1
 fi
 if [[ "$(sha256sum "${deb_file}" | cut -d' ' -f1)" != "${expected_sha256}" ]]; then
    echo "SHA-256 mismatch for ${deb_file}; refusing to install."
    exit 1
 fi
 sudo dpkg -i "${deb_file}"
 sudo ldconfig
 rm -f "${deb_file}"
 sudo cp /usr/share/libdxrt-bin/service/dxrt.service /etc/systemd/system/
 # With an endpoint set, dxrtd binds that path only, so the socket goes in a
 # directory Frigate can mount (kept across restarts so the mount stays valid)
 # and a symlink at the default /tmp path keeps host tools that do not set the
 # variable working through their own fallback.
 sudo mkdir -p /etc/systemd/system/dxrt.service.d
 sudo tee /etc/systemd/system/dxrt.service.d/frigate.conf > /dev/null <<'UNIT'
 [Service]
 RuntimeDirectory=dxrt
 RuntimeDirectoryMode=0755
 RuntimeDirectoryPreserve=yes
 Environment=DXRT_DYNAMIC_IPC_ENDPOINT=/run/dxrt/dxrt_dynamic_ipc.sock
 ExecStartPost=/bin/ln -sfn /run/dxrt/dxrt_dynamic_ipc.sock /tmp/dxrt_dynamic_ipc.sock
 UNIT
 sudo systemctl daemon-reload
 sudo systemctl enable dxrt.service
 sudo systemctl restart dxrt.service
 if ! sudo systemctl is-active --quiet dxrt.service; then
    echo "dxrt.service did not start. Check: sudo journalctl -u dxrt.service"
    exit 1
 fi
 echo "DEEPX driver and runtime installation complete."
 echo "Driver version:   $(modinfo -F version dxrt_driver) (expected ${driver_version#v})"
 echo "Runtime version:  ${runtime_release}"
 echo "Device node(s):   $(echo /dev/dxrt*)"
 echo
 echo "This driver expects NPU firmware ${firmware_version}. Check it with:"
 echo "  dxrt-cli --status"
 echo "Update the module if it does not match before starting Frigate."
@@ -1,4 +1,4 @@
-ruff == 0.16.10
+ruff == 0.15.20
 # types
 types-peewee == 4.0.*
@@ -1,66 +1,66 @@
 aiofiles == 25.1.*
 click == 8.5.*
 # FastAPI
-aiohttp == 3.14.*
+aiohttp == 3.12.*
-starlette == 1.7.*
+starlette == 0.47.*
 starlette-context == 0.5.*
-fastapi[standard-no-fastapi-cloud-cli] == 0.142.*
+fastapi[standard-no-fastapi-cloud-cli] == 0.116.*
-uvicorn == 0.54.*
+uvicorn == 0.52.*
 slowapi == 0.1.*
-joserfc == 1.7.*
+joserfc == 1.6.*
-cryptography == 50.0.*
+cryptography == 46.0.*
 pathvalidate == 3.3.*
 markupsafe == 3.0.*
-python-multipart == 0.0.32
+python-multipart == 0.0.31
 # Classification Model Training
 tensorflow == 2.19.* ; platform_machine == 'aarch64'
-tensorflow-cpu == 2.21.* ; platform_machine == 'x86_64'
+tensorflow-cpu == 2.19.* ; platform_machine == 'x86_64'
 # General
-mypy == 2.4.0
+mypy == 1.6.1
-onvif-zeep-async == 4.3.*
+onvif-zeep-async == 4.0.*
 paho-mqtt == 2.1.*
-pandas == 3.0.*
+pandas == 2.2.*
-peewee == 4.5.*
+peewee == 3.17.*
-peewee_migrate == 2.3.*
+peewee_migrate == 1.14.*
-psutil == 7.2.*
+psutil == 7.1.*
-pydantic == 2.13.*
+pydantic == 2.10.*
 git+https://github.com/fbcotter/py3nvml#egg=py3nvml
-tzdata == 2026.5
+pytz == 2025.*
-pyzmq == 27.2.*
+pyzmq == 27.1.*
-ruamel.yaml == 0.19.*
+ruamel.yaml == 0.18.*
-tzlocal == 5.4.4
+tzlocal == 5.2
-requests == 2.34.*
+requests == 2.33.*
-types-requests == 2.33.*
+types-requests == 2.32.*
 norfair == 2.3.*
 setproctitle == 1.3.*
-ws4py == 0.6.*
+ws4py == 0.5.*
 unidecode == 1.4.*
 titlecase == 2.4.*
 # Image Manipulation
 numpy == 1.26.*
 opencv-python-headless == 4.11.0.*
 opencv-contrib-python == 4.11.0.*
-scipy == 1.17.*
+scipy == 1.16.*
 # OpenVino & ONNX
 openvino == 2025.4.*
-onnxruntime == 1.30.*
+onnxruntime == 1.22.*
 # Embeddings
-transformers == 5.19.*
+transformers == 4.45.*
 # Generative AI
-google-genai == 2.29.*
+google-genai == 1.58.*
 ollama == 0.6.*
-openai == 3.26.*
+openai == 1.65.*
 # push notifications
 py-vapid == 1.9.4
-pywebpush == 2.5.*
+pywebpush == 2.0.*
 # alpr
 pyclipper == 1.4.*
-shapely == 2.2.*
+shapely == 2.0.*
-rapidfuzz==3.14.*
+rapidfuzz==3.12.*
 # HailoRT
-argcomplete==3.7.*
+argcomplete==2.0.*
-contextlib2==21.6.*
+contextlib2==0.6.*
-future==1.0.*
+future==0.18.*
 netaddr==1.3.*
 netifaces==0.10.*
 prometheus-client == 0.26.*
@@ -71,6 +71,6 @@ tflite_runtime @ https://github.com/feranick/TFlite-builds/releases/download/v2.
 sherpa-onnx==1.13.*
 faster-whisper==1.2.*
 librosa==0.11.*
-soundfile==0.14.*
+soundfile==0.13.*
 # Memory profiling
 memray == 1.20.*
@@ -37,7 +37,6 @@ device_globs=(
    "/dev/nvmap"
    "/dev/nvidia*"
    "/dev/memx*"
    "/dev/dxrt*"
 )
 IFS=',' read -ra extra_globs <<< "${DEVICE_ACL_PATHS:-}"
@@ -8,14 +8,13 @@ from typing import Any
 from ruamel.yaml import YAML
 sys.path.insert(0, "/opt/frigate")
 from frigate.config.env import apply_config_env_vars, substitute_frigate_vars
 from frigate.const import (
    BIRDSEYE_PIPE,
    LIBAVFORMAT_VERSION_MAJOR,
 )
 from frigate.ffmpeg_presets import parse_preset_hardware_acceleration_encode
 from frigate.util.config import find_config_file, resolve_ffmpeg_path
 from frigate.util.env import apply_config_env_vars, substitute_frigate_vars
 from frigate.util.live_streams import raw_transcode_streams
 from frigate.util.services import (
    is_go2rtc_arbitrary_exec_allowed,
    is_restricted_go2rtc_source,
@@ -175,17 +174,6 @@ for name in list(go2rtc_config.get("streams", {})):
            del go2rtc_config["streams"][name]
            continue
 # add transcoded live streams; a user stream with the same name wins here and
 # fails Frigate's config validation
 transcoded_streams = raw_transcode_streams(config)
 if transcoded_streams:
    if go2rtc_config.get("streams") is None:
        go2rtc_config["streams"] = {}
    for name, source in transcoded_streams.items():
        go2rtc_config["streams"].setdefault(name, source)
 # add birdseye restream stream if enabled
 if config.get("birdseye", {}).get("restream", False):
    birdseye: dict[str, Any] = config.get("birdseye")
@@ -14,4 +14,4 @@ nvidia-nccl-cu12==2.26.2.post1; platform_machine == 'x86_64'
 nvidia-nvjitlink-cu12==12.8.93; platform_machine == 'x86_64'
 onnx==1.16.*; platform_machine == 'x86_64'
 onnxruntime-gpu==1.24.*; platform_machine == 'x86_64'
-protobuf==7.36.*; platform_machine == 'x86_64'
+protobuf==3.20.3; platform_machine == 'x86_64'
@@ -824,38 +824,6 @@ cpu:
        models:
          - devices:
              - cpu:3
 xdna2:
  title: AMD XDNA2
  models:
    - key: yolov9
      label: YOLOv9
      recommended: true
      download: |-
        Prepare the model using the frigate-xdna setup instructions linked above. For local YOLO models, Frigate must have access to the same ONNX file bytes as the sidecar. The example below uses YOLOv9-C at 320x320. Frigate+ models may instead use the same `plus://MODEL_ID` in Frigate and the sidecar.
      ui: |-
        Navigate to **Settings > System > Detection models** and add a model. The ZMQ endpoint is not reported by the hardware probe, so set `devices` to `zmq:tcp://xdna:5555` in YAML. Then, on the same model, open the **Custom Model** tab and configure:
        | Field                                    | Value                                      |
        | ---------------------------------------- | ------------------------------------------ |
        | **Custom object detector model path**    | `/config/models/yolov9-c-320.onnx`       |
        | **Label map for custom object detector** | `/labelmap/coco-80.txt`                   |
        | **Object detection model input width**   | `320`                                    |
        | **Object detection model input height**  | `320`                                    |
        | **Model Input Pixel Color Format**       | `rgb` (Frigate's default value)          |
        | **Model Input Tensor Shape**             | `nchw`                                   |
        | **Model Input D Type**                   | `float`                                  |
        | **Object Detection Model Type**          | `yolo-generic`                           |
      yaml: |-
        models:
          - devices:
              - zmq:tcp://xdna:5555
            model_type: yolo-generic
            width: 320
            height: 320
            input_tensor: nchw
            input_dtype: float
            path: /config/models/yolov9-c-320.onnx
            labelmap_path: /labelmap/coco-80.txt
 memryx:
  title: MemryX
  models:
@@ -999,65 +967,6 @@ memryx:
            # The .zip file must contain:
            # ├── ssdlite_mobilenet.dfp          (a file ending with .dfp)
            # └── ssdlite_mobilenet_post.onnx    (optional; only if the model includes a cropped post-processing network)
 deepx:
  title: DEEPX NPU
  models:
    - key: yolo
      label: YOLO
      recommended: true
      download: No model is bundled with Frigate. Download a pre-compiled YOLO `.dxnn` model from the [DEEPX ModelZoo](https://developer.deepx.ai/modelzoo) or compile your own with DX-COM, then bind-mount it into the container and point the model's `path` at it. The recommended model is `yolox-s_640x640_ppu.dxnn`. Its Post-Processing Unit (PPU) compile moves candidate selection onto the NPU, which makes it the fastest ModelZoo model measured through Frigate (about 13 ms on a DX-M1). The output layout is read from the compiled model, so anchor-based, anchor-free, NMS-in-head and PPU models (anchor-based or anchor-free) all need no extra configuration; prefer a `PPU` variant whenever the ModelZoo offers one. PPU models must be compiled with DX-COM 2.4.0 or later, which writes the head layout Frigate reads into the file.
      ui: |-
        Navigate to **Settings > System > Detection models** and select **DEEPX NPU** from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
        | Field                                    | Value                                                |
        | ---------------------------------------- | ---------------------------------------------------- |
        | **Custom object detector model path**    | `/config/model_cache/deepx/yolox-s_640x640_ppu.dxnn` |
        | **Label map for custom object detector** | `/labelmap/coco-80.txt`                              |
        | **Object detection model input width**   | `640`                                                |
        | **Object detection model input height**  | `640`                                                |
        | **Model Input Pixel Color Format**       | `rgb` (Frigate's default value)                      |
        | **Model Input Tensor Shape**             | `nhwc` (Frigate's default value)                     |
        | **Model Input D Type**                   | `int` (Frigate's default value)                      |
        | **Object Detection Model Type**          | `yolo-generic`                                       |
        Quantization is baked into the compiled model, so no normalization is applied on the host and the input defaults do not need to be overridden.
      yaml: |-
        models:
          - devices:
              - deepx:PCIe:0
            path: /config/model_cache/deepx/yolox-s_640x640_ppu.dxnn
            labelmap_path: /labelmap/coco-80.txt
            model_type: yolo-generic
            width: 640
            height: 640
    - key: yolox
      label: YOLOX
      recommended: false
      download: No model is bundled with Frigate. Download a pre-compiled YOLOX `.dxnn` model from the [DEEPX ModelZoo](https://developer.deepx.ai/modelzoo), then bind-mount it into the container and point the model's `path` at it. The `_ppu` variant is faster and also works with the `yolo-generic` model type; the plain export needs `yolox` so its raw head is decoded.
      ui: |-
        Navigate to **Settings > System > Detection models** and select **DEEPX NPU** from the **Hardware** dropdown. Then, on the same model, open the **Custom Model** tab and configure:
        | Field                                    | Value                                    |
        | ---------------------------------------- | ---------------------------------------- |
        | **Custom object detector model path**    | `/config/model_cache/deepx/yolox-s_640x640.dxnn`|
        | **Label map for custom object detector** | `/labelmap/coco-80.txt`                  |
        | **Object detection model input width**   | `640`                                    |
        | **Object detection model input height**  | `640`                                    |
        | **Model Input Pixel Color Format**       | `rgb` (Frigate's default value)          |
        | **Model Input Tensor Shape**             | `nhwc` (Frigate's default value)         |
        | **Model Input D Type**                   | `int` (Frigate's default value)          |
        | **Object Detection Model Type**          | `yolox`                                  |
        The width and height must match the resolution the `.dxnn` file was compiled for.
      yaml: |-
        models:
          - devices:
              - deepx:PCIe:0
            path: /config/model_cache/deepx/yolox-s_640x640.dxnn
            labelmap_path: /labelmap/coco-80.txt
            model_type: yolox
            width: 640
            height: 640
 tensorrt:
  title: TensorRT
  models:
@@ -152,10 +152,9 @@ auth:
 models:
    # Optional: the camera environment this model is for (default: shown below)
    # Cameras select a model by setting detect -> scene to a matching value, and
-    # the model with a scene of default is used by any camera that does not set one.
+    # a model with a scene of all is used by any camera that does not set one.
-    # Any name made up of letters, numbers, _ and - is valid, such as thermal.
+    # Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
-    # Models that use the same model file are combined into one model.
+  - scene: all
  - scene: default
    # Required: hardware this model runs on, as <detector> or <detector>:<device>
    # See https://docs.frigate.video/configuration/object_detectors for the
    # detectors available and the devices each one accepts. All of a model's
@@ -294,9 +293,9 @@ ffmpeg:
    # Optional: output args for detect streams (default: shown below)
    detect: -threads 2 -f rawvideo -pix_fmt yuv420p
    # Optional: output args for record streams (default: shown below)
-    record: preset-record-generic-audio-aac
+    record: preset-record-generic
    # Optional: output args for sub stream record streams (default: the record output args above)
-    # record_sub: preset-record-generic-audio-aac
+    # record_sub: preset-record-generic
  # Optional: Time in seconds to wait before ffmpeg retries connecting to the camera. (default: shown below)
  # If set too low, frigate will retry a connection to the camera's stream too frequently, using up the limited streams some cameras can allow at once
  # If set too high, then if a ffmpeg crash or camera stream timeout occurs, you could potentially lose up to a maximum of retry_interval second(s) of footage
@@ -317,9 +316,9 @@ detect:
  # Optional: height of the frame for the input with the detect role (default: use native stream resolution)
  height: 720
  # Optional: the environment this camera looks at, which picks the model it runs on
-  # (default: the model with a scene of default)
+  # (default: the model with a scene of all)
-  # Must match the scene of a configured model
+  # Valid values are all, indoor, outdoor, indoor_thermal, outdoor_thermal
-  scene: thermal
+  scene: outdoor
  # Optional: desired fps for your camera for the input with the detect role (default: shown below)
  # NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
  fps: 5
@@ -571,8 +570,6 @@ notifications:
  enabled: False
  # Optional: Email for push service to reach out to
  # NOTE: This is required to use notifications
  # NOTE: Email can be specified with an environment variable or docker secrets that must begin with 'FRIGATE_'.
  #       e.g. email: '{FRIGATE_NOTIFICATION_EMAIL}'
  email: "admin@example.com"
  # Optional: Cooldown time for notifications in seconds (default: shown below)
  cooldown: 0
@@ -803,7 +800,7 @@ lpr:
 # to Google or OpenAI's LLMs to generate descriptions. GenAI features can be configured at
 # the camera level to enhance privacy for indoor cameras.
 # NOTE: genai is a map of named providers. Each key is a name you choose for the provider,
-#       and each role (chat, descriptions, embeddings, transcribe) may be assigned to exactly one provider.
+#       and each role (chat, descriptions, embeddings) may be assigned to exactly one provider.
 genai:
  # Required: name of the provider (chosen by you, used to reference it elsewhere)
  my_provider:
@@ -816,13 +813,11 @@ genai:
    # Required: The model to use with the provider.
    model: gemini-1.5-flash
    # Optional: Roles this provider handles (default: shown below)
-    # Each role (chat, descriptions, embeddings, transcribe) must be assigned to exactly
+    # Each role (chat, descriptions, embeddings) must be assigned to exactly one provider.
    # one provider.
    roles:
      - chat
      - descriptions
      - embeddings
      - transcribe
    # Optional additional args to pass to the GenAI Provider (default: None)
    provider_options:
      keep_alive: -1
@@ -835,19 +830,13 @@ genai:
 audio_transcription:
  # Optional: Enable live and speech event audio transcription (default: shown below)
  enabled: False
  # Optional: The transcription backend (default: shown below)
  # Either 'whisper' for Frigate's built-in local models, or the name of a genai
  # provider that has 'transcribe' in its roles. device and model_size are ignored
  # when a genai provider is named.
  model: whisper
  # Optional: The device to run the models on for live transcription. (default: shown below)
  device: CPU
  # Optional: Set the model size used for live transcription. (default: shown below)
  model_size: small
  # Optional: Set the language used for transcription translation. (default: shown below)
-  # Use 'auto' to let the model detect the language, or a language code from
+  # List of language codes: https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10
-  # https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10
+  language: en
  language: auto
 # Optional: Configuration for classification models
 classification:
@@ -903,21 +892,6 @@ live:
  streams:
    main_stream: main_stream_name
    sub_stream: sub_stream_name
  # Optional: Lower-quality live streams transcoded by go2rtc while someone is watching.
  # NOTE: Set at the camera level only.
  transcode:
    # Optional: Enable transcoded streams (default: shown below)
    enabled: False
    # Optional: go2rtc stream to transcode (default: the first live stream)
    source: main_stream_name
    # Optional: One transcoded stream per quality (default: shown below)
    qualities:
      - height: 720
        bitrate: 1200
      - height: 480
        bitrate: 500
      - height: 360
        bitrate: 250
  # Optional: Set the height of the jsmpeg stream. (default: 720)
  # This must be less than or equal to the height of the detect stream. Lower resolutions
  # reduce bandwidth required for viewing the jsmpeg stream. Width is computed to match known aspect ratio.
@@ -204,7 +204,7 @@ Frequently-heard labels like `speech` can generate a lot of events, and each eve
 ### Audio Transcription
-Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI's open-source Whisper models via `faster-whisper`, and can alternatively offload transcription to a [GenAI provider](#genai-provider). The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service. Automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.
+Frigate supports fully local audio transcription using either `sherpa-onnx` or OpenAI's open-source Whisper models via `faster-whisper`. The goal of this feature is to support Semantic Search for `speech` audio events. Frigate is not intended to act as a continuous, fully-automatic speech transcription service. Automatically transcribing all speech (or queuing many audio events for transcription) requires substantial CPU (or GPU) resources and is impractical on most systems. For this reason, transcriptions for events are initiated manually from the UI or the API rather than being run continuously in the background.
 :::info
@@ -224,7 +224,6 @@ To enable transcription, configure it globally and optionally disable for specif
 **Global:** Navigate to <NavPath path="Settings > Enrichments > Audio transcription" />.
 - Set **Enable audio transcription** to on
 - Set **Audio transcription model or GenAI provider name** to `whisper` for Frigate's built-in local models, or to the name of a GenAI provider
 - Set **Transcription device** to the desired device
 - Set **Model size** to the desired size
@@ -236,7 +235,6 @@ To enable transcription, configure it globally and optionally disable for specif
 ```yaml
 audio_transcription:
  enabled: True
  model: whisper
  device: ...
  model_size: ...
 ```
@@ -265,88 +263,20 @@ The optional config parameters that can be set at the global level include:
 - **`enabled`**: Enable or disable the audio transcription feature.
  - Default: `False`
  - It is recommended to only configure the features at the global level, and enable it at the individual camera level.
 - **`model`**: The transcription backend.
  - Default: `whisper`
  - `whisper` uses Frigate's built-in local models, described by `device` and `model_size` below.
  - Any other value must name a key in your `genai` config whose entry has `transcribe` in its `roles`. See [GenAI Provider](#genai-provider).
 - **`device`**: Device to use to run transcription and translation models.
  - Default: `CPU`
  - This can be `CPU` or `GPU`. The `sherpa-onnx` models are lightweight and run on the CPU only. The `whisper` models can run on GPU but are only supported on CUDA hardware.
  - Ignored when `model` names a GenAI provider.
 - **`model_size`**: The size of the model used for live transcription.
  - Default: `small`
  - This can be `small` or `large`. The `small` setting uses `sherpa-onnx` models that are fast, lightweight, and always run on the CPU but are not as accurate as the `whisper` model.
-  - This config option applies to **live transcription only**. With `model: whisper`, recorded `speech` events always use a different `whisper` model (and can be accelerated for CUDA hardware if available with `device: GPU`).
+  - This config option applies to **live transcription only**. Recorded `speech` events will always use a different `whisper` model (and can be accelerated for CUDA hardware if available with `device: GPU`).
-  - Ignored when `model` names a GenAI provider.
+- **`language`**: Defines the language used by `whisper` to translate `speech` audio events (and live audio only if using the `large` model).
- **`language`**: Defines the language used to transcribe and translate `speech` audio events (and live audio only if using the `large` model or a GenAI provider).
+  - Default: `en`
-  - Default: `auto`
+  - You must use a valid [language code](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10).
  - `auto` lets the model detect the language itself, which most models do well. Set an explicit language only if detection is picking the wrong one.
  - Otherwise you must use a valid [language code](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10).
  - Transcriptions for `speech` events are translated.
  - Live audio is translated only if you are using the `large` model. The `small` `sherpa-onnx` model is English-only.
-The only field that is valid at the camera level is `enabled`. In particular `model` is global only: the transcription backend is a process-wide resource shared by every camera.
+The only field that is valid at the camera level is `enabled`.
 #### GenAI Provider
 Frigate can send audio to a GenAI provider for transcription when that provider has the `transcribe` role. This is useful if you already run a GenAI provider, or if you do not have the CPU/GPU headroom for a local whisper model. See [Provider support](/configuration/genai/genai_config#provider-support) for which providers can serve this role. The model must accept audio: either a dedicated ASR model such as Qwen3-ASR, or a general multimodal model that accepts audio.
 To use a GenAI provider for audio transcription:
 1. Configure a GenAI provider with `transcribe` in its `roles`.
 2. Set the audio transcription model to that GenAI config key (e.g. `whisper_cloud`).
 <ConfigTabs>
 <TabItem value="ui">
 Navigate to <NavPath path="Settings > Enrichments > Audio transcription" />.
 | Field                                                | Description                                                                                              |
 | ---------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
 | **Audio transcription model or GenAI provider name** | Set to the GenAI config key (e.g. `whisper_cloud`) to use a configured GenAI provider for transcription |
 The GenAI provider must also be configured with the `transcribe` role under <NavPath path="Settings > Enrichments > Generative AI" />.
 </TabItem>
 <TabItem value="yaml">
 ```yaml
 genai:
  whisper_cloud:
    provider: openai
    api_key: your-api-key
    model: gpt-transcribe
    roles:
      - transcribe
 audio_transcription:
  enabled: True
  model: whisper_cloud
  language: en
 ```
 </TabItem>
 </ConfigTabs>
 :::warning
 **Give `transcribe` its own `genai` entry.** A `genai` entry has a single `model` string that is shared by every role it holds, so `roles: [descriptions, transcribe]` would send the same model name to both the chat endpoint and the transcription endpoint. Transcription models and chat models are almost never the same model, so define a dedicated entry as shown above.
 :::
 :::warning
 **Live transcription against a metered provider is billed continuously.** In live mode Frigate uploads an overlapping ~2 second window of audio roughly once per second, per camera, for as long as audio stays above that camera's `audio.min_volume`. Windows below that threshold are never uploaded, which is what keeps a quiet camera near zero requests, but a camera pointed at a busy street will keep sending.
 Three things keep this opt-in: `transcribe` is not one of the default roles, live transcription is off by default, and the volume gate suppresses silence. Transcription of recorded `speech` events is unaffected - it remains a manual, one-request-per-event action.
 :::
 `device` and `model_size` have no effect on this path and no local model is ever downloaded.
 `language` defaults to `auto`, which sends no language hint and lets the model detect it. Most audio models detect language well, so leave it on `auto` unless detection is picking the wrong one.
 When set explicitly, it is sent as the transcription endpoint's native `language` parameter for OpenAI, Azure, and llama.cpp, and as part of the prompt for Gemini. This matters for dedicated ASR models such as Qwen3-ASR: they read the prompt as contextual biasing rather than as an instruction, so a language named in the prompt is ignored, while the endpoint parameter is honored.
 #### Live transcription
@@ -362,8 +292,6 @@ Results can be error-prone due to a number of factors, including:
 For speech sources close to the camera with minimal background noise, use the `small` model.
 A [GenAI provider](#genai-provider) is generally the most accurate option for live transcription, at the cost of a network round trip per window. That round trip has to stay under about a second to keep up with the audio; if it does not, Frigate drops the oldest buffered audio rather than letting the backlog grow.
 If you have CUDA hardware, you can experiment with the `large` `whisper` model on GPU. Performance is not quite as fast as the `sherpa-onnx` `small` model, but live transcription is far more accurate. Using the `large` model with CPU will likely be too slow for real-time transcription.
 #### Transcription and translation of `speech` audio events
@@ -380,7 +308,7 @@ Only one `speech` event may be transcribed at a time. Frigate does not automatic
 :::
-With `model: whisper`, recorded `speech` events 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. With a [GenAI provider](#genai-provider), the recorded clip is sent to the provider instead and no local model is used.
+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
@@ -169,10 +169,10 @@ The [Add Camera Wizard](cameras.md#adding-a-camera-with-the-add-camera-wizard) i
 1. Click **Add Camera** in <NavPath path="Settings > Global configuration > Camera management" />.
 2. Choose **Manual selection** as the stream detection method and select **Reolink** as the camera brand.
-3. The wizard queries the camera and automatically uses an http-flv stream for cameras 5MP and lower. For higher resolution cameras, it tries the http-flv stream first and falls back to RTSP when the camera does not support it.
+3. The wizard queries the camera and automatically uses an http-flv stream for cameras 5MP and lower, or an RTSP stream for higher resolution cameras.
-4. The wizard turns on **Use stream compatibility mode** for the http-flv stream it selects. Enable it for any other http-flv stream you add, such as the sub stream.
+4. In the validation step, enable **Use stream compatibility mode** for http-flv streams when the wizard recommends it.
-If you use the **Probe camera** method instead, the discovered stream URLs will be RTSP. For Reolink cameras 5MP and lower, where http-flv is recommended, the wizard will show a warning in the validation step.
+If you use the **Probe camera** method instead, the discovered stream URLs will be RTSP. For Reolink cameras where http-flv is recommended, the wizard will show a warning in the validation step.
 The wizard covers standard single-camera setups. For two way talk, cameras connected through a Reolink NVR, or audio transcoding for WebRTC live view, configure the camera manually as shown below.
@@ -85,14 +85,6 @@ An optional config, `save_attempts`, can be set as a key under the model name. T
 </TabItem>
 </ConfigTabs>
 ## Review items
 When a model's state changes while its camera has an active review item, the change is recorded on that review item. This includes changes in the few seconds before the item starts, such as a garage door opening just before the car is detected. State changes never create or extend review items on their own, and the first state reported after Frigate starts is not recorded as a change.
 Recorded changes appear in the review item's data as `classification_state_changes` (see the [`frigate/reviews`](/integrations/mqtt#frigatereviews) MQTT topic) and are passed to [GenAI review summaries](/configuration/genai/genai_review) as facts, so a description can note that a gate was opened during the activity.
 Change times are most accurate with `motion: true`. A model that only runs on an `interval` notices a change at its next run, so the change may be recorded late or attached to a later review item.
 ## Training the model
 Creating and training the model is done within the Frigate UI using the `Classification` page. The process consists of three steps:
@@ -17,19 +17,17 @@ Hardware acceleration arguments tell FFmpeg to decode your camera's video stream
 See [the hardware acceleration docs](/configuration/hardware_acceleration_video.md) for details on setting up hardware acceleration for your GPU / iGPU, then select the preset that matches your hardware.
-| Preset (YAML config)      | UI Label                | Usage                                         | Notes                                                           |
+| Preset (YAML config)  | UI Label                | Usage                             | Notes                                                           |
-| ------------------------- | ----------------------- | --------------------------------------------- | --------------------------------------------------------------- |
+| --------------------- | ----------------------- | --------------------------------- | --------------------------------------------------------------- |
-| preset-rpi-64-h264        | Raspberry Pi (H.264)    | 64-bit Raspberry Pi, H.264 stream             |                                                                 |
+| preset-rpi-64-h264    | Raspberry Pi (H.264)    | 64-bit Raspberry Pi, H.264 stream |                                                                 |
-| preset-rpi-64-h265        | Raspberry Pi (H.265)    | 64-bit Raspberry Pi, H.265 stream             |                                                                 |
+| preset-rpi-64-h265    | Raspberry Pi (H.265)    | 64-bit Raspberry Pi, H.265 stream |                                                                 |
-| preset-apple-silicon-h264 | Apple Silicon (H.264)   | Apple Silicon Mac under lighter, H.264 stream | Needs the `lighter.sh/video` device                             |
+| preset-vaapi          | VAAPI (Intel/AMD GPU)   | Intel or AMD GPU via VAAPI        | Check the hwaccel docs to ensure the correct driver is selected |
-| preset-apple-silicon-h265 | Apple Silicon (H.265)   | Apple Silicon Mac under lighter, H.265 stream | Needs the `lighter.sh/video` device                             |
+| preset-intel-qsv-h264 | Intel QuickSync (H.264) | Intel QuickSync, H.264 stream     | If you have issues, use the VAAPI preset instead                |
-| preset-vaapi              | VAAPI (Intel/AMD GPU)   | Intel or AMD GPU via VAAPI                    | Check the hwaccel docs to ensure the correct driver is selected |
+| preset-intel-qsv-h265 | Intel QuickSync (H.265) | Intel QuickSync, H.265 stream     | If you have issues, use the VAAPI preset instead                |
-| preset-intel-qsv-h264     | Intel QuickSync (H.264) | Intel QuickSync, H.264 stream                 | If you have issues, use the VAAPI preset instead                |
+| preset-nvidia         | NVIDIA GPU              | NVIDIA GPU                        |                                                                 |
-| preset-intel-qsv-h265     | Intel QuickSync (H.265) | Intel QuickSync, H.265 stream                 | If you have issues, use the VAAPI preset instead                |
+| preset-jetson-h264    | NVIDIA Jetson (H.264)   | NVIDIA Jetson, H.264 stream       |                                                                 |
-| preset-nvidia             | NVIDIA GPU              | NVIDIA GPU                                    |                                                                 |
+| preset-jetson-h265    | NVIDIA Jetson (H.265)   | NVIDIA Jetson, H.265 stream       |                                                                 |
-| preset-jetson-h264        | NVIDIA Jetson (H.264)   | NVIDIA Jetson, H.264 stream                   |                                                                 |
+| preset-rkmpp          | Rockchip RKMPP          | Rockchip MPP                      | Use an image with the `-rk` suffix and run in privileged mode   |
 | preset-jetson-h265        | NVIDIA Jetson (H.265)   | NVIDIA Jetson, H.265 stream                   |                                                                 |
 | preset-rkmpp              | Rockchip RKMPP          | Rockchip MPP                                  | Use an image with the `-rk` suffix and run in privileged mode   |
 <ConfigTabs>
 <TabItem value="ui">
@@ -43,19 +43,7 @@ genai:
 The examples on this page all use `my_provider`, but the name is arbitrary and is only used to reference the provider elsewhere in the config (for example, `semantic_search.model`).
-Each provider handles one or more **roles**: `chat`, `descriptions`, `embeddings`, and `transcribe`. A provider handles the first three by default; `transcribe` must always be listed explicitly. Each role may be assigned to exactly one provider. Define a single provider if you want it to do everything, or split the roles across several providers using the `roles` option. Not every provider supports every role; see [Provider support](#provider-support).
+Each provider handles one or more **roles**: `chat`, `descriptions`, and `embeddings`. A provider handles all three by default, and each role may be assigned to exactly one provider. Define a single provider if you want it to do everything, or split the roles across several providers using the `roles` option.
 ### Provider support
 | Provider                      | Descriptions | Chat | Embeddings | Transcription |
 | ----------------------------- | :----------: | :--: | :--------: | :-----------: |
 | llama.cpp (`llamacpp`)        |      ✅      |  ✅  |     ✅     |      ✅       |
 | Ollama (`ollama`)             |      ✅      |  ✅  |     ✅     |      ❌       |
 | OpenAI (`openai`)             |      ✅      |  ✅  |     ❌     |      ✅       |
 | Azure OpenAI (`azure_openai`) |      ✅      |  ✅  |     ❌     |      ✅       |
 | Google Gemini (`gemini`)      |      ✅      |  ✅  |     ❌     |      ✅       |
 A ✅ means Frigate can use the provider for that feature. The configured model must also support it: a vision model for descriptions and chat, a multimodal embedding model for embeddings (see [Embedding models](#embedding-models)), and an audio-capable model for transcription. Some features also need extra provider setup, covered in each provider's section below. OpenAI-compatible servers use the `openai` provider, so they follow the OpenAI row.
 If the provider you choose requires an API key, you may either directly paste it in your configuration, or store it in an environment variable prefixed with `FRIGATE_`.
@@ -75,31 +63,19 @@ Running Generative AI models on CPU is not recommended, as high inference times
 You must use a vision-capable model with Frigate. The following models are recommended for local deployment of the `descriptions` and `chat` roles:
-| Model               | Review [frame mode](/configuration/genai/genai_review#frame-mode) | Notes                                                                                                                                                                |
+| Model               | Notes                                                                                                                                                                |
-| ------------------- | --------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `qwen3-vl`          | `frames`                                                              | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object. Follows a sequence of frames on its own.     |
+| `qwen3-vl`          | Strong visual and situational understanding, enhanced ability to identify smaller objects and interactions with object.                                              |
-| `qwen3.6`/`qwen3.8` | `frames`                                                              | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
+| `qwen3.6`/`qwen3.8` | Strong situational understanding, but missing DeepStack from qwen3-vl leading to worse performance for identifying objects in people's hand and other small details. |
-| `gemma4`            | `annotated_frames`                                                    | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action. Loses track of activity that repeats or reverses, so it benefits from annotated frames. |
+| `gemma4`            | Strong situational understanding, sometimes resorts to more vague terms like 'interacts' instead of assigning a specific action.                                     |
 #### Embedding models
 The `embeddings` role needs a different kind of model. Text queries are matched against the stored image embeddings, so the model must be trained to place images and text into the same vector space. A chat or description model will still return vectors when asked, but those vectors are not trained for retrieval and text searches will return poor matches with no error to indicate why.
-| Model                | Notes                                                                                                                                                                  |
+| Model                | Notes                                                                                                                                                               |
-| -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `embeddinggemma-2`   | Multimodal embeddings for [Semantic Search](/configuration/semantic_search#genai-provider). Strong semantic search accuracy with efficient inference on a small model. |
+| `qwen3-vl-embedding` | Multimodal embeddings for [Semantic Search](/configuration/semantic_search#genai-provider). Must be served by llama.cpp started with `--embeddings` and `--mmproj`. |
 | `qwen3-vl-embedding` | Multimodal embeddings for [Semantic Search](/configuration/semantic_search#genai-provider). Good performance, large model that requires strong hardware for inference. |
 #### Transcription models
 The `transcribe` role needs a model that accepts audio input. A text-only or vision-only model cannot serve this role. The following are recommended for local deployment of the `transcribe` role:
 | Model       | Notes                                                                                                                                                                                                      |
 | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
 | `qwen3-asr` | Dedicated speech recognition model covering 30 languages, and the better choice for transcription quality. It only transcribes, so it cannot be shared with the `descriptions` or `chat` roles.            |
 | `gemma4`    | General multimodal model that accepts audio as well as images, so one served model can cover `transcribe` alongside the other roles. Transcript quality is below `qwen3-asr`, particularly on noisy audio. |
 Both must be served by llama.cpp started with the matching audio `--mmproj`. llama.cpp only reports audio support when an audio projector is loaded. Without it Frigate sees the model as text-only and the `transcribe` role is unavailable in the UI. Frigate transcribes through the server's `/v1/audio/transcriptions` route, which llama.cpp serves for any audio-capable model.
 :::info
@@ -165,10 +141,6 @@ genai:
 Frigate queries the llama.cpp server for the model's context size at startup and logs it along with the other detected capabilities. If `context_size` is set in `provider_options`, that value is always used instead, even when the server reports its own.
 #### Embeddings
 To serve the `embeddings` role for [Semantic Search](/configuration/semantic_search#genai-provider), start the llama.cpp server with `--embeddings`, plus `--mmproj` for image support. See the [llama.cpp server documentation](https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md) for details.
 ### Ollama
 [Ollama](https://ollama.com/) allows you to self-host large language models and keep everything running locally. It is highly recommended to host this server on a machine with an Nvidia graphics card, or on a Apple silicon Mac for best performance.
@@ -214,10 +186,6 @@ genai:
 </TabItem>
 </ConfigTabs>
 #### Embeddings
 Ollama can serve the `embeddings` role for [Semantic Search](/configuration/semantic_search#genai-provider). Embedding images requires Ollama 0.40.1 or newer and an embedding model with a vision encoder, such as `embeddinggemma-2:440m`. For a saved provider, the UI hides the role unless Ollama reports its model as an embedding model. Use a separate provider entry for the embedding model rather than adding the role to a vision chat model.
 ### OpenAI-Compatible
 Frigate supports any provider that implements the OpenAI API standard. This includes self-hosted solutions like [vLLM](https://docs.vllm.ai/), [LocalAI](https://localai.io/), and other OpenAI-compatible servers.
@@ -192,45 +192,6 @@ review:
 </TabItem>
 </ConfigTabs>
 ### Frame Mode
 Review items are sent to the model as a sequence of still frames. Some models follow that sequence well on their own; others lose track of activity that repeats or reverses, and describe a single trip when the subject actually made several. The `frame_mode` option controls how those frames are presented.
 - `frames` (default): the prompt followed by the frames, exactly as earlier versions of Frigate sent them.
 - `annotated_frames`: each frame is preceded by its frame number and elapsed time, along with notes describing what the object tracker recorded at that moment, such as an object being first detected, starting to move, turning around, stopping, or no longer being detected.
 The notes come from tracking data rather than from the images, so they describe activity the model may not have picked up on its own. In testing with a person carrying three waste bins to the curb one at a time, `gemma4` described a single trip on every attempt with `frames`, and consistently described multiple trips with `annotated_frames`. Models that already handle these sequences well, such as the `qwen3-vl` family, gain little and should stay on `frames`.
 Changes reported by [state classification](/configuration/custom_classification/state_classification#review-items) models during the review item are listed in the prompt in both modes. `annotated_frames` also notes each change before the frame where it happened.
 Annotated mode also caps the number of frames, since the notes already establish the order of events and extra near-duplicate frames tend to crowd out the middle of a clip. Longer review items are sampled more sparsely as a result, and typically use fewer tokens than `frames` mode for the same item.
 :::note
 Annotated mode needs tracking data for the review item. If none is available, Frigate falls back to sending plain frames for that item.
 :::
 <ConfigTabs>
 <TabItem value="ui">
 Navigate to <NavPath path="Settings > Global configuration > Review" />.
 - Set **GenAI config > Frame mode** to the desired mode (e.g., `annotated_frames`)
 </TabItem>
 <TabItem value="yaml">
 ```yaml {4}
 review:
  genai:
    enabled: true
    frame_mode: annotated_frames
 ```
 </TabItem>
 </ConfigTabs>
 ### Response Style
 Different models respond to the built-in prompt with very different writing styles: some produce natural narration while others sound short and mechanical. The `response_style` option selects a writing style preset that rewords the prompt's instructions for the user-facing fields (the title, short summary, and scene description). Presets replace those instructions rather than adding extra ones, so the model never receives competing style directions.
@@ -43,10 +43,6 @@ Frigate supports presets for optimal hardware accelerated video decoding:
 - [RKNN](#rockchip-platform): Frigate can utilize the media engine in RockChip SOCs to accelerate video decoding.
 **Apple Silicon Mac** <CommunityBadge />
 - [lighter](#apple-silicon-mac-lighter): Frigate can utilize the media engine in Apple Silicon Macs to accelerate video decoding, when running under the lighter container runtime.
 **Other Hardware**
 Depending on your system, these presets may not be compatible, and you may need to use manual hwaccel args to take advantage of your hardware. More information on hardware accelerated decoding for ffmpeg can be found here: https://trac.ffmpeg.org/wiki/HWAccelIntro
@@ -537,35 +533,3 @@ output_args:
 Make sure that your SoC supports hardware acceleration for your input stream and your input stream is h264 encoding. For example, if your camera streams with h264 encoding, your SoC must be able to de- and encode with it. If you are unsure whether your SoC meets the requirements, take a look at the datasheet.
 :::
 ## Apple Silicon Mac (lighter)
 [lighter](https://github.com/fieldwork-ai/lighter) is an open-source container runtime for macOS. It gives a container the Mac's media engine as a standard V4L2 decoder, backed by VideoToolbox, so Frigate decodes H.264 and H.265 streams in hardware with the ffmpeg it already ships. It works on M1 and newer Macs with lighter 0.9.2 or newer.
 Give the container the video device. With Docker Compose:
 ```yaml {4-5}
 services:
  frigate:
    ...
    devices:
      - lighter.sh/video=all
 ```
 Or with `docker run`, add `--device lighter.sh/video=all`.
 Then set the preset for the codec your cameras stream. The decoder is specific to the codec, so if your cameras mix H.264 and H.265, set the preset for the most common codec globally and override it on the other cameras:
 ```yaml
 ffmpeg:
  hwaccel_args: preset-apple-silicon-h264
 cameras:
  garage: # an H.265 camera
    ffmpeg:
      hwaccel_args: preset-apple-silicon-h265
 ```
 The presets decode on the media engine and encode the Birdseye restream and timelapses there too. Scaling to the detect resolution runs on the CPU, as ffmpeg's V4L2 decoders cannot scale.
 lighter can also run object detection on the Mac's Neural Engine; see [Apple Neural Engine (lighter)](object_detectors.md#apple-neural-engine-lighter).
@@ -378,10 +378,10 @@ Navigate to <NavPath path="Settings > Camera configuration > Object detection" /
 Navigate to <NavPath path="Settings > Camera configuration > Objects" />.
-| Field                                                     | Description         |
+| Field                                          | Description         |
-| --------------------------------------------------------- | ------------------- |
+| ---------------------------------------------- | ------------------- |
-| **Objects to track**                                      | Add `license_plate` |
+| **Objects to track**                           | Add `license_plate` |
-| **Object filters > License Plate > Confidence threshold** | Set to `0.7`        |
+| **Object filters > License Plate > Threshold** | Set to `0.7`        |
 Navigate to <NavPath path="Settings > Camera configuration > Motion detection" />.
@@ -92,7 +92,7 @@ go2rtc:
 ### Setting Streams For Live UI
-You can configure Frigate to allow manual selection of the stream you want to view in the Live UI. For example, you may want to view your camera's substream on mobile devices, but the full resolution stream on desktop devices. Setting the streams list will populate a dropdown in the UI's Live view that allows you to choose between the streams. This stream setting is _per device_ and is saved in your browser's local storage. When a camera has more than one stream, the dropdown also offers **Auto**, which is used until you pick a specific stream. Auto starts on the first stream, steps down the list when your connection can't keep up, and steps back up when it recovers. To retry the top stream right away, select **Try highest quality** under the stream picker. List streams from highest to lowest quality, and avoid names that are plain numbers (such as `720`), which the browser sorts ahead of the others. In the UI, drag streams to reorder them, or use **Auto order** to sort them by measured bitrate.
+You can configure Frigate to allow manual selection of the stream you want to view in the Live UI. For example, you may want to view your camera's substream on mobile devices, but the full resolution stream on desktop devices. Setting the streams list will populate a dropdown in the UI's Live view that allows you to choose between the streams. This stream setting is _per device_ and is saved in your browser's local storage.
 Additionally, when creating and editing camera groups in the UI, you can choose the stream you want to use for your camera group's Live dashboard.
@@ -158,26 +158,6 @@ cameras:
 </TabItem>
 </ConfigTabs>
 ### Transcoded streams
 When a camera has no suitable sub stream, Frigate can add lower-quality streams that go2rtc transcodes to H.264 while someone is watching. They appear in the stream list like any other stream, so Auto mode can step down to them. Enable them under <NavPath path="Settings > Camera configuration > Live playback" />, or in YAML:
 ```yaml
 cameras:
  test_cam:
    live:
      transcode:
        enabled: true
        source: test_cam # optional, defaults to the first live stream
        qualities:
          - height: 720
            bitrate: 1200 # kbps
          - height: 480
            bitrate: 500
 ```
 Each quality becomes a go2rtc stream named `<camera>_transcode_<height>p`. go2rtc picks a hardware encoder automatically and falls back to the CPU, which costs CPU for each transcode while it is being watched. Check go2rtc's `api/ffmpeg/hardware` page to see which encoder it found. Using a sub stream as the `source` lowers the cost.
 ### WebRTC extra configuration:
 WebRTC works by creating a TCP or UDP connection on port `8555`. However, it requires additional configuration:
@@ -383,13 +363,6 @@ When your browser runs into problems playing back your camera streams, it will l
    - `Safari reported InvalidStateError.`
    - `Safari reported decoding errors.`
 - **mse-codec**
  - What it means: go2rtc has no codec for this stream that the browser can play.
  - What to try: Pick a stream with a codec the browser supports (H.264 is the most compatible), or use a browser that supports the stream's codec. In Auto, Frigate skips this stream for the rest of the session.
  - Possible console messages from the player code:
    - `mse: streams: codecs not matched: ...`
 - **stalled**
  - What it means: Playback has stalled because the player has fallen too far behind live (extended buffering or no data arriving).
  - What to try: This is usually indicative of the browser struggling to decode too many high-resolution streams at once. Try selecting a lower-bandwidth stream (substream), reduce the number of live streams open, improve the network connection, or lower the camera resolution. Also check your camera's keyframe (I-frame) interval: shorter intervals make playback start and recover faster. You can also try increasing the timeout value in <NavPath path="Settings > UI" /> .
@@ -24,18 +24,15 @@ 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, Mini PCIe, and m.2 formats allowing for a wide range of compatibility with devices.
 - [Hailo](#hailo): The Hailo-8, Hailo-8L and Hailo-8R AI Acceleration modules are available in m.2 format with a HAT for RPi devices, offering a wide range of compatibility with devices.
 - <CommunityBadge /> [MemryX](#memryx-mx3): The MX3 Acceleration module is available in m.2 format, offering broad compatibility across various platforms.
 - <CommunityBadge /> [DEEPX](#deepx-npu): The DEEPX NPU is available in m.2 format and as a HAT+ for the Raspberry Pi 5, offering broad compatibility across various platforms.
 **AMD**
 - [ROCm](#amdrocm-gpu-detector): ROCm can run on AMD Discrete GPUs to provide efficient object detection.
 - [ONNX](#onnx): ROCm will automatically be detected and used as a detector in the `-rocm` Frigate image when a supported ONNX model is configured.
 - <CommunityBadge /> [XDNA2](#amd-xdna2): AMD Ryzen AI / XDNA2 NPUs can run object detection through the community-maintained `frigate-xdna` ZMQ sidecar.
 **Apple Silicon**
 - [Apple Silicon](#apple-silicon-detector): Apple Silicon can run on M1 and newer Apple Silicon devices.
 - <CommunityBadge /> [ONNX](#apple-neural-engine-lighter): the ONNX detector runs on the Neural Engine of M1 and newer Macs when Frigate runs under the lighter container runtime.
 **Intel**
@@ -104,36 +101,32 @@ Coral EdgeTPU and MemryX accelerators can only be opened by one process, so thos
 ### Running more than one model
-Cameras can be split across models by scene, which is useful when some cameras benefit from a differently trained model, such as thermal cameras. Each model declares the `scene` it is for, and each camera picks one with `detect -> scene`:
+Cameras can be split across models by scene, which is useful when indoor and outdoor cameras benefit from differently trained models. Each model declares the `scene` it is for, and each camera picks one with `detect -> scene`:
 ```yaml
 models:
-  - scene: default
+  - scene: outdoor
-    path: plus://your-model
+    path: plus://your-outdoor-model
    devices:
      - edgetpu:pci:0
-  - scene: thermal
+  - scene: indoor
-    path: /config/model_cache/thermal.onnx
+    path: /config/model_cache/indoor.onnx
    model_type: yolo-generic
    devices:
      - openvino:GPU
 cameras:
  driveway:
    ...
  backyard_thermal:
    detect:
-      scene: thermal
+      scene: outdoor
    ...
  hallway:
    detect:
      scene: indoor
    ...
 ```
-A scene is any name made up of letters, numbers, `_`, and `-`. The model with a scene of `default` is used by every camera that does not set one (or sets a scene that no model is configured for), and `default` is used when a model does not declare a scene. Changing a camera's scene requires a restart.
+Available scenes are `all`, `indoor`, `outdoor`, `indoor_thermal`, and `outdoor_thermal`. A model with a scene of `all` is used by every camera that does not set one, and `all` is the default when a model does not declare a scene. Changing a camera's scene requires a restart.
 :::warning
 Scenes are for running **different** models. Do not configure the same model under several scenes to dedicate a detector to specific cameras: every detector of a model already serves every camera using it, and splitting them only leaves some detectors idle while others fall behind. Frigate detects models that use the same model file, even under a different path or file name, combines them into one model with all of their hardware, and logs a warning.
 :::
 ### Choosing a model size
@@ -490,7 +483,7 @@ See [ONNX supported models](#onnx) for supported models, there are some caveats:
 ## ONNX
-ONNX is an open format for building machine learning models, Frigate supports running ONNX models on CPU, OpenVINO, ROCm, TensorRT, and a Mac's Neural Engine. On startup Frigate will automatically try to use a GPU if one is available.
+ONNX is an open format for building machine learning models, Frigate supports running ONNX models on CPU, OpenVINO, ROCm, and TensorRT. On startup Frigate will automatically try to use a GPU if one is available.
 :::info
@@ -506,9 +499,6 @@ If the correct build is used for your GPU then the GPU will be detected and used
  - Nvidia GPUs will automatically be detected and used with the ONNX detector in the `-tensorrt` Frigate image.
  - Jetson devices will automatically be detected and used with the ONNX detector in the `-tensorrt-jp6` Frigate image.
 - **Apple Silicon Mac** <CommunityBadge />
  - The Neural Engine will automatically be detected and used with the ONNX detector when Frigate runs under lighter with its Neural Engine device. See [Apple Neural Engine (lighter)](#apple-neural-engine-lighter).
 :::
 :::tip
@@ -524,22 +514,6 @@ models:
 :::
 ### Apple Neural Engine (lighter) {#apple-neural-engine-lighter}
 [lighter](https://github.com/fieldwork-ai/lighter) is an open-source container runtime for macOS. A container started with its `lighter.sh/ane` device gets an ONNX Runtime execution provider that runs models on the Mac's Neural Engine, and the ONNX detector uses it automatically, with the same models and configuration as on any other hardware. It works on M1 and newer Macs with lighter 0.9.2 or newer.
 Give the Frigate container the Neural Engine device. With Docker Compose:
 ```yaml
 services:
  frigate:
    image: ghcr.io/blakeblackshear/frigate:stable-standard-arm64
    devices:
      - lighter.sh/ane=all
 ```
 Or with `docker run`, add `--device lighter.sh/ane=all`. Frigate then reports the Neural Engine under **Settings > System > Detection models**, and the ONNX detector's model loads on it. lighter can also decode camera streams on the Mac's media engine; see [Video Decoding](hardware_acceleration_video.md#apple-silicon-mac-lighter).
 ### Configuration {#configuration-onnx}
 <ModelConfigDropdown detectorTitle="ONNX" models={objectDetectorsModels.onnx.models} />
@@ -568,28 +542,6 @@ When using CPU detectors, you can add one CPU detector per camera. Adding more d
 # Community Supported Detectors
 ## AMD XDNA2
 AMD Ryzen AI / XDNA2 NPUs can be used through the community-maintained
 [frigate-xdna](https://github.com/mitchins/frigate-xdna) detector sidecar.
 The sidecar runs separately from Frigate and connects using Frigate's ZMQ
 detector interface.
 Currently qualified on **Ryzen AI Max 300 / Strix Halo**. Other XDNA2 devices
 are not yet qualified; XDNA1 is unsupported.
 Follow the frigate-xdna setup instructions to prepare and start the sidecar
 before starting Frigate.
 ### Configuration {#configuration-xdna2}
 Using the detector config below will connect Frigate to the sidecar:
 <ModelConfigDropdown detectorTitle="AMD XDNA2" models={objectDetectorsModels.xdna2.models} />
 The example assumes Frigate and the sidecar share a Docker network where the
 sidecar is named `xdna`.
 ## MemryX MX3
 This detector is available for use with the MemryX MX3 accelerator M.2 module. Frigate supports the MX3 on compatible hardware platforms, providing efficient and high-performance object detection.
@@ -660,63 +612,6 @@ For detailed instructions on compiling models, refer to the [MemryX Compiler](ht
 ---
 ## DEEPX NPU
 This detector is available for use with the DEEPX NPU, both the DX-M1 M.2 module and the DX-M1M on the Sixfab AI HAT+ for the Raspberry Pi 5. The configuration below applies unchanged to either form factor. DEEPX NPU support in Frigate is developed and maintained by [Sixfab](https://sixfab.com).
 See the [installation docs](../frigate/installation.md#deepx-npu) for information on installing the DEEPX kernel driver and runtime on the host and passing the NPU through to the container.
 To run a model on a DEEPX NPU, list a `deepx` device on that model.
 :::info
 The DX-RT Python bindings are not part of the Frigate image. They are downloaded and installed into `/config/.local` the first time a DEEPX device is configured, verified against pinned checksums, and updated automatically when a Frigate release pins a new version. If the container has no internet access, see [Detector runtimes](/frigate/network_requirements#detector-runtimes) for how to provide the files yourself.
 :::
 ### Configuration {#configuration-deepx}
 <ModelConfigDropdown detectorTitle="DEEPX" models={objectDetectorsModels.deepx.models} />
 Frigate does not bundle a model for this detector. Models must be compiled to DEEPX's `.dxnn` format. Two model types are supported:
 - `yolo-generic` for YOLO object detection models, the recommended default. The detector reads the model's output layout from the compiled file, so anchor-based, anchor-free and NMS-in-head models all work with the same configuration, as do models compiled with DEEPX's Post-Processing Unit (PPU) support.
 - `yolox` for YOLOX models compiled without PPU support, whose raw head needs Frigate's YOLOX decoder. A YOLOX model compiled with PPU support works under either `yolox` or `yolo-generic`.
 The quickest way to get one is the [DEEPX ModelZoo](https://developer.deepx.ai/modelzoo), which publishes pre-compiled `.dxnn` files for a range of YOLO object detection models. Download the `.dxnn`, bind-mount it into the container, and point the model's `path` at it. Alternatively, compile your own model with the DX-COM compiler. The recommended starting point is `yolox-s_640x640_ppu.dxnn`, the fastest ModelZoo model measured through Frigate:
 ```yaml
 models:
  - devices:
      - deepx:PCIe:0
    path: /config/model_cache/deepx/yolox-s_640x640_ppu.dxnn
    labelmap_path: /labelmap/coco-80.txt
    model_type: yolo-generic
    width: 640
    height: 640
 ```
 For PPU models, use a `.dxnn` compiled with DX-COM 2.4.0 or later. Frigate reads the PPU head layout the compiler writes into the file and refuses to load a PPU model without it.
 `model_type` must be set to `yolo-generic` or `yolox` to match the model; `yolo-generic` is the recommended default unless the model is a raw YOLOX export. Frigate defaults it to `ssd`, which this detector does not support, so the detector refuses to start on a model that leaves it unset.
 `width` and `height` must match the resolution the model was compiled for. Quantization parameters are baked into the `.dxnn` file at compile time, so no normalization is applied on the host and Frigate's default `input_tensor`, `input_pixel_format`, and `input_dtype` values do not need to be overridden.
 A DEEPX device is `PCIe:<index>`, as reported on the detector settings page. The NPU daemon multiplexes across processes, so the same device may be listed more than once to run additional inference processes against it:
 ```yaml
 models:
  - devices:
      - deepx:PCIe:0
      - deepx:PCIe:0
 ```
 #### Label maps
 The object detection models in the DEEPX ModelZoo are trained on the standard 80-class COCO label set, so `labelmap_path` must be set to `/labelmap/coco-80.txt`. Frigate's default label map uses an extended 91-class COCO scheme, and leaving it in place will cause detections to be reported as the wrong object type. For `yolo-generic` models the label map is also what the detector uses to tell the output layout, so a label map with the wrong number of classes is reported as an error at startup.
 ---
 ## NVidia TensorRT Detector
 Nvidia Jetson devices may be used for object detection using the TensorRT libraries. Due to the size of the additional libraries, this detector is only provided in images with the `-tensorrt-jp6` tag suffix, e.g. `ghcr.io/blakeblackshear/frigate:stable-tensorrt-jp6`. This detector is designed to work with Yolo models for object detection.
@@ -45,10 +45,10 @@ Any detection below `min_score` will be immediately thrown out and never tracked
 Navigate to <NavPath path="Settings > Global configuration > Objects" /> to set score filters globally.
-| Field                                              | Description                                                      |
+| Field                                   | Description                                                      |
-| -------------------------------------------------- | ---------------------------------------------------------------- |
+| --------------------------------------- | ---------------------------------------------------------------- |
-| **Object filters > Person > Minimum confidence**   | Minimum score for a single detection to initiate tracking        |
+| **Object filters > Person > Min Score** | Minimum score for a single detection to initiate tracking        |
-| **Object filters > Person > Confidence threshold** | Minimum computed (median) score to be considered a true positive |
+| **Object filters > Person > Threshold** | Minimum computed (median) score to be considered a true positive |
 To override score filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" /> and select the camera.
@@ -103,12 +103,12 @@ Conceptually, a ratio of 1 is a square, 0.5 is a "tall skinny" box, and 2 is a "
 Navigate to <NavPath path="Settings > Global configuration > Objects" /> to set shape filters globally.
-| Field                                              | Description                                                              |
+| Field                                   | Description                                                              |
-| -------------------------------------------------- | ------------------------------------------------------------------------ |
+| --------------------------------------- | ------------------------------------------------------------------------ |
-| **Object filters > Person > Minimum object area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Min Area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Maximum object area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Max Area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Minimum aspect ratio** | Minimum width/height ratio of the bounding box                           |
+| **Object filters > Person > Min Ratio** | Minimum width/height ratio of the bounding box                           |
-| **Object filters > Person > Maximum aspect ratio** | Maximum width/height ratio of the bounding box                           |
+| **Object filters > Person > Max Ratio** | Maximum width/height ratio of the bounding box                           |
 To override shape filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" /> and select the camera.
@@ -70,14 +70,14 @@ Object filters help reduce false positives by constraining the size, shape, and
 Navigate to <NavPath path="Settings > Global configuration > Objects" />.
-| Field                                              | Description                                                              |
+| Field                                   | Description                                                              |
-| -------------------------------------------------- | ------------------------------------------------------------------------ |
+| --------------------------------------- | ------------------------------------------------------------------------ |
-| **Object filters > Person > Minimum object area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Min Area**  | Minimum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Maximum object area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
+| **Object filters > Person > Max Area**  | Maximum bounding box area in pixels (or decimal for percentage of frame) |
-| **Object filters > Person > Minimum aspect ratio** | Minimum width/height ratio of the bounding box                           |
+| **Object filters > Person > Min Ratio** | Minimum width/height ratio of the bounding box                           |
-| **Object filters > Person > Maximum aspect ratio** | Maximum width/height ratio of the bounding box                           |
+| **Object filters > Person > Max Ratio** | Maximum width/height ratio of the bounding box                           |
-| **Object filters > Person > Minimum confidence**   | Minimum score for the object to initiate tracking                        |
+| **Object filters > Person > Min Score** | Minimum score for the object to initiate tracking                        |
-| **Object filters > Person > Confidence threshold** | Minimum computed score to be considered a true positive                  |
+| **Object filters > Person > Threshold** | Minimum computed score to be considered a true positive                  |
 To override filters for a specific camera, navigate to <NavPath path="Settings > Camera configuration > Objects" />.
@@ -191,12 +191,14 @@ cameras:
    detect:
      enabled: false
    record:
-      enabled: true
+      enabled: false
    profiles:
      away:
        enabled: true
        detect:
          enabled: true
        record:
          enabled: true
      home:
        enabled: false
 ```
@@ -249,12 +251,6 @@ Leaving the `objects` section empty (or omitting `track`) does not clear the lis
 Fields that require a Frigate restart to take effect cannot be overridden by profiles, since profiles are applied at runtime without restarting. Those fields are hidden when editing a profile override and can only be changed on the base configuration.
 ### Why can't a profile enable recording when it's disabled in the base config?
 Frigate only sets up a camera's recording stream at startup when recording is enabled in the base config, so enabling it later from a profile has no effect. The same applies to turning recording on from the UI or MQTT.
 To keep recording off by default, leave `record.enabled: true` in the base config and create a profile that sets `record.enabled: false`. Activate that profile and it will be restored automatically when Frigate starts.
 ### Can I schedule profiles to be enabled or disabled at certain times?
 Not within Frigate itself. Frigate is an NVR, not an automation platform, so it intentionally does not include a scheduler for activating profiles. Instead, activate profiles from an automation platform that already handles time- and event-based triggers well, such as [Home Assistant](https://www.home-assistant.io/) or [Node-RED](https://nodered.org/). These integrate with Frigate and give you far more robust and flexible scheduling than a built-in scheduler could.
@@ -133,12 +133,13 @@ Switching between V1 and V2 requires reindexing your embeddings. The embeddings
 ### GenAI Provider
-Frigate can use a GenAI provider for semantic search embeddings when that provider has the `embeddings` role. See [Provider support](/configuration/genai/genai_config#provider-support) for which providers can serve this role.
+Frigate can use a GenAI provider for semantic search embeddings when that provider has the `embeddings` role. Currently, only **llama.cpp** supports multimodal embeddings (both text and images).
-To use a GenAI provider for semantic search:
+To use llama.cpp for semantic search:
-1. Configure a GenAI provider with `embeddings` in its `roles`, using a multimodal embedding model (both text and images). See [Embedding models](/configuration/genai/genai_config#embedding-models) for recommendations, and your provider's section of the [GenAI docs](/configuration/genai/genai_config) for any extra setup it needs.
+1. Configure a GenAI provider with `embeddings` in its `roles`.
 2. Set the semantic search model to the GenAI config key (e.g. `default`).
 3. Start the llama.cpp server with `--embeddings` and `--mmproj` for image support.
 <ConfigTabs>
 <TabItem value="ui">
@@ -173,6 +174,8 @@ semantic_search:
 </TabItem>
 </ConfigTabs>
 The llama.cpp server must be started with `--embeddings` for the embeddings API, and a multi-modal embeddings model. See the [llama.cpp server documentation](https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md) for details.
 :::note
 Switching between Jina models and a GenAI provider requires reindexing. Embeddings from different backends are incompatible.
@@ -242,8 +245,8 @@ Triggers are best configured through the Frigate UI.
 1. Navigate to <NavPath path="Settings > Enrichments > Triggers" /> and select a camera from the dropdown menu.
 2. Click **Add Trigger** to create a new trigger or use the pencil icon to edit an existing one.
 3. In the **Create Trigger** wizard:
-   - Enter a **Name** for the trigger (e.g., "Red Car Alert"). Frigate derives the trigger's
+   - Enter a **Name** for the trigger (e.g., "Red Car Alert").
-     internal **ID** from this name, which can be revealed and edited with the show/hide toggle.
+   - Enter a descriptive **Friendly Name** for the trigger (e.g., "Red car on the driveway camera").
   - Select the **Type** (`Thumbnail` or `Description`).
   - For `Thumbnail`, select an image to trigger this action when a similar thumbnail image is detected, based on the threshold.
   - For `Description`, enter text to trigger this action when a similar tracked object description is detected.
@@ -28,7 +28,7 @@ During testing, enable the Zones option for the [Debug view](/usage/live#the-sin
 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Under the **Zones** section, click the plus icon to add a new zone.
 3. Click on the camera's latest image to create the points for the zone boundary. Click the first point again to close the polygon.
-4. Configure zone options such as **Name**, **Objects**, **Loitering Time**, and **Inertia** in the zone editor.
+4. Configure zone options such as **Friendly name**, **Objects**, **Loitering time**, and **Inertia** in the zone editor.
 5. Press **Save** when finished.
 </TabItem>
@@ -200,7 +200,7 @@ When using loitering zones, a review item will behave in the following way:
 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Edit or create the zone (e.g., `sidewalk`).
-   - Set **Loitering Time** to the desired number of seconds (e.g., `4`)
+   - Set **Loitering time** to the desired number of seconds (e.g., `4`)
   - Under **Objects**, add the relevant object types (e.g., `person`)
 </TabItem>
@@ -291,7 +291,7 @@ Accurate real-world distance measurements are required to estimate speeds. These
 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Create or edit a zone with exactly 4 points aligned to the ground plane.
-3. In the zone editor, enable **Speed Estimation** and enter the real-world **Line A distance**, **Line B distance**, **Line C distance**, and **Line D distance** between each pair of consecutive points.
+3. In the zone editor, enter the real-world **Distances** between each pair of consecutive points.
   - For example, if the distance between the first and second points is 10 meters, between the second and third is 12 meters, etc.
 4. Distances are measured in meters (metric) or feet (imperial), depending on the **Unit system** setting.
@@ -358,7 +358,7 @@ Zones can be configured with a minimum speed requirement, meaning an object must
 1. Navigate to <NavPath path="Settings > Camera configuration > Masks / Zones" /> and select the desired camera.
 2. Edit or create the zone with distances configured.
-   - Set **Speed Threshold** to the desired minimum speed (e.g., `20`)
+   - Set **Speed threshold** to the desired minimum speed (e.g., `20`)
   - The unit is kph or mph, depending on the **Unit system** setting
 </TabItem>
@@ -54,7 +54,7 @@ An object filter mask drops any [bounding box](#bounding-box) whose bottom cente
 ## Min Score
-The lowest score a detected object can have to be kept during tracking. Anything scoring below the minimum is assumed to be a [false positive](#false-positive) and discarded. Set with `min_score` in the config, shown as **Minimum confidence** in the settings UI.
+The lowest score a detected object can have to be kept during tracking. Anything scoring below the minimum is assumed to be a [false positive](#false-positive) and discarded.
 ## Model
@@ -86,7 +86,7 @@ A more specific identity assigned to a [tracked object](#tracked-object-event-in
 ## Threshold
-The median score an object must reach to be considered a true positive. Set with `threshold` in the config, shown as **Confidence threshold** in the settings UI.
+The median score an object must reach to be considered a true positive.
 ## Top Score
@@ -65,26 +65,14 @@ Frigate supports multiple different detectors that work on different types of ha
  - [Supports many model architectures](../../configuration/object_detectors#memryx-mx3)
  - Runs best with tiny, small, or medium-size models
 - <CommunityBadge /> [DEEPX](#deepx-npu): The DEEPX NPU is available in m.2 format and as a HAT+ for the Raspberry Pi 5, allowing for a wide range of compatibility with devices.
  - [Supports YOLO model architectures](../../configuration/object_detectors#deepx-npu)
  - Runs best with tiny or small size models
  - Runs efficiently on low power hardware
 **AMD**
 - [ROCm](#rocm---amd-gpu): ROCm can run on AMD Discrete GPUs to provide efficient object detection
  - [Supports limited model architectures](../../configuration/object_detectors#amdrocm-gpu-detector)
  - Runs best on discrete AMD GPUs
 - <CommunityBadge /> [XDNA2 (Ryzen AI)](#amd-xdna2): AMD XDNA2 NPU (sub-watt power AI/ML processor separate to the GPU) inside Strix and other "AI" branded AMD platforms
  - Has only been tested with YOLOv9, in theory other graphs may be compiled too.
  - Runs via ZMQ proxy which adds some latency, only recommended for local connection
 **Apple Silicon**
 - [ONNX via lighter](#apple-silicon): The ONNX detector runs on the Neural Engine of M1 and newer Macs when Frigate runs in the lighter container runtime
  - [Supports the same model architectures as the ONNX detector](../../configuration/object_detectors#apple-neural-engine-lighter)
  - Runs inside the Frigate container, with no separate detector process to set up
  - The recommended way to run Frigate on a Mac
 - [Apple Silicon](#apple-silicon): Apple Silicon is usable on all M1 and newer Apple Silicon devices to provide efficient and fast object detection
  - [Supports primarily ssdlite and mobilenet model architectures](../../configuration/object_detectors#apple-silicon-detector)
  - Runs well with any size models including large
@@ -189,7 +177,7 @@ Frigate is able to utilize an Nvidia GPU which supports the 12.x series of CUDA
 #### Minimum Hardware Support
-12.x series of CUDA libraries are used which have minor version compatibility. The minimum driver version on the host system must be `>=545`. The oldest supported GPU generation for object detection is Pascal (Compute Capability 6.0 or greater). Older generations such as Maxwell are not supported but can still be used by following a [community workaround](https://github.com/blakeblackshear/frigate/discussions/20088). For other GPUs, check the NVIDIA GPU Compute Capability table linked below.
+12.x series of CUDA libraries are used which have minor version compatibility. The minimum driver version on the host system must be `>=545`. Also the GPU must support a Compute Capability of `5.0` or greater. This generally correlates to a Maxwell-era GPU or newer, check the NVIDIA GPU Compute Capability table linked below.
 Make sure your host system has the [nvidia-container-runtime](https://docs.docker.com/config/containers/resource_constraints/#access-an-nvidia-gpu) installed to pass through the GPU to the container and the host system has a compatible driver installed for your GPU.
@@ -218,13 +206,7 @@ Inference is done with the `onnx` detector type. Speeds will vary greatly depend
 ### Apple Silicon
-Frigate on a Mac is best run in the [lighter](https://github.com/fieldwork-ai/lighter) container runtime, where the [ONNX detector](../configuration/object_detectors.md#apple-neural-engine-lighter) runs on the Neural Engine of M1 and newer Macs from inside the Frigate container. There is no separate detector process to install or keep running, and the same container can decode video on the Mac's media engine.
+With the [Apple Silicon](../configuration/object_detectors.md#apple-silicon-detector) detector Frigate can take advantage of the NPU in M1 and newer Apple Silicon.
 | Name | YOLOv9 Inference Time                  | YOLO-NAS Inference Time | RF-DETR Inference Time |
 | ---- | -------------------------------------- | ----------------------- | ---------------------- |
 | M1   | t-320: 3.3 ms s-320: 7 ms s-640: 13 ms | 320: 6.6 ms             | Nano-320: 38 ms        |
 Alternatively, with the [Apple Silicon](../configuration/object_detectors.md#apple-silicon-detector) detector Frigate can take advantage of the NPU in M1 and newer Apple Silicon.
 :::warning
@@ -275,32 +257,6 @@ The MX3 is a pipelined architecture, where the maximum frames per second support
 Inference speeds may vary depending on the host platform. The above data was measured on an **Intel 13700 CPU**. Platforms like Raspberry Pi, Orange Pi, and other ARM-based SBCs have different levels of processing capability, which may limit total FPS.
 ### DEEPX NPU
 Frigate supports the DEEPX NPU in both of its form factors: the **DX-M1** M.2 module, which works on x86 (Intel/AMD) and ARM-based SBCs such as the Raspberry Pi 5, and the **DX-M1M** on the [Sixfab AI HAT+](https://docs.sixfab.com/docs/ai-hat-plus-raspberry-pi-5-quickstart) for the Raspberry Pi 5. Both use the same driver and runtime, so the configuration is identical for either one. DEEPX NPU support in Frigate is developed and maintained by [Sixfab](https://sixfab.com).
 The DEEPX driver and runtime run on the Docker host rather than inside the Frigate container and must be installed before the NPU can be used. See the [installation docs](installation.md#deepx-npu) for the setup steps and [the detector docs](/configuration/object_detectors#deepx-npu) for the configuration.
 Frigate does not bundle a model for this detector. Models use DEEPX's `.dxnn` format, and pre-compiled YOLO models can be downloaded from the [DEEPX ModelZoo](https://developer.deepx.ai/modelzoo). Prefer a model with a `_ppu` suffix whenever one is available for the architecture you want: these run part of the post-processing on the NPU itself and are considerably faster, roughly 2.5x for the same architecture and input size. **YOLOX-S with PPU is the recommended starting point.**
 Inference times for a few recommended models, measured through Frigate's own stats on a DX-M1:
 | Model             | Input Size | DX-M1 Inference Time |
 | ----------------- | ---------- | -------------------- |
 | YOLOX-S (PPU)     | 640        | ~ 13 ms              |
 | YOLOv9-t (PPU)    | 640        | ~ 18 ms              |
 | YOLOv4 (PPU)      | 512        | ~ 20 ms              |
 | YOLOX-S           | 640        | ~ 34 ms              |
 | YOLOv9-s          | 640        | ~ 39 ms              |
 Other ModelZoo YOLO variants are also supported but have not been measured. Inference speeds vary with the host platform, so a slower host such as a Raspberry Pi 5 will report higher times than those above.
 :::note
 A few ModelZoo models can not be used with Frigate: SSD models (they are trained on Pascal VOC, so their labels do not match Frigate's), DAMO-YOLO models, face and pose models, and the PPU builds of YOLOv7.
 :::
 ### Nvidia Jetson
 Jetson devices are supported via the TensorRT or ONNX detectors when running Jetpack 6. It will [make use of the Jetson's hardware media engine](/configuration/hardware_acceleration_video#nvidia-jetson) when configured with the [appropriate presets](/configuration/ffmpeg_presets#hwaccel-presets), and will make use of the Jetson's GPU and DLA for object detection when configured with the [TensorRT detector](/configuration/object_detectors#nvidia-tensorrt-detector).
@@ -341,32 +297,6 @@ The inference time of a rk3588 with all 3 cores enabled is typically 25-30 ms fo
 | ---------------- | ----------------------------------- |
 | yolov9-tiny      | ~ 4 ms                              |
 ### AMD Ryzen AI / XDNA2
 Frigate supports AMD XDNA2 NPUs through the community-maintained
 frigate-xdna ZMQ sidecar. It works with stock Frigate and supports
 Frigate+ models or compatible local YOLO ONNX models. Models are compiled
 once on the target system and cached for subsequent use.
 Currently qualified on **Ryzen AI Max 300 / Strix Halo**. Other XDNA2
 devices are not yet qualified; XDNA1 is unsupported.
 Measured YOLOv9 detector latency on Strix Halo:
 | Model | 320 | 640 |
 | ----- | ---: | ---: |
 | YOLOv9-T | ~7.4 ms | unsupported |
 | YOLOv9-S | ~9.0 ms | ~20.0 ms |
 | YOLOv9-M | ~13.1 ms | ~34.4 ms |
 | YOLOv9-C | ~14.1 ms | ~35.2 ms |
 | YOLOv9-E | ~69.4 ms | ~224.8 ms |
 **YOLOv9-C at 320 is the recommended quality/performance balance.**
 C at 640 is also usable where the lower throughput is acceptable.
 Setup, model preparation, and compatibility details are available
 [in the frigate-xdna documentation](https://github.com/mitchins/frigate-xdna).
 ## What does Frigate use the CPU for and what does it use a detector for? (ELI5 Version)
 This is taken from a [user question on reddit](https://www.reddit.com/r/homeassistant/comments/q8mgau/comment/hgqbxh5/?utm_source=share&utm_medium=web2x&context=3). Modified slightly for clarity.
@@ -381,99 +381,6 @@ If you can't use Docker Compose, you can run the container with something simila
 Finally, configure [hardware object detection](/configuration/object_detectors#memryx-mx3) to complete the setup.
 ### DEEPX NPU
 The DEEPX NPU is available in two form factors, and Frigate supports both:
 - **DX-M1** in the M.2 2280 form factor (like an NVMe SSD), for x86 (Intel/AMD) PCs, the Raspberry Pi 5, and other ARM SBCs with an exposed PCIe M.2 slot.
 - **DX-M1M** on the [Sixfab AI HAT+](https://docs.sixfab.com/docs/ai-hat-plus-raspberry-pi-5-quickstart), a HAT+ board that connects to the Raspberry Pi 5 over PCIe Gen 3 x1.
 Both present the NPU through the same PCIe driver and DX-RT runtime, so the setup below and the detector configuration are identical for either one. Nothing needs to change when moving between them.
 DEEPX NPU support in Frigate is developed and maintained by [Sixfab](https://sixfab.com).
 #### Versions
 A DEEPX install has several separately versioned pieces, and they all have to agree. The driver, the runtime, and the daemon live on the Docker host; Frigate itself carries only the Python bindings, which it downloads on first start:
 | Component      | Version  | Installed on | Installed by              |
 | -------------- | -------- | ------------ | ------------------------- |
 | Kernel driver  | `v2.6.0` | Host  | `user_installation.sh`    |
 | DX-RT runtime  | `v3.4.0` | Host  | `user_installation.sh`    |
 | NPU firmware   | `v2.7.4` | The module   | Flashed from the host     |
 | DX-RT bindings | `v3.4.0` | Frigate      | Downloaded at first start |
 :::warning
 A version mismatch does not produce a startup error. It typically shows up as inference requests that are accepted but never return a result, so detections simply stop appearing while Frigate looks healthy. If that happens after a Frigate upgrade, check every version in the table before anything else.
 :::
 The installation script installs the DX-RT runtime on the host and enables `dxrt.service`, so the daemon starts at boot and any other program on the host can share the NPU with Frigate. Check the firmware version with `dxrt-cli --status` and update the module if it does not match the table above.
 #### Installation
 The DEEPX kernel driver must be installed on the host rather than in the container, because containers share the host kernel and cannot load kernel modules. Installing it creates the `/dev/dxrt*` device nodes that are passed through to Frigate. The same script installs the DX-RT runtime and enables `dxrt.service`, the daemon that owns the NPU and hands work to it on behalf of Frigate and anything else on the host.
 1. Copy or download [this script](https://github.com/blakeblackshear/frigate/blob/dev/docker/deepx/user_installation.sh).
 2. Ensure it has execution permissions with `sudo chmod +x user_installation.sh`
 3. Run the script with `./user_installation.sh`
 4. **Restart your computer** to complete driver installation.
 Confirm the NPU is visible before continuing:
 ```bash
 ls /dev/dxrt*
 ```
 Then confirm the daemon is running and listening in `/run/dxrt`:
 ```bash
 systemctl is-active dxrt.service
 ls /run/dxrt/
 ```
 #### Setup
 To set up Frigate, follow the default installation instructions, for example: `ghcr.io/blakeblackshear/frigate:stable`
 #### Docker configuration
 Frigate needs the NPU device node and the directory holding the daemon's socket:
 ```yaml
 services:
  frigate:
    devices:
      - /dev/dxrt0:/dev/dxrt0
    volumes:
      - /run/dxrt:/run/dxrt
 ```
 If you can't use Docker Compose, add `--device /dev/dxrt0:/dev/dxrt0 -v /run/dxrt:/run/dxrt` to your `docker run` command.
 Add one `--device` per NPU, contiguously from `/dev/dxrt0`, since the client stops enumerating at the first gap.
 The installation script configures `dxrt.service` to place its socket in `/run/dxrt` through a systemd drop-in. Mounting the directory rather than the socket file means the container sees the new socket after `dxrt.service` is restarted, rather than holding on to a deleted one.
 `dxrtd` listens on an abstract socket as well, but that one does not cross into a container, so Frigate names the filesystem socket through `DXRT_DYNAMIC_IPC_ENDPOINT` on your behalf. Set that variable on the container yourself only if the daemon listens somewhere else, which means you also set it for `dxrtd` through its own systemd drop-in. The script writes `/etc/systemd/system/dxrt.service.d/frigate.conf` for exactly that, and has `dxrt.service` link the socket to `/tmp/dxrt_dynamic_ipc.sock` when it starts, so the host's own `dxrt-cli` and `dxtop` keep finding it at the default path they fall back to.
 :::note
 The DX-RT client exits when `dxrt.service` stops, so restart the Frigate container after restarting `dxrt.service`.
 :::
 The device node is needed as well as the socket, because the client opens the NPU directly even though the daemon arbitrates access. Without it, inference fails with `Device not found`.
 `/dev/shm` does not need sharing.
 The DX-RT python bindings are not shipped in the Frigate image. Frigate downloads them on first start when a DEEPX detector is configured, and caches them under `/config`.
 #### Configuration
 Finally, configure [hardware object detection](/configuration/object_detectors#deepx-npu) to complete the setup.
 ### Rockchip platform
 Make sure that you use a linux distribution that comes with the rockchip BSP kernel 5.10 or 6.1 and necessary drivers (especially rkvdec2 and rknpu). To check, enter the following commands:
@@ -11,12 +11,6 @@ MQTT requires a network connection to your broker. This is typically local, but
 :::
 :::note
 Wherever a topic below includes a camera, mask, or zone name, use its `ID` from the config, not its `friendly_name`. For example, a camera with `friendly_name: "Back Yard"` and ID `back_yard` publishes to `frigate/back_yard/...`, not `frigate/Back Yard/...`.
 :::
 ## General Frigate Topics
 ### `frigate/available`
@@ -218,7 +212,6 @@ An `update` with the same ID will be published when:
 - The severity changes from `detection` to `alert`
 - Additional objects are detected
 - An object is recognized via face, lpr, etc.
 - A [state classification](/configuration/custom_classification/state_classification#review-items) model changes state
 When the review activity has ended a final `end` message is published.
@@ -242,8 +235,7 @@ When the review activity has ended a final `end` message is published.
      "objects": ["person", "car"],
      "sub_labels": [],
      "zones": [],
-      "audio": [],
+      "audio": []
      "classification_state_changes": []
    }
  },
  "after": {
@@ -262,16 +254,7 @@ When the review activity has ended a final `end` message is published.
      "objects": ["person", "car"],
      "sub_labels": ["Bob"],
      "zones": ["front_yard"],
-      "audio": [],
+      "audio": []
      "classification_state_changes": [
        // verified changes of state classification models on this camera
        {
          "model": "front_gate",
          "from": "closed",
          "to": "open",
          "timestamp": 1718987131.52
        }
      ]
    }
  }
 }
@@ -27,10 +27,6 @@ The [Advanced Camera Card](https://card.camera/#/README) is a Home Assistant das
 It supports automatically setting the sub labels in Frigate for person objects that are detected and recognized.
 This is a fork (with fixed errors and new features) of [original Double Take](https://github.com/jakowenko/double-take) project which, unfortunately, isn't being maintained by author.
 ## [frigate-abr](https://github.com/007hacky007/frigate-abr)
 [frigate-abr](https://github.com/007hacky007/frigate-abr) is a drop-in Docker image of Frigate that adds adaptive bitrate (ABR) playback for recordings: a sidecar transcodes footage to lower quality tiers on demand, for reviewing over slow remote connections. Segments are transcoded when played and cached, so no additional stream is recorded. Frigate itself is not modified.
 ## [Frigate Notify](https://github.com/0x2142/frigate-notify)
 [Frigate Notify](https://github.com/0x2142/frigate-notify) is a simple app designed to send notifications from Frigate to your favorite platforms. Intended to be used with standalone Frigate installations - Home Assistant not required, MQTT is optional but recommended.
@@ -21,13 +21,7 @@ Yes. Models and metadata are stored in the `model_cache` directory within the co
 ### Can I keep using my Frigate+ models even if I do not renew my subscription?
-Yes. Subscriptions to Frigate+ provide access to the infrastructure used to train the models. Models you train during an active subscription remain licensed for your continued use even after your subscription ends — models already in your model cache will keep working indefinitely. An active subscription is required to train new models and download new versions.
+Yes. Subscriptions to Frigate+ provide access to the infrastructure used to train the models. Models trained with your subscription are yours to keep and use forever. However, do note that the terms and conditions prohibit you from sharing, reselling, or creating derivative products from the models.
 ### Can I use Frigate+ models commercially?
 A standard subscription covers use on camera systems you own or operate, including for your business. A shop, restaurant, warehouse, or office running Frigate+ at its own locations (including multiple locations) is exactly the kind of use the subscription is for.
 What the standard subscription does not cover is using Frigate+ models to provide a product or service to others. If you're deploying models at your customers' sites, bundling them with hardware you sell, or running them as part of a hosted or managed service, even if your customers never receive the model files themselves, you'll need a commercial license.
 Note that professional installers are fine under standard subscriptions when each customer holds their own Frigate+ subscription. The commercial license is for cases where your license powers your customers' sites.
 ### Why can't I submit images to Frigate+?
@@ -63,20 +63,20 @@ Frigate+ models generally have much higher scores than the default model provide
 <ConfigTabs>
 <TabItem value="ui">
-Navigate to <NavPath path="Settings > Global configuration > Objects" />. Under **Object filters**, set **Minimum confidence** and **Confidence threshold** for each object type, then click **Save**.
+Navigate to <NavPath path="Settings > Global configuration > Objects" />. Under **Object filters**, set **Min Score** and **Threshold** for each object type, then click **Save**.
-| Object            | Minimum confidence | Confidence threshold |
+| Object            | Min Score | Threshold |
-| ----------------- | ------------------ | -------------------- |
+| ----------------- | --------- | --------- |
-| **dog**           | .7                 | .9                   |
+| **dog**           | .7        | .9        |
-| **cat**           | .65                | .8                   |
+| **cat**           | .65       | .8        |
-| **face**          | .7                 |                      |
+| **face**          | .7        |           |
-| **package**       | .65                | .9                   |
+| **package**       | .65       | .9        |
-| **license_plate** | .6                 |                      |
+| **license_plate** | .6        |           |
-| **amazon**        | .75                |                      |
+| **amazon**        | .75       |           |
-| **ups**           | .75                |                      |
+| **ups**           | .75       |           |
-| **fedex**         | .75                |                      |
+| **fedex**         | .75       |           |
-| **person**        | .65                | .85                  |
+| **person**        | .65       | .85       |
-| **car**           | .65                | .85                  |
+| **car**           | .65       | .85       |
 </TabItem>
 <TabItem value="yaml">
@@ -65,11 +65,11 @@ Some users may find that Frigate+ models result in more false positives initiall
 Frigate+ models support a more relevant set of objects for security cameras. The labels for annotation in Frigate+ are configurable by editing the camera in the Cameras section of Frigate+. Currently, the following objects are supported:
- **People**: `person`, `face`, `baby`
+- **People**: `person`, `face`
- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `garbage truck`, `license_plate`
+- **Vehicles**: `car`, `motorcycle`, `bicycle`, `boat`, `school_bus`, `license_plate`
 - **Delivery Logos**: `amazon`, `usps`, `ups`, `fedex`, `dhl`, `an_post`, `purolator`, `postnl`, `nzpost`, `postnord`, `gls`, `dpd`, `canada_post`, `royal_mail`
- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`, `skunk`, `kangaroo`, `possum`, `rodent`
+- **Animals**: `dog`, `cat`, `deer`, `horse`, `bird`, `raccoon`, `fox`, `bear`, `cow`, `squirrel`, `goat`, `rabbit`, `skunk`, `kangaroo`
- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`, `baby_stroller`
+- **Other**: `package`, `waste_bin`, `bbq_grill`, `robot_lawnmower`, `umbrella`
 Other object types available in the default Frigate model are not available. Additional object types will be added in future releases.
@@ -77,12 +77,9 @@ Other object types available in the default Frigate model are not available. Add
 Candidate labels are also available for annotation. These labels don't have enough data to be included in the model yet, but using them will help add support sooner. You can enable these labels by editing the camera settings.
-Where possible, these labels are mapped to existing labels during training. For example, any `duck` labels are mapped to `bird` until support for new labels is added.
+Where possible, these labels are mapped to existing labels during training. For example, any `baby` labels are mapped to `person` until support for new labels is added.
- **Vehicles**: `tractor`, `golf_cart`, `bus`, `airplane`, `helicopter`, `rickshaw`, `scooter`
+The candidate labels are: `baby`, `bpost`, `badger`, `possum`, `rodent`, `chicken`, `groundhog`, `boar`, `hedgehog`, `tractor`, `golf cart`, `garbage truck`, `bus`, `sports ball`, `la_poste`, `lawnmower`, `heron`, `rickshaw`, `wombat`, `auspost`, `aramex`, `bobcat`, `mustelid`, `transoflex`, `airplane`, `drone`, `mountain_lion`, `crocodile`, `turkey`, `baby_stroller`, `monkey`, `coyote`, `porcupine`, `parcelforce`, `sheep`, `snake`, `helicopter`, `lizard`, `duck`, `hermes`, `cargus`, `fan_courier`, `sameday`
 - **Delivery Logos**: `bpost`, `auspost`, `aramex`, `transoflex`, `parcelforce`, `hermes`, `cargus`, `fan_courier`, `sameday`, `la_poste`
 - **Animals**: `badger`, `chicken`, `duck`, `turkey`, `groundhog`, `boar`, `hedgehog`, `wombat`, `bobcat`, `mustelid`, `mountain_lion`, `crocodile`, `monkey`, `coyote`, `porcupine`, `sheep`, `snake`, `lizard`, `heron`, `elk`, `moose`, `pig`, `donkey`, `civet`
 - **Other**: `sports_ball`, `drone`, `lawnmower`
 Candidate labels are not available for automatic suggestions.
@@ -53,14 +53,6 @@ go2rtc:
 Point the camera's inputs at the restream as described in the [restream docs](/configuration/restream.md), and swap `detect -> width` and `detect -> height` to match the rotated resolution.
 ### Can I add a privacy mask to hide part of my camera's view?
 Frigate does not have privacy masks. [Motion masks and object filter masks](../configuration/masks.md) only affect detection, they don't hide anything in live view, recordings, or snapshots.
 Privacy masks are best configured in the camera's firmware settings so the area is blacked out before the video ever leaves the camera and no extra processing is needed. Check there first.
 If your camera does not support privacy masks, there is no efficient alternative. Frigate copies the camera's video into recordings and live view without decoding it, so part of the image can't be hidden without transcoding (re-encoding) the stream. This can be done with a custom ffmpeg filter in go2rtc, but it is not recommended. Every masked camera needs a continuous re-encode, which significantly increases CPU usage, especially for high resolution streams.
 ### My mjpeg stream or snapshots look green and crazy
 This almost always means that the width/height defined for your camera are not correct. Double check the resolution with VLC or another player. Also make sure you don't have the width and height values backwards.
@@ -192,6 +184,6 @@ Filters and masks only hide the incorrect result - they don't teach Frigate what
 ### Where do I see problems Frigate has detected?
-Open System > Health. The Notices list keeps a record of problems Frigate has found. Acknowledge an entry to hide it until the problem happens again, or mute it to hide it for good. Hidden entries stay listed under Show hidden in the filter. Ongoing conditions, such as an offline camera or recordings deleted before their retention period, appear in the status bar for admins until they clear, and the status bar links to the Notices list while it has entries showing. On mobile, tap the warning icon in the bottom navigation bar to see them.
+Open System > Health. The Notices list keeps a record of problems Frigate has found, and you can dismiss any entry to acknowledge it. Ongoing conditions, such as an offline camera or recordings deleted before their retention period, appear in the status bar for admins until they clear, and the status bar links to the Notices list while it has undismissed entries. On mobile, tap the warning icon in the bottom navigation bar to see them.
 The Hardware section below the notices shows whether the detection hardware, hardware acceleration, and enrichment devices in your config were found and are being used, so a GPU that silently fell back to the CPU shows up as a warning. Run stream checks to probe every camera's streams for the same problems the camera wizard reports.
@@ -397,11 +397,19 @@ dmesg | grep -i -E "gpu|drm|reset|hang"
 Messages like `trying reset from guc_exec_queue_timedout_job` or similar GPU reset/hang messages indicate a driver or hardware issue. Ensure your kernel and GPU drivers (especially Intel) are up to date.
-#### Step 6: Verify go2rtc stream configuration
+#### Step 6: Verify hardware acceleration configuration
 An incorrect `hwaccel_args` preset can cause ffmpeg to fail silently or consume excessive CPU, starving the detector of resources.
 - After upgrading Frigate, verify your preset matches your hardware (e.g., `preset-intel-qsv-h264` instead of the deprecated `preset-vaapi`).
 - For h265 cameras, use the corresponding h265 preset (e.g., `preset-intel-qsv-h265`).
 - Note that `hwaccel_args` are only relevant for the detect stream. Frigate does not decode the record stream.
 #### Step 7: Verify go2rtc stream configuration
 Ensure that the ffmpeg source names in your go2rtc configuration match the correct camera stream. A misconfigured stream name (e.g., copying a config from one camera to another without updating the stream reference) will cause the wrong stream to be used or the stream to fail entirely.
-#### Step 7: Check system resources
+#### Step 8: Check system resources
 If none of the above apply, the issue may be a general resource constraint. Monitor the following on your host:
@@ -3,9 +3,6 @@ import * as path from "node:path";
 import type { Config, PluginConfig } from "@docusaurus/types";
 import type * as OpenApiPlugin from "docusaurus-plugin-openapi-docs";
 // Bump when a new stable release ships
 const STABLE_VERSION = "0.18";
 const config: Config = {
  title: "Frigate",
  tagline: "NVR With Realtime Object Detection for IP Cameras",
@@ -26,17 +23,17 @@ const config: Config = {
    mermaid: true,
  },
  i18n: {
-    defaultLocale: "en",
+    defaultLocale: 'en',
-    locales: ["en"],
+    locales: ['en'],
    localeConfigs: {
      en: {
-        label: "English",
+        label: 'English',
-      },
+      }
    },
  },
  themeConfig: {
    announcementBar: {
-      id: "frigate_plus",
+      id: 'frigate_plus',
      content: `
        <span style="margin-right: 8px; display: inline-block; animation: pulse 2s infinite;">🚀</span>
        Get more relevant and accurate detections with Frigate+ models.
@@ -48,8 +45,8 @@ const config: Config = {
            50%       { transform: scale(1.1); }
          }
        </style>`,
-      backgroundColor: "#005f73",
+      backgroundColor: '#005f73',
-      textColor: "#e0fbfc",
+      textColor: '#e0fbfc',
      isCloseable: false,
    },
    docs: {
@@ -86,15 +83,15 @@ const config: Config = {
      },
    },
    prism: {
-      magicComments: [
+      magicComments:[
        {
-          className: "theme-code-block-highlighted-line",
+          className: 'theme-code-block-highlighted-line',
-          line: "highlight-next-line",
+          line: 'highlight-next-line',
-          block: { start: "highlight-start", end: "highlight-end" },
+          block: {start: 'highlight-start', end: 'highlight-end'},
        },
        {
-          className: "code-block-error-line",
+          className: 'code-block-error-line',
-          line: "highlight-error-line",
+          line: 'highlight-error-line',
        },
      ],
      additionalLanguages: ["bash", "json"],
@@ -134,11 +131,6 @@ const config: Config = {
        srcDark: "img/branding/logo-dark.svg",
      },
      items: [
        {
          href: "https://github.com/blakeblackshear/frigate/releases",
          label: `${STABLE_VERSION}`,
          position: "left",
        },
        {
          to: "/",
          activeBasePath: "docs",
@@ -156,19 +148,19 @@ const config: Config = {
          position: "right",
        },
        {
-          type: "localeDropdown",
+          type: 'localeDropdown',
-          position: "right",
+          position: 'right',
          dropdownItemsAfter: [
            {
-              label: "简体中文（社区翻译）",
+              label: '简体中文（社区翻译）',
-              href: "https://docs.frigate-cn.video",
+              href: 'https://docs.frigate-cn.video',
-            },
+            }
-          ],
+          ]
        },
        {
-          href: "https://github.com/blakeblackshear/frigate",
+          href: 'https://github.com/blakeblackshear/frigate',
-          label: "GitHub",
+          label: 'GitHub',
-          position: "right",
+          position: 'right',
        },
      ],
    },
@@ -7030,9 +7030,9 @@
      }
    },
    "node_modules/brace-expansion": {
-      "version": "1.1.21",
+      "version": "1.1.18",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.21.tgz",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.18.tgz",
-      "integrity": "sha512-9zeA+KLZNNzglF2TPKRQEDyx6Yby7daAkuy8MiPzpXPsYDWi/DRM8jmwUDxokQjYqBpv5DgPiwD4h4ZZSy1Ujw==",
+      "integrity": "sha512-Edep/X9fGqVNmzKBVsDYIOtD+z1tuezV70LBjdCst9Tqu76lsnvRiZ6oTic1n+/BIwX6QDGAO94PN4N2SADvtw==",
      "license": "MIT",
      "dependencies": {
        "balanced-match": "^1.0.0",
@@ -7698,15 +7698,14 @@
      }
    },
    "node_modules/compression": {
-      "version": "1.8.2",
+      "version": "1.8.1",
-      "resolved": "https://registry.npmjs.org/compression/-/compression-1.8.2.tgz",
+      "resolved": "https://registry.npmjs.org/compression/-/compression-1.8.1.tgz",
-      "integrity": "sha512-o8vI5RE5A6EVVOd9o41jKp41aJom+QTEO/Bx8MYNjexMo/Bv2WOjUfZr+aL0WnYSgymUy6zeguqLTsIhV0gMvQ==",
+      "integrity": "sha512-9mAqGPHLakhCLeNyxPkK4xVo746zQ/czLH1Ky+vkitMnWfWZps8r0qXuwhwizagCRttsL4lfG4pIOvaWLpAP0w==",
      "license": "MIT",
      "dependencies": {
        "bytes": "3.1.2",
        "compressible": "~2.0.18",
        "debug": "2.6.9",
        "destroy": "1.2.0",
        "negotiator": "~0.6.4",
        "on-headers": "~1.1.0",
        "safe-buffer": "5.2.1",
@@ -7714,10 +7713,6 @@
      },
      "engines": {
        "node": ">= 0.8.0"
      },
      "funding": {
        "type": "opencollective",
        "url": "https://opencollective.com/express"
      }
    },
    "node_modules/compression/node_modules/bytes": {
@@ -9465,9 +9460,9 @@
      }
    },
    "node_modules/dompurify": {
-      "version": "3.4.16",
+      "version": "3.4.13",
-      "resolved": "https://registry.npmjs.org/dompurify/-/dompurify-3.4.16.tgz",
+      "resolved": "https://registry.npmjs.org/dompurify/-/dompurify-3.4.13.tgz",
-      "integrity": "sha512-sqo+pNp3qRhCIpbgRi1y8Tgk27Bo2Ry7w0dC1NBeNTdZChWjz9Xb/KOoZbRP/R6pQZ80Qw8YhXw13hWWBbMRnQ==",
+      "integrity": "sha512-2vmYIoqjze2d+kakP8S/nS5shfsl587kzwEjcGlTdiksUVgFHnFCsLYDVj/JNqJVOQZGSYBTmuycv0PodwmnMQ==",
      "license": "(MPL-2.0 OR Apache-2.0)",
      "optionalDependencies": {
        "@types/trusted-types": "^2.0.7"
@@ -10130,9 +10125,9 @@
      "license": "MIT"
    },
    "node_modules/fast-uri": {
-      "version": "3.1.8",
+      "version": "3.1.7",
-      "resolved": "https://registry.npmjs.org/fast-uri/-/fast-uri-3.1.8.tgz",
+      "resolved": "https://registry.npmjs.org/fast-uri/-/fast-uri-3.1.7.tgz",
-      "integrity": "sha512-GZMtZUTNRpOVIECoXwLNZS5xUGE+mVNbTB8h/7Rwh2TFWcBQiPzTgyZi05BF9UMZKkLJv8XBRJTlU7zg8+ZfMg==",
+      "integrity": "sha512-dOvZVzjdZdz7phd9v6jCbwxrBW3fK6n8Rc0CtdmM4bumzMnxywBYhuph6J819RRw/ku+rLbelwfMunktuzVVHg==",
      "funding": [
        {
          "type": "github",
@@ -11380,15 +11375,15 @@
      }
    },
    "node_modules/image-size": {
-      "version": "2.0.4",
+      "version": "2.0.2",
-      "resolved": "https://registry.npmjs.org/image-size/-/image-size-2.0.4.tgz",
+      "resolved": "https://registry.npmjs.org/image-size/-/image-size-2.0.2.tgz",
-      "integrity": "sha512-QRUkFFsRV/6fuESxb9Vkq+a0LkSrgKXuc2NEqfikiXxxN/G3tjWt5EVUlMaImRBZRZK/jRBEbYvpPYZL8t08Zw==",
+      "integrity": "sha512-IRqXKlaXwgSMAMtpNzZa1ZAe8m+Sa1770Dhk8VkSsP9LS+iHD62Zd8FQKs8fbPiagBE7BzoFX23cxFnwshpV6w==",
      "license": "MIT",
      "bin": {
        "image-size": "bin/image-size.js"
      },
      "engines": {
-        "node": ">=18"
+        "node": ">=16.x"
      }
    },
    "node_modules/immer": {
@@ -17453,9 +17448,9 @@
      "license": "ISC"
    },
    "node_modules/proxy-addr": {
-      "version": "2.0.8",
+      "version": "2.0.7",
-      "resolved": "https://registry.npmjs.org/proxy-addr/-/proxy-addr-2.0.8.tgz",
+      "resolved": "https://registry.npmjs.org/proxy-addr/-/proxy-addr-2.0.7.tgz",
-      "integrity": "sha512-5nnx0yGyVUcY6t9RnWcARWtwT9F1D8O9rt08htPvnd49W1IgZtmLkhu9WfMzQj1cFxjHIO6connUNVW5k7AVyQ==",
+      "integrity": "sha512-llQsMLSUDUPT44jdrU/O37qlnifitDP+ZwrmmZcoSKyLKvtZxpyV0n2/bD/N4tBAAZ/gJEdZU7KMraoK1+XYAg==",
      "license": "MIT",
      "dependencies": {
        "forwarded": "0.2.0",
@@ -17463,10 +17458,6 @@
      },
      "engines": {
        "node": ">= 0.10"
      },
      "funding": {
        "type": "opencollective",
        "url": "https://opencollective.com/express"
      }
    },
    "node_modules/proxy-addr/node_modules/ipaddr.js": {
@@ -19238,9 +19229,9 @@
      }
    },
    "node_modules/shell-quote": {
-      "version": "1.12.0",
+      "version": "1.10.0",
-      "resolved": "https://registry.npmjs.org/shell-quote/-/shell-quote-1.12.0.tgz",
+      "resolved": "https://registry.npmjs.org/shell-quote/-/shell-quote-1.10.0.tgz",
-      "integrity": "sha512-PcByqNyT/38F2kDNi006HAMRJaULuBzq/FOsw3qdZvX/GA9W/jamDaRskgHjubHiftXK5sIFxLNkvrXUwcof6Q==",
+      "integrity": "sha512-w1aiOKwKuRgtwAReIIj89puqg+I7GvX4IbLrvmhXbzQsj1+Zwi4VO3+fa6ZF91TWSjIxoEkKnMeHcLEODK5ZXA==",
      "license": "MIT",
      "engines": {
        "node": ">= 0.4"
@@ -19522,9 +19513,9 @@
      }
    },
    "node_modules/source-map-js": {
-      "version": "1.2.2",
+      "version": "1.2.1",
-      "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.2.2.tgz",
+      "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.2.1.tgz",
-      "integrity": "sha512-KGj/8Y43x35aZVDtt+J4mK1hoLGHULMYfSkODJNQjNDC3oW1PqPoxMwo0pLUsWM/UEGzON/NxeHywEfNXNP3Vw==",
+      "integrity": "sha512-UXWMKhLOwVKb728IUtQPXxfYU+usdybtUrK/8uGE8CQMvrhOpwvzDBwj0QhSL7MQc7vIsISBG8VQ8+IDQxpfQA==",
      "license": "BSD-3-Clause",
      "engines": {
        "node": ">=0.10.0"
@@ -412,39 +412,6 @@ paths:
      security:
        - frigateAdminAuth: []
      x-required-role: admin
  /go2rtc/streams/{stream_name}/bitrate:
    get:
      tags:
        - Camera
      summary: Go2Rtc Stream Bitrate
      description: |-
        **Access:** Admin role required.
        Measure a go2rtc stream's bitrate over a few seconds.
      operationId: 
        go2rtc_stream_bitrate_go2rtc_streams__stream_name__bitrate_get
      parameters:
        - name: stream_name
          in: path
          required: true
          schema:
            type: string
            title: Stream Name
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema: {}
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
      security:
        - frigateAdminAuth: []
      x-required-role: admin
  /ffprobe:
    get:
      tags:
@@ -1088,7 +1055,6 @@ paths:
          application/json:
            schema:
              type: object
              additionalProperties: true
              title: Body
      responses:
        '200':
@@ -1132,7 +1098,6 @@ paths:
          application/json:
            schema:
              type: object
              additionalProperties: true
              title: Body
      responses:
        '200':
@@ -1287,7 +1252,6 @@ paths:
          application/json:
            schema:
              type: object
              additionalProperties: true
              title: Body
      responses:
        '200':
@@ -1647,7 +1611,6 @@ paths:
          application/json:
            schema:
              type: object
              additionalProperties: true
              title: Body
      responses:
        '200':
@@ -1695,7 +1658,6 @@ paths:
          application/json:
            schema:
              type: object
              additionalProperties: true
              title: Body
      responses:
        '200':
@@ -1743,7 +1705,6 @@ paths:
          application/json:
            schema:
              type: object
              additionalProperties: true
              title: Body
      responses:
        '200':
@@ -1785,7 +1746,6 @@ paths:
          application/json:
            schema:
              type: object
              additionalProperties: true
              title: Body
      responses:
        '200':
@@ -1869,7 +1829,6 @@ paths:
          application/json:
            schema:
              type: object
              additionalProperties: true
              title: Body
      responses:
        '200':
@@ -2131,7 +2090,7 @@ paths:
          required: false
          schema:
            type: string
-            default: UTC
+            default: utc
            title: Timezone
      responses:
        '200':
@@ -3266,7 +3225,7 @@ paths:
            anyOf:
              - type: string
              - type: 'null'
-            default: UTC
+            default: utc
            title: Timezone
      responses:
        '200':
@@ -3491,7 +3450,6 @@ paths:
          application/json:
            schema:
              type: object
              additionalProperties: true
              title: Body
      responses:
        '200':
@@ -4216,20 +4174,19 @@ paths:
        Get notices, most severe first.
        Args:
-            include_hidden: Also return acknowledged and muted notices, for the
+            include_dismissed: Also return dismissed notices, for the history view
                hidden list
        Returns:
            The notices
      operationId: get_notices_notices_get
      parameters:
-        - name: include_hidden
+        - name: include_dismissed
          in: query
          required: false
          schema:
            type: boolean
            default: false
-            title: Include Hidden
+            title: Include Dismissed
      responses:
        '200':
          description: Successful Response
@@ -4264,16 +4221,16 @@ paths:
      security:
        - frigateAdminAuth: []
      x-required-role: admin
-  /notices/muted_checks:
+  /notices/dismissed_checks:
    get:
      tags:
        - Notices
-      summary: Get Muted Checks
+      summary: Get Dismissed Checks
      description: |-
        **Access:** Admin role required.
-        Get the muted config and stream check rows, newest first.
+        Get the dismissed config and stream check rows, newest first.
-      operationId: get_muted_checks_notices_muted_checks_get
+      operationId: get_dismissed_checks_notices_dismissed_checks_get
      responses:
        '200':
          description: Successful Response
@@ -4283,16 +4240,16 @@ paths:
      security:
        - frigateAdminAuth: []
      x-required-role: admin
-  /notices/hidden:
+  /notices/dismissed:
    delete:
      tags:
        - Notices
-      summary: Unhide All Notices
+      summary: Purge Dismissed
      description: |-
        **Access:** Admin role required.
-        Show every acknowledged and muted notice and check row again.
+        Delete every dismissed notice and check row so each can show again.
-      operationId: unhide_all_notices_notices_hidden_delete
+      operationId: purge_dismissed_notices_dismissed_delete
      responses:
        '200':
          description: Successful Response
@@ -4302,83 +4259,18 @@ paths:
      security:
        - frigateAdminAuth: []
      x-required-role: admin
-  /notices/{notice_id}/acknowledge:
+  /notices/{notice_id}/dismiss:
    post:
      tags:
        - Notices
-      summary: Acknowledge Notice
+      summary: Dismiss Notice
      description: |-
        **Access:** Admin role required.
-        Hide a notice until it happens again.
+        Hide a notice or a config or stream check row.
-        Config and stream check rows and the update notice never repeat, so they
+        It stays hidden if the same problem happens again.
-        can only be muted.
+      operationId: dismiss_notice_notices__notice_id__dismiss_post
      operationId: acknowledge_notice_notices__notice_id__acknowledge_post
      parameters:
        - name: notice_id
          in: path
          required: true
          schema:
            type: string
            title: Notice Id
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema: {}
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
      security:
        - frigateAdminAuth: []
      x-required-role: admin
  /notices/{notice_id}/mute:
    post:
      tags:
        - Notices
      summary: Mute Notice
      description: |-
        **Access:** Admin role required.
        Hide a notice or a config or stream check row for good.
      operationId: mute_notice_notices__notice_id__mute_post
      parameters:
        - name: notice_id
          in: path
          required: true
          schema:
            type: string
            title: Notice Id
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema: {}
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
      security:
        - frigateAdminAuth: []
      x-required-role: admin
  /notices/{notice_id}/hidden:
    delete:
      tags:
        - Notices
      summary: Unhide Notice
      description: |-
        **Access:** Admin role required.
        Show an acknowledged or muted notice or check row again.
      operationId: unhide_notice_notices__notice_id__hidden_delete
      parameters:
        - name: notice_id
          in: path
@@ -4502,16 +4394,6 @@ paths:
              - type: 'null'
            default: 100
            title: Limit
        - name: offset
          in: query
          required: false
          schema:
            anyOf:
              - type: integer
                minimum: 0
              - type: 'null'
            default: 0
            title: Offset
        - name: after
          in: query
          required: false
@@ -4666,7 +4548,7 @@ paths:
            anyOf:
              - type: string
              - type: 'null'
-            default: UTC
+            default: utc
            title: Timezone
      responses:
        '200':
@@ -4817,16 +4699,6 @@ paths:
              - type: 'null'
            default: 50
            title: Limit
        - name: offset
          in: query
          required: false
          schema:
            anyOf:
              - type: integer
                minimum: 0
              - type: 'null'
            default: 0
            title: Offset
        - name: cameras
          in: query
          required: false
@@ -4928,7 +4800,7 @@ paths:
            anyOf:
              - type: string
              - type: 'null'
-            default: UTC
+            default: utc
            title: Timezone
        - name: min_score
          in: query
@@ -5008,7 +4880,7 @@ paths:
            anyOf:
              - type: string
              - type: 'null'
-            default: UTC
+            default: utc
            title: Timezone
        - name: has_clip
          in: query
@@ -5661,7 +5533,6 @@ paths:
            application/json:
              schema:
                type: object
                additionalProperties: true
                title: Response Create Trigger Embedding Trigger Embedding Post
        '422':
          description: Validation Error
@@ -5710,7 +5581,6 @@ paths:
            application/json:
              schema:
                type: object
                additionalProperties: true
                title: Response Update Trigger Embedding Trigger Embedding  
                  Camera Name   Name  Put
        '422':
@@ -5753,7 +5623,6 @@ paths:
            application/json:
              schema:
                type: object
                additionalProperties: true
                title: Response Delete Trigger Embedding Trigger Embedding  
                  Camera Name   Name  Delete
        '422':
@@ -5790,7 +5659,6 @@ paths:
            application/json:
              schema:
                type: object
                additionalProperties: true
                title: Response Get Triggers Status Triggers Status  Camera Name  
                  Get
        '422':
@@ -7367,7 +7235,7 @@ paths:
          required: false
          schema:
            type: string
-            default: UTC
+            default: utc
            title: Timezone
        - name: cameras
          in: query
@@ -7417,7 +7285,7 @@ paths:
          required: false
          schema:
            type: string
-            default: UTC
+            default: utc
            title: Timezone
      responses:
        '200':
@@ -7587,7 +7455,6 @@ paths:
                type: array
                items:
                  type: object
                  additionalProperties: true
                title: Response No Recordings Recordings Unavailable Get
        '422':
          description: Validation Error
@@ -7791,19 +7658,13 @@ components:
          title: Update Topic
        config_data:
          anyOf:
-            - additionalProperties: true
+            - type: object
              type: object
            - type: 'null'
          title: Config Data
        skip_save:
          type: boolean
          title: Skip Save
          default: false
        replace_paths:
          items:
            type: string
          type: array
          title: Replace Paths
      type: object
      title: AppConfigSetBody
    AppPostLoginBody:
@@ -8032,7 +7893,7 @@ components:
      properties:
        file:
          type: string
-          contentMediaType: application/octet-stream
+          format: binary
          title: File
      type: object
      required:
@@ -8042,7 +7903,7 @@ components:
      properties:
        file:
          type: string
-          contentMediaType: application/octet-stream
+          format: binary
          title: File
      type: object
      required:
@@ -8140,7 +8001,6 @@ components:
        tool_calls:
          anyOf:
            - items:
                additionalProperties: true
                type: object
              type: array
            - type: 'null'
@@ -8436,7 +8296,6 @@ components:
            - type: 'null'
          title: Model Type
        data:
          additionalProperties: true
          type: object
          title: Data
      type: object
@@ -8508,8 +8367,7 @@ components:
          default: true
        draw:
          anyOf:
-            - additionalProperties: true
+            - type: object
              type: object
            - type: 'null'
          title: Draw
          default: {}
@@ -8752,8 +8610,7 @@ components:
          description: Error message for failed jobs
        results:
          anyOf:
-            - additionalProperties: true
+            - type: object
              type: object
            - type: 'null'
          title: Results
          description: Result metadata for completed jobs
@@ -9003,7 +8860,6 @@ components:
            - type: 'null'
          title: Base Url
        provider_options:
          additionalProperties: true
          type: object
          title: Provider Options
      type: object
@@ -9544,7 +9400,6 @@ components:
          type: string
          title: Tool Name
        arguments:
          additionalProperties: true
          type: object
          title: Arguments
      type: object
@@ -9622,11 +9477,6 @@ components:
        type:
          type: string
          title: Error Type
        input:
          title: Input
        ctx:
          type: object
          title: Context
      type: object
      required:
        - loc
@@ -99,10 +99,6 @@ def main() -> None:
        print("***    End Config Validation Errors                       ***")
        print("*************************************************************")
        # force a non-zero exit code for config failures
        if args.validate_config:
            sys.exit(1)
        # attempt to start Frigate in recovery mode
        try:
            config = FrigateConfig.load(install=True, safe_load=True)
@@ -63,7 +63,6 @@ from frigate.util.builtin import (
    flatten_config_data,
    load_labels,
    process_config_query_string,
    split_config_key_path,
    update_yaml_file_bulk,
 )
 from frigate.util.config import (
@@ -71,10 +70,6 @@ from frigate.util.config import (
    find_config_file,
    redact_credential,
 )
 from frigate.util.live_streams import (
    generated_transcode_streams,
    sync_transcode_streams,
 )
 from frigate.util.object_names import get_categorized_object_names
 from frigate.util.schema import get_config_schema
 from frigate.util.services import (
@@ -311,12 +306,9 @@ def config(request: Request):
        mode="json", warnings="none", exclude_none=True
    )
-    is_admin = request.headers.get("remote-role") == "admin"
+    # remove environment_vars for non-admin users
-
+    if request.headers.get("remote-role") != "admin":
    # hide environment_vars and the notification email from non-admin users
    if not is_admin:
        config.pop("environment_vars", None)
        redact_credential(config["notifications"], "email")
    # redact mqtt credentials
    redact_credential(config["mqtt"], "password")
@@ -373,15 +365,7 @@ def config(request: Request):
                camera_name
            )
            if base_sections:
-                # copy so redaction below can't alter the profile manager's cache
+                camera_dict["base_config"] = base_sections
                camera_dict["base_config"] = copy.deepcopy(base_sections)
        # cameras inherit the global notification email
        if not is_admin:
            redact_credential(camera_dict["notifications"], "email")
            redact_credential(
                camera_dict.get("base_config", {}).get("notifications", {}), "email"
            )
    # remove go2rtc stream passwords
    go2rtc: dict[str, Any] = config_obj.go2rtc.model_dump(
@@ -409,11 +393,6 @@ def config(request: Request):
        model_dict["non_logo_attributes"] = model.non_logo_attributes
        model_dict["labelmap"] = model.merged_labelmap
        # report the configured reference rather than the resolved cache path,
        # so saving the config back doesn't lose the Frigate+ model
        if model.plus_id:
            model_dict["path"] = f"plus://{model.plus_id}"
        if not config["plus"]["enabled"]:
            continue
@@ -450,8 +429,6 @@ def ffmpeg_presets():
        hwaccel_presets = [
            "preset-rpi-64-h264",
            "preset-rpi-64-h265",
            "preset-apple-silicon-h264",
            "preset-apple-silicon-h265",
            "preset-jetson-h264",
            "preset-jetson-h265",
            "preset-rkmpp",
@@ -831,17 +808,6 @@ def _config_set_in_memory(request: Request, body: AppConfigSetBody) -> JSONRespo
        )
 def _config_path_exists(data: Any, key_path: str) -> bool:
    """Return whether a dotted config path is present in parsed yaml."""
    for key in split_config_key_path(key_path):
        if not isinstance(data, dict) or key not in data:
            return False
        data = data[key]
    return True
@router.put("/config/set", dependencies=[Depends(require_role(["admin"]))])
 def config_set(request: Request, body: AppConfigSetBody):
    config_file = find_config_file()
@@ -896,19 +862,6 @@ def config_set(request: Request, body: AppConfigSetBody):
                        status_code=400,
                    )
                # delete replaced paths first so their maps are rewritten in
                # the order sent; update_yaml would otherwise keep old order
                if body.replace_paths:
                    old_yaml = ruamel.yaml.YAML(typ="safe").load(old_raw_config) or {}
                    updates = {
                        **{
                            path: ""
                            for path in body.replace_paths
                            if _config_path_exists(old_yaml, path)
                        },
                        **updates,
                    }
                # apply all updates in a single operation
                update_yaml_file_bulk(config_file, updates)
@@ -972,15 +925,9 @@ def config_set(request: Request, body: AppConfigSetBody):
            if request.app.dispatcher is not None:
                request.app.dispatcher.clear_runtime_state_for_yaml_keys(updates.keys())
            go2rtc_synced = True
            if body.requires_restart == 0 or body.update_topic:
                old_config: FrigateConfig = request.app.frigate_config
                swap_runtime_config(request.app, config)
                go2rtc_synced = sync_transcode_streams(
                    generated_transcode_streams(old_config),
                    generated_transcode_streams(config),
                )
                if body.update_topic:
                    if body.update_topic.startswith("config/cameras/"):
@@ -1040,7 +987,6 @@ def config_set(request: Request, body: AppConfigSetBody):
                            if body.requires_restart == 0
                            else "Config successfully updated, restart to apply"
                        ),
                        "go2rtc_synced": go2rtc_synced,
                    }
                ),
                status_code=200,
@@ -130,18 +130,23 @@ def require_admin_by_default():
        if path.startswith(EXEMPT_PREFIXES):
            return
-        # Camera routes enforce per-camera access via route-level dependencies
+        # Dynamic camera path exemption:
-        # (e.g. require_camera_access). Match on the route template, not the raw
+        # Any path whose first segment matches a configured camera name should
-        # path, so a camera named like another namespace (e.g. "faces") can't
+        # bypass the global admin requirement. These endpoints enforce access
-        # waive the admin check for that namespace's routes.
+        # via route-level dependencies (e.g. require_camera_access) to ensure
-        route = request.scope.get("route")
+        # per-camera authorization. This allows non-admin authenticated users
-        if (
+        # (e.g. viewer role) to access camera-specific resources without
-            route is not None
+        # needing admin privileges.
-            and route.path.startswith("/{camera_name}")
+        try:
-            and request.path_params.get("camera_name")
+            if path.startswith("/"):
-            in request.app.frigate_config.cameras
+                first_segment = path.split("/", 2)[1]
-        ):
+                if (
-            return
+                    first_segment
                    and first_segment in request.app.frigate_config.cameras
                ):
                    return
        except Exception:
            pass
        # For all other paths, require admin role
        # Internal port requests have admin role set automatically
@@ -319,17 +324,11 @@ def get_remote_addr(request: Request):
            network = ipaddress.ip_network(proxy)
        except ValueError:
            logger.warning(f"Unable to parse trusted network: {proxy}")
            continue
        trusted_proxies.append(network)
    # return the first remote address that is not trusted
    for addr in route:
-        try:
+        ip = ipaddress.ip_address(addr.strip())
            ip = ipaddress.ip_address(addr.strip())
        except ValueError:
            logger.debug("Invalid address in X-Forwarded-For header")
            return direct_addr or "127.0.0.1"
        logger.debug(f"Checking {ip} (v{ip.version})")
        trusted = False
        for trusted_proxy in trusted_proxies:
@@ -474,11 +473,12 @@ def create_encoded_jwt(user, role, expiration, secret):
 def set_jwt_cookie(response: Response, cookie_name, encoded_jwt, max_age, secure):
    # TODO: ideally this would set secure as well, but that requires TLS
-    # Starlette sets SameSite=Lax by default. The cookie is still sent to
+    # SameSite is intentionally left unset (browsers default to Lax). Setting
-    # same-site iframes (e.g. Home Assistant on the same host or domain), but
+    # SameSite=Lax/Strict would stop the cookie from being sent in cross-origin
-    # not to cross-site ones. CSRF is also mitigated by requiring a custom
+    # iframes, breaking embedded views such as the Home Assistant Frigate card.
-    # X-CSRF-TOKEN header, which cross-origin pages cannot set without a CORS
+    # CSRF is instead mitigated by requiring a custom X-CSRF-TOKEN header, which
-    # preflight that Frigate never grants (see check_csrf in api/fastapi_app.py).
+    # cross-origin pages cannot set without a CORS preflight that Frigate never
    # grants (see check_csrf in api/fastapi_app.py).
    response.set_cookie(
        key=cookie_name,
        value=encoded_jwt,
@@ -33,17 +33,12 @@ from frigate.config.camera.updater import (
    CameraConfigUpdateEnum,
    CameraConfigUpdateTopic,
 )
 from frigate.config.env import UnknownVariableError, substitute_frigate_vars
 from frigate.models import User
 from frigate.util.builtin import clean_camera_user_pass, get_record_segment_time
 from frigate.util.camera_cleanup import cleanup_camera_db, cleanup_camera_files
 from frigate.util.config import find_config_file
 from frigate.util.env import UnknownVariableError, substitute_frigate_vars
 from frigate.util.image import run_ffmpeg_snapshot
 from frigate.util.live_streams import (
    generated_transcode_streams,
    measure_stream_bitrate,
    sync_transcode_streams,
 )
 from frigate.util.services import (
    analyze_record_keyframes,
    ffprobe_stream,
@@ -253,34 +248,6 @@ def go2rtc_delete_stream(stream_name: str):
        )
@router.get(
    "/go2rtc/streams/{stream_name}/bitrate",
    dependencies=[Depends(require_role(["admin"]))],
 )
 async def go2rtc_stream_bitrate(request: Request, stream_name: str):
    """Measure a go2rtc stream's bitrate over a few seconds."""
    config: FrigateConfig = request.app.frigate_config
    known = set(config.go2rtc.model_dump().get("streams") or {}) | set(
        generated_transcode_streams(config)
    )
    if stream_name not in known:
        return JSONResponse(
            content={"success": False, "message": "Unknown stream"},
            status_code=404,
        )
    kbps = await asyncio.to_thread(measure_stream_bitrate, stream_name)
    if kbps is None:
        return JSONResponse(
            content={"success": False, "message": "Stream sent no data"},
            status_code=502,
        )
    return JSONResponse(content={"success": True, "kbps": round(kbps)})
@router.get("/ffprobe", dependencies=[Depends(require_role(["admin"]))])
 def ffprobe(request: Request, paths: str = "", detailed: bool = False):
    path_param = paths
@@ -1375,12 +1342,6 @@ async def delete_camera(
    except Exception:
        logger.debug("Failed to remove go2rtc stream for %s", camera_name)
    await asyncio.to_thread(
        sync_transcode_streams,
        generated_transcode_streams(frigate_config),
        generated_transcode_streams(request.app.frigate_config),
    )
    return JSONResponse(
        content={
            "success": True,
@@ -1,6 +1,5 @@
 """Object classification APIs."""
 import asyncio
 import datetime
 import logging
 import os
@@ -49,9 +48,6 @@ logger = logging.getLogger(__name__)
 router = APIRouter(tags=[Tags.classification])
 # concurrent collections for one model share its temp and train directories
 example_collection_lock = asyncio.Lock()
 def invalid_name_response(value: str) -> JSONResponse:
    """Response for a name that cannot be used as a path component."""
@@ -1294,10 +1290,7 @@ async def generate_object_examples(request: Request, body: GenerateObjectExample
    if model_name is None:
        return invalid_name_response(body.model_name)
-    async with example_collection_lock:
+    collect_object_classification_examples(model_name, body.label)
        await asyncio.to_thread(
            collect_object_classification_examples, model_name, body.label
        )
    return JSONResponse(
        content={"success": True, "message": "Example generation completed"},
@@ -51,7 +51,6 @@ def swap_runtime_config(app: FastAPI, config: FrigateConfig) -> None:
    if app.stats_emitter is not None:
        app.stats_emitter.config = config
        app.stats_emitter.hardware_stats.set_config(config)
    if app.dispatcher is not None:
        app.dispatcher.config = config
@@ -7,4 +7,4 @@ class AppTimelineHourlyQueryParameters(BaseModel):
    after: float | None = None
    before: float | None = None
    limit: int | None = 200
-    timezone: str | None = "UTC"
+    timezone: str | None = "utc"
@@ -14,7 +14,6 @@ class EventsQueryParams(BaseModel):
    zone: str | None = "all"
    zones: str | None = "all"
    limit: int | None = 100
    offset: int | None = Field(0, ge=0)
    after: float | None = None
    before: float | None = None
    time_range: str | None = DEFAULT_TIME_RANGE
@@ -40,7 +39,7 @@ class EventsQueryParams(BaseModel):
    max_length: float | None = None
    event_id: str | None = None
    sort: str | None = None
-    timezone: str | None = "UTC"
+    timezone: str | None = "utc"
 class EventsSearchQueryParams(BaseModel):
@@ -56,7 +55,6 @@ class EventsSearchQueryParams(BaseModel):
        deprecated=True,
    )
    limit: int | None = 50
    offset: int | None = Field(0, ge=0)
    cameras: str | None = "all"
    labels: str | None = "all"
    sub_labels: str | None = "all"
@@ -68,7 +66,7 @@ class EventsSearchQueryParams(BaseModel):
    has_clip: bool | None = None
    has_snapshot: bool | None = None
    is_submitted: bool | None = None
-    timezone: str | None = "UTC"
+    timezone: str | None = "utc"
    min_score: float | None = None
    max_score: float | None = None
    min_speed: float | None = None
@@ -78,6 +76,6 @@ class EventsSearchQueryParams(BaseModel):
 class EventsSummaryQueryParams(BaseModel):
-    timezone: str | None = "UTC"
+    timezone: str | None = "utc"
    has_clip: int | None = None
    has_snapshot: int | None = None
@@ -3,7 +3,7 @@ from pydantic.json_schema import SkipJsonSchema
 class MediaRecordingsSummaryQueryParams(BaseModel):
-    timezone: str = "UTC"
+    timezone: str = "utc"
    cameras: str | None = "all"
@@ -19,7 +19,7 @@ class ReviewSummaryQueryParams(BaseModel):
    cameras: str = "all"
    labels: str = "all"
    zones: str = "all"
-    timezone: str = "UTC"
+    timezone: str = "utc"
 class ReviewActivityMotionQueryParams(BaseModel):
@@ -10,8 +10,6 @@ class AppConfigSetBody(BaseModel):
    update_topic: str | None = None
    config_data: dict[str, Any] | None = None
    skip_save: bool = False
    # paths rewritten whole, so a map saves in the order sent
    replace_paths: list[str] = Field(default_factory=list)
 class GenAIProbeBody(BaseModel):
@@ -129,7 +129,6 @@ def events(
        zones = zone
    limit = params.limit
    offset = params.offset
    after = params.after
    before = params.before
    time_range = params.time_range
@@ -362,15 +361,11 @@ def events(
    else:
        order_by = Event.start_time.desc()
    # offset paging needs a stable order when scores or speeds tie
    tiebreaker = [Event.id] if sort and sort.startswith(("score", "speed")) else []
    events = (
        Event.select(*selected_columns)
        .where(reduce(operator.and_, clauses))
-        .order_by(order_by, *tiebreaker)
+        .order_by(order_by)
        .limit(limit)
        .offset(offset)
        .dicts()
        .iterator()
    )
@@ -539,7 +534,6 @@ def events_search(
    search_type = params.search_type
    include_thumbnails = params.include_thumbnails
    limit = params.limit
    offset = params.offset
    sort = params.sort
    # Filters
@@ -846,9 +840,6 @@ def events_search(
    if search_results:
        events_query = events_query.where(Event.id << list(search_results.keys()))
    # sorts below are stable, so this orders ties for offset paging
    events_query = events_query.order_by(Event.id)
    # Fetch events and process them in a single pass
    processed_events = []
    for event in events_query.dicts():
@@ -906,7 +897,7 @@ def events_search(
        processed_events.sort(key=lambda x: x["start_time"], reverse=True)
    # Limit the number of events returned
-    processed_events = processed_events[offset:][:limit]
+    processed_events = processed_events[:limit]
    return JSONResponse(content=processed_events)
@@ -1,7 +1,6 @@
 import asyncio
 import logging
 import re
 from contextlib import asynccontextmanager
 from fastapi import Depends, FastAPI, Request
 from fastapi.responses import JSONResponse
@@ -82,20 +81,9 @@ def create_fastapi_app(
    config_holder: ConfigHolder | None = None,
    notice_registry: NoticeRegistry | None = None,
 ):
    @asynccontextmanager
    async def lifespan(app: FastAPI):
        logger.info("FastAPI started")
        asyncio.create_task(
            debug_replay_auto_stop_watchdog(
                replay_manager, frigate_config, config_publisher
            )
        )
        yield
    logger.info("Starting FastAPI app")
    app = FastAPI(
        debug=False,
        lifespan=lifespan,
        swagger_ui_parameters={"apisSorter": "alpha", "operationsSorter": "alpha"},
        dependencies=[Depends(require_admin_by_default())]
        if enforce_default_admin
@@ -131,6 +119,15 @@ def create_fastapi_app(
            database.close()
        return response
    @app.on_event("startup")
    async def startup():
        logger.info("FastAPI started")
        asyncio.create_task(
            debug_replay_auto_stop_watchdog(
                replay_manager, frigate_config, config_publisher
            )
        )
    # Rate limiter (used for login endpoint)
    if frigate_config.auth.failed_login_rate_limit is None:
        limiter.enabled = False
@@ -14,10 +14,10 @@ from enum import Enum
 from pathlib import Path as FilePath
 from typing import IO, Any
 from urllib.parse import unquote
 from zoneinfo import ZoneInfo
 import cv2
 import numpy as np
 import pytz
 from fastapi import APIRouter, Depends, Path, Query, Request, Response
 from fastapi.responses import FileResponse, JSONResponse, StreamingResponse
 from pathvalidate import sanitize_filename
@@ -63,8 +63,8 @@ from frigate.util.recording_coverage import (
    null_audio_glitches,
    plan_clip,
    resolve_coverage,
    stream_has_audio,
 )
 from frigate.util.time import get_normalized_tz_name
 logger = logging.getLogger(__name__)
@@ -681,10 +681,15 @@ async def _vod_response(
        end_ts,
        force_discontinuity,
    )
    intervals = resolve_coverage(camera_name, start_ts, end_ts)
    # rows contradicting their stream's audio composition are
    # truncated-shutdown glitches
    main_audio = stream_has_audio(intervals, main=True)
    sub_audio = stream_has_audio(intervals, main=False)
    spans = build_spans(
-        null_audio_glitches(resolve_coverage(camera_name, start_ts, end_ts)),
+        null_audio_glitches(intervals, main_audio, sub_audio),
        stream_preference,
    )
@@ -838,9 +843,7 @@ async def vod_hour(
    parts = year_month.split("-")
    start_date = (
        datetime(int(parts[0]), int(parts[1]), day, hour, tzinfo=UTC)
-        - datetime.now(
+        - datetime.now(pytz.timezone(tz_name.replace(",", "/"))).utcoffset()
            ZoneInfo(get_normalized_tz_name(tz_name.replace(",", "/")))
        ).utcoffset()
    )
    end_date = start_date + timedelta(hours=1) - timedelta(milliseconds=1)
    start_ts = start_date.timestamp()
@@ -14,18 +14,17 @@ router = APIRouter(tags=[Tags.notices])
@router.get("/notices", dependencies=[Depends(require_role(["admin"]))])
-def get_notices(request: Request, include_hidden: bool = False) -> JSONResponse:
+def get_notices(request: Request, include_dismissed: bool = False) -> JSONResponse:
    """Get notices, most severe first.
    Args:
-        include_hidden: Also return acknowledged and muted notices, for the
+        include_dismissed: Also return dismissed notices, for the history view
            hidden list
    Returns:
        The notices
    """
    return JSONResponse(
-        content=request.app.notice_registry.active(include_hidden=include_hidden)
+        content=request.app.notice_registry.active(include_dismissed=include_dismissed)
    )
@@ -35,64 +34,37 @@ def get_notice_stats(request: Request) -> JSONResponse:
    return JSONResponse(content=request.app.notice_registry.stats())
-@router.get("/notices/muted_checks", dependencies=[Depends(require_role(["admin"]))])
+@router.get(
-def get_muted_checks(request: Request) -> JSONResponse:
+    "/notices/dismissed_checks", dependencies=[Depends(require_role(["admin"]))]
-    """Get the muted config and stream check rows, newest first."""
+)
-    return JSONResponse(content=request.app.notice_registry.muted_checks())
+def get_dismissed_checks(request: Request) -> JSONResponse:
    """Get the dismissed config and stream check rows, newest first."""
    return JSONResponse(content=request.app.notice_registry.dismissed_checks())
-@router.delete("/notices/hidden", dependencies=[Depends(require_role(["admin"]))])
+@router.delete("/notices/dismissed", dependencies=[Depends(require_role(["admin"]))])
-def unhide_all_notices(request: Request) -> JSONResponse:
+def purge_dismissed(request: Request) -> JSONResponse:
-    """Show every acknowledged and muted notice and check row again."""
+    """Delete every dismissed notice and check row so each can show again."""
-    request.app.notice_registry.unhide_all()
+    request.app.notice_registry.purge_dismissed()
-    return JSONResponse(content={"success": True, "message": "Notices shown again"})
+    return JSONResponse(
        content={"success": True, "message": "Dismissed notices cleared"}
    )
 # model notice ids contain a slash, so the id is a path parameter
@router.post(
-    "/notices/{notice_id:path}/acknowledge",
+    "/notices/{notice_id:path}/dismiss",
    dependencies=[Depends(require_role(["admin"]))],
 )
-def acknowledge_notice(request: Request, notice_id: str) -> JSONResponse:
+def dismiss_notice(request: Request, notice_id: str) -> JSONResponse:
-    """Hide a notice until it happens again.
+    """Hide a notice or a config or stream check row.
-    Config and stream check rows and the update notice never repeat, so they
+    It stays hidden if the same problem happens again.
    can only be muted.
    """
-    if not request.app.notice_registry.acknowledge(notice_id):
+    if not request.app.notice_registry.dismiss(notice_id):
        return JSONResponse(
            content={"success": False, "message": "Notice not found"},
            status_code=404,
        )
-    return JSONResponse(content={"success": True, "message": "Notice acknowledged"})
+    return JSONResponse(content={"success": True, "message": "Notice dismissed"})
@router.post(
    "/notices/{notice_id:path}/mute",
    dependencies=[Depends(require_role(["admin"]))],
 )
 def mute_notice(request: Request, notice_id: str) -> JSONResponse:
    """Hide a notice or a config or stream check row for good."""
    if not request.app.notice_registry.mute(notice_id):
        return JSONResponse(
            content={"success": False, "message": "Notice not found"},
            status_code=404,
        )
    return JSONResponse(content={"success": True, "message": "Notice muted"})
@router.delete(
    "/notices/{notice_id:path}/hidden",
    dependencies=[Depends(require_role(["admin"]))],
 )
 def unhide_notice(request: Request, notice_id: str) -> JSONResponse:
    """Show an acknowledged or muted notice or check row again."""
    if not request.app.notice_registry.unhide(notice_id):
        return JSONResponse(
            content={"success": False, "message": "Notice not found"},
            status_code=404,
        )
    return JSONResponse(content={"success": True, "message": "Notice shown again"})
@@ -5,8 +5,8 @@ import logging
 import os
 import threading
 from datetime import UTC, datetime, timedelta
 from zoneinfo import ZoneInfo
 import pytz
 from fastapi import APIRouter, Depends, HTTPException
 from fastapi.responses import JSONResponse
@@ -22,7 +22,6 @@ from frigate.api.defs.response.preview_response import (
 from frigate.api.defs.tags import Tags
 from frigate.const import BASE_DIR, CACHE_DIR, PREVIEW_FRAME_TYPE
 from frigate.models import Previews
 from frigate.util.time import get_normalized_tz_name
 logger = logging.getLogger(__name__)
@@ -127,9 +126,7 @@ def preview_hour(
    parts = year_month.split("-")
    start_date = (
        datetime(int(parts[0]), int(parts[1]), int(day), int(hour), tzinfo=UTC)
-        - datetime.now(
+        - datetime.now(pytz.timezone(tz_name.replace(",", "/"))).utcoffset()
            ZoneInfo(get_normalized_tz_name(tz_name.replace(",", "/")))
        ).utcoffset()
    )
    end_date = start_date + timedelta(hours=1) - timedelta(milliseconds=1)
    start_ts = start_date.timestamp()
@@ -175,7 +175,7 @@ async def all_recordings_summary(
@router.get(
    "/{camera_name}/recordings/summary", dependencies=[Depends(require_camera_access)]
 )
-async def recordings_summary(camera_name: str, timezone: str = "UTC"):
+async def recordings_summary(camera_name: str, timezone: str = "utc"):
    """Returns hourly summary for recordings of given camera"""
    time_range_query = (
@@ -44,22 +44,6 @@ logger = logging.getLogger(__name__)
 router = APIRouter(tags=[Tags.review])
 def get_label_clause(label: str, include_audio: bool = True):
    """Build a clause matching a label within a review segment's data.
    Verified objects are stored with a `-verified` suffix (eg. `person-verified`)
    so that variant is matched as well.
    """
    clause = (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*') | (
        ReviewSegment.data["objects"].cast("text") % f'*"{label}-verified"*'
    )
    if include_audio:
        clause |= ReviewSegment.data["audio"].cast("text") % f'*"{label}"*'
    return clause
@router.get(
    "/review",
    response_model=list[ReviewSegmentResponse],
@@ -109,7 +93,10 @@ async def review(
        filtered_labels = labels.split(",")
        for label in filtered_labels:
-            label_clauses.append(get_label_clause(label))
+            label_clauses.append(
                (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
                | (ReviewSegment.data["audio"].cast("text") % f'*"{label}"*')
            )
        clauses.append(reduce(operator.or_, label_clauses))
    if zones != "all":
@@ -252,7 +239,10 @@ async def review_summary(
        filtered_labels = labels.split(",")
        for label in filtered_labels:
-            label_clauses.append(get_label_clause(label))
+            label_clauses.append(
                (ReviewSegment.data["objects"].cast("text") % f'*"{label}"*')
                | (ReviewSegment.data["audio"].cast("text") % f'*"{label}"*')
            )
        clauses.append(reduce(operator.or_, label_clauses))
    if zones != "all":
        # use matching so segments with multiple zones
@@ -350,8 +340,9 @@ async def review_summary(
        filtered_labels = labels.split(",")
        for label in filtered_labels:
-            label_clauses.append(get_label_clause(label, include_audio=False))
+            label_clauses.append(
-
+                ReviewSegment.data["objects"].cast("text") % f'*"{label}"*'
            )
        clauses.append(reduce(operator.or_, label_clauses))
    # Find the time range of available data
@@ -702,7 +693,7 @@ def motion_activity(
    df = df[df["camera"] != ""]
    # change types for output
-    df.index = df.index.as_unit("s").astype(int)
+    df.index = df.index.astype(int) // (10**9)
    normalized = df.reset_index().to_dict("records")
    return JSONResponse(content=normalized)
@@ -12,8 +12,8 @@ from pathlib import Path
 import psutil
 import uvicorn
 from peewee import SqliteDatabase
 from peewee_migrate import Router
 from playhouse.sqlite_ext import SqliteExtDatabase
 from frigate.api.auth import hash_password
 from frigate.api.fastapi_app import create_fastapi_app
@@ -49,6 +49,7 @@ from frigate.debug_replay import (
    DebugReplayManager,
    cleanup_replay_cameras,
 )
 from frigate.detectors.detector_config import SceneEnum
 from frigate.detectors.detector_types import api_types
 from frigate.detectors.device import build_detector_config, runner_names
 from frigate.embeddings import EmbeddingProcess, EmbeddingsContext
@@ -77,7 +78,7 @@ from frigate.notices.registry import NoticeRegistry
 from frigate.object_detection.base import ObjectDetectProcess
 from frigate.object_detection.util import detection_frame_size
 from frigate.output.output import OutputProcess
-from frigate.ptz.autotrack import PtzAutoTracker
+from frigate.ptz.autotrack import PtzAutoTrackerThread
 from frigate.ptz.onvif import OnvifController
 from frigate.record.cleanup import RecordingCleanup
 from frigate.record.export import migrate_exports
@@ -107,7 +108,7 @@ class FrigateApp:
        self.metrics_manager = manager
        self.audio_process: mp.Process | None = None
        self.stop_event = stop_event
-        self.detection_queues: dict[str, Queue] = {
+        self.detection_queues: dict[SceneEnum, Queue] = {
            model.scene: mp.Queue() for model in config.models
        }
        self.detectors: dict[str, ObjectDetectProcess] = {}
@@ -166,7 +167,11 @@ class FrigateApp:
        # create camera_metrics
        for camera_name in self.config.cameras.keys():
            self.camera_metrics[camera_name] = CameraMetrics(self.metrics_manager)
-            self.ptz_metrics[camera_name] = PTZMetrics()
+            self.ptz_metrics[camera_name] = PTZMetrics(
                autotracker_enabled=self.config.cameras[
                    camera_name
                ].onvif.autotracking.enabled
            )
    def init_queues(self) -> None:
        # Queue for cameras to push tracked objects to
@@ -186,7 +191,7 @@ class FrigateApp:
        self.timeline_queue: Queue = mp.Queue()
    def init_database(self) -> None:
-        def vacuum_db(db: SqliteDatabase) -> None:
+        def vacuum_db(db: SqliteExtDatabase) -> None:
            logger.info("Running database vacuum")
            db.execute_sql("VACUUM;")
@@ -197,7 +202,7 @@ class FrigateApp:
                logger.error("Unable to write to /config to save DB state")
        # Migrate DB schema
-        migrate_db = SqliteDatabase(self.config.database.path)
+        migrate_db = SqliteExtDatabase(self.config.database.path)
        # Run migrations
        del logging.getLogger("peewee_migrate").handlers[:]
@@ -390,7 +395,7 @@ class FrigateApp:
                logger.error("Unable to prepare the %s runtime: %s", detector_type, err)
    def start_detectors(self) -> None:
-        model_cameras: dict[str, list[str]] = {
+        model_cameras: dict[SceneEnum, list[str]] = {
            model.scene: [] for model in self.config.models
        }
@@ -439,7 +444,7 @@ class FrigateApp:
                )
    def start_ptz_autotracker(self) -> None:
-        self.ptz_autotracker_thread = PtzAutoTracker(
+        self.ptz_autotracker_thread = PtzAutoTrackerThread(
            self.config,
            self.onvif_controller,
            self.ptz_metrics,
@@ -478,7 +483,7 @@ class FrigateApp:
    def start_audio_processor(self) -> None:
        self.audio_process = AudioProcessor(
-            self.config, self.camera_metrics, self.embeddings_metrics, self.stop_event
+            self.config, self.camera_metrics, self.stop_event
        )
        self.audio_process.start()
        self.processes["audio_detector"] = self.audio_process.pid or 0
@@ -551,20 +556,10 @@ class FrigateApp:
                "output",
                lambda: OutputProcess(self.config, self.stop_event),
            ),
            (
                "audio_process",
                "audio_detector",
                lambda: AudioProcessor(
                    self.config,
                    self.camera_metrics,
                    self.embeddings_metrics,
                    self.stop_event,
                ),
            ),
        ]
        for attr, key, factory in specs:
-            if getattr(self, attr, None) is None:
+            if not hasattr(self, attr):
                continue
            def on_restart(
@@ -43,6 +43,8 @@ class CameraMetrics:
 class PTZMetrics:
    autotracker_enabled: Synchronized
    start_time: Synchronized
    stop_time: Synchronized
    frame_time: Synchronized
@@ -50,17 +52,21 @@ class PTZMetrics:
    max_zoom: Synchronized
    min_zoom: Synchronized
    tracking_active: Event
    motor_stopped: Event
    reset: Event
-    def __init__(self) -> None:
+    def __init__(self, *, autotracker_enabled: bool):
-        self.start_time = mp.Value("d", 0)
+        self.autotracker_enabled = mp.Value("i", autotracker_enabled)  # type: ignore[assignment]
        self.stop_time = mp.Value("d", 0)
        self.frame_time = mp.Value("d", 0)
        self.zoom_level = mp.Value("d", 0)
        self.max_zoom = mp.Value("d", 0)
        self.min_zoom = mp.Value("d", 0)
        self.start_time = mp.Value("d", 0)  # type: ignore[assignment]
        self.stop_time = mp.Value("d", 0)  # type: ignore[assignment]
        self.frame_time = mp.Value("d", 0)  # type: ignore[assignment]
        self.zoom_level = mp.Value("d", 0)  # type: ignore[assignment]
        self.max_zoom = mp.Value("d", 0)  # type: ignore[assignment]
        self.min_zoom = mp.Value("d", 0)  # type: ignore[assignment]
        self.tracking_active = mp.Event()
        self.motor_stopped = mp.Event()
        self.reset = mp.Event()
@@ -104,13 +104,12 @@ class CameraActivityManager:
        all_objects: list[dict[str, Any]] = []
        for camera in new_activity.keys():
-            camera_config = self.config.cameras.get(camera)
+            if camera not in self.config.cameras:
            if camera_config is None:
                continue
            # handle cameras that were added dynamically
            if camera not in self.camera_all_object_counts:
-                self.__init_camera(camera_config)
+                self.__init_camera(self.config.cameras[camera])
            new_objects = new_activity[camera].get("objects", [])
            all_objects.extend(new_objects)
@@ -235,13 +234,12 @@ class AudioActivityManager:
        now = datetime.datetime.now().timestamp()
        for camera in new_activity.keys():
-            camera_config = self.config.cameras.get(camera)
+            if camera not in self.config.cameras:
            if camera_config is None:
                continue
            # handle cameras that were added dynamically
            if camera not in self.current_audio_detections:
-                self.__init_camera(camera_config)
+                self.__init_camera(self.config.cameras[camera])
            new_detections = new_activity[camera].get("detections", [])
            if self.compare_audio_activity(camera, new_detections, now):
@@ -15,6 +15,7 @@ from frigate.config.camera.updater import (
    CameraConfigUpdateSubscriber,
 )
 from frigate.const import REPLAY_CAMERA_PREFIX
 from frigate.detectors.detector_config import SceneEnum
 from frigate.models import Regions
 from frigate.object_detection.util import detection_frame_size
 from frigate.util.builtin import empty_and_close_queue
@@ -30,7 +31,7 @@ class CameraMaintainer(threading.Thread):
    def __init__(
        self,
        config: FrigateConfig,
-        detection_queues: dict[str, Queue],
+        detection_queues: dict[SceneEnum, Queue],
        detected_frames_queue: Queue,
        camera_metrics: DictProxy,
        ptz_metrics: dict[str, PTZMetrics],
@@ -120,7 +121,9 @@ class CameraMaintainer(threading.Thread):
        if runtime:
            self.camera_metrics[name] = CameraMetrics(self.metrics_manager)
-            self.ptz_metrics[name] = PTZMetrics()
+            self.ptz_metrics[name] = PTZMetrics(
                autotracker_enabled=config.onvif.autotracking.enabled
            )
            self.region_grids[name] = get_camera_regions_grid(
                name,
                config.detect,
@@ -169,10 +172,6 @@ class CameraMaintainer(threading.Thread):
            frame_size = config.frame_shape_yuv[0] * config.frame_shape_yuv[1]
            self.frame_manager.create(f"{config.name}_frame{i}", frame_size)
        # a leftover ledger from an unclean exit may be the wrong length
        self.frame_manager.delete(f"{config.name}_frame_times")
        self.frame_manager.create(f"{config.name}_frame_times", max(count, 1) * 8)
        capture_process = CameraCapture(
            config,
            count,
@@ -16,7 +16,7 @@ from frigate.config import (
    ZoomingModeEnum,
 )
 from frigate.const import CLIPS_DIR, THUMB_DIR
-from frigate.ptz.autotrack import PtzAutoTracker, calculate_max_target_box
+from frigate.ptz.autotrack import PtzAutoTrackerThread
 from frigate.track.tracked_object import TrackedObject
 from frigate.util.image import (
    SharedMemoryFrameManager,
@@ -35,7 +35,7 @@ class CameraState:
        name: str,
        config: FrigateConfig,
        frame_manager: SharedMemoryFrameManager,
-        ptz_autotracker_thread: PtzAutoTracker,
+        ptz_autotracker_thread: PtzAutoTrackerThread,
    ) -> None:
        self.name = name
        self.config = config
@@ -115,13 +115,17 @@ class CameraState:
                # draw thicker box around ptz autotracked object
                if (
                    self.camera_config.onvif.autotracking.enabled
-                    and self.ptz_autotracker_thread.autotracker_init.get(self.name)
+                    and self.ptz_autotracker_thread.ptz_autotracker.autotracker_init.get(
-                    and self.ptz_autotracker_thread.tracked_object[self.name]
+                        self.name
                    )
                    and self.ptz_autotracker_thread.ptz_autotracker.tracked_object[
                        self.name
                    ]
                    is not None
                    and obj["id"]
-                    == self.ptz_autotracker_thread.tracked_object[  # type: ignore[union-attr]
+                    == self.ptz_autotracker_thread.ptz_autotracker.tracked_object[
                        self.name
-                    ].obj_data["id"]
+                    ].obj_data["id"]  # type: ignore[attr-defined]
                    and obj["frame_time"] == frame_time
                ):
                    thickness = 5
@@ -134,9 +138,9 @@ class CameraState:
                        and self.camera_config.detect.width is not None
                        and self.camera_config.detect.height is not None
                    ):
-                        max_target_box = calculate_max_target_box(
+                        max_target_box = self.ptz_autotracker_thread.ptz_autotracker.tracked_object_metrics[
-                            self.camera_config.onvif.autotracking.zoom_factor
+                            self.name
-                        )
+                        ]["max_target_box"]  # type: ignore[index]
                        side_length = max_target_box * (
                            max(
                                self.camera_config.detect.width,
@@ -349,7 +353,7 @@ class CameraState:
            return
        current_frame = self.frame_manager.get(
-            frame_name, self.camera_config.frame_shape_yuv, frame_time
+            frame_name, self.camera_config.frame_shape_yuv
        )
        tracked_objects = self.tracked_objects.copy()
@@ -404,7 +408,7 @@ class CameraState:
            self.send_mqtt_snapshot(new_obj, object_type)
            for c in self.callbacks["start"]:
-                c(self.name, new_obj, frame_name, frame_time)
+                c(self.name, new_obj, frame_name)
        for id in updated_ids:
            updated_obj = tracked_objects[id]
@@ -416,7 +420,7 @@ class CameraState:
            if autotracker_update or significant_update:
                for c in self.callbacks["autotrack"]:
-                    c(self.name, updated_obj, frame_name, frame_time)
+                    c(self.name, updated_obj, frame_name)
            if thumb_update and current_frame is not None:
                # ensure this frame is stored in the cache
@@ -466,7 +470,7 @@ class CameraState:
            ):
                # call event handlers
                for c in self.callbacks["update"]:
-                    c(self.name, updated_obj, frame_name, frame_time)
+                    c(self.name, updated_obj, frame_name)
                updated_obj.last_published = frame_time
            # send MQTT snapshot when object first enters a required zone,
@@ -488,7 +492,7 @@ class CameraState:
                removed_obj.obj_data["end_time"] = frame_time
                logger.debug(f"{self.name}: end callback for object {id}")
                for c in self.callbacks["end"]:
-                    c(self.name, removed_obj, frame_name, frame_time)
+                    c(self.name, removed_obj, frame_name)
        # TODO: can i switch to looking this up and only changing when an event ends?
        # maintain best objects
@@ -603,6 +607,9 @@ class CameraState:
                self.current_frame_time = frame_time
                self._current_frame = np.copy(current_frame)
                if self.previous_frame_id is not None:
                    self.frame_manager.close(self.previous_frame_id)
            self.previous_frame_id = frame_name
    def send_mqtt_snapshot(self, new_obj: TrackedObject, object_type: str) -> None:
@@ -12,7 +12,6 @@ class DetectionTypeEnum(str, Enum):
    video = "video"
    audio = "audio"
    lpr = "lpr"
    classification_state = "classification_state"
 class DetectionPublisher(Publisher):
@@ -759,13 +759,15 @@ class Dispatcher:
                    "Autotracking must be enabled in the config to be turned on via MQTT."
                )
                return
-            if not ptz_autotracker_settings.enabled:
+            if not self.ptz_metrics[camera_name].autotracker_enabled.value:
                logger.info(f"Turning on ptz autotracker for {camera_name}")
                self.ptz_metrics[camera_name].autotracker_enabled.value = True
                self.ptz_metrics[camera_name].start_time.value = 0
                ptz_autotracker_settings.enabled = True
        elif payload == "OFF":
-            if ptz_autotracker_settings.enabled:
+            if self.ptz_metrics[camera_name].autotracker_enabled.value:
                logger.info(f"Turning off ptz autotracker for {camera_name}")
                self.ptz_metrics[camera_name].autotracker_enabled.value = False
                self.ptz_metrics[camera_name].start_time.value = 0
                ptz_autotracker_settings.enabled = False
@@ -780,9 +782,7 @@ class Dispatcher:
        try:
            payload = int(payload)
        except ValueError:
-            logger.warning(
+            f"Received unsupported value for motion contour area: {payload}"
                f"Received unsupported value for motion contour area: {payload}"
            )
            return
        motion_settings = self.config.cameras[camera_name].motion
@@ -799,9 +799,7 @@ class Dispatcher:
        try:
            payload = int(payload)
        except ValueError:
-            logger.warning(
+            f"Received unsupported value for motion threshold: {payload}"
                f"Received unsupported value for motion threshold: {payload}"
            )
            return
        motion_settings = self.config.cameras[camera_name].motion
@@ -816,9 +814,7 @@ class Dispatcher:
    def _on_global_notification_command(self, payload: str) -> None:
        """Callback for global notification topic."""
        if payload != "ON" and payload != "OFF":
-            logger.warning(
+            f"Received unsupported value for all notification: {payload}"
                f"Received unsupported value for all notification: {payload}"
            )
            return
        notification_settings = self.config.notifications
@@ -8,16 +8,7 @@ from .zmq_proxy import Publisher, Subscriber
 class EventUpdatePublisher(
-    Publisher[
+    Publisher[tuple[EventTypeEnum, EventStateEnum, str | None, str, dict[str, Any]]]
        tuple[
            EventTypeEnum,
            EventStateEnum,
            str | None,
            str,
            dict[str, Any],
            float | None,
        ]
    ]
 ):
    """Publishes events (objects, audio, manual)."""
@@ -28,14 +19,7 @@ class EventUpdatePublisher(
    def publish(
        self,
-        payload: tuple[
+        payload: tuple[EventTypeEnum, EventStateEnum, str | None, str, dict[str, Any]],
            EventTypeEnum,
            EventStateEnum,
            str | None,
            str,
            dict[str, Any],
            float | None,
        ],
        sub_topic: str = "",
    ) -> None:
        super().publish(payload, sub_topic)
@@ -2,8 +2,6 @@ from __future__ import annotations
 import logging
 import queue
 import selectors
 import socket
 import threading
 import time
 from collections.abc import Callable
@@ -58,11 +56,6 @@ class MqttClient(Communicator):
        self._next_connect_time = 0.0
        self._last_on_connect_dispatch = 0.0
        # lets other threads interrupt the worker's socket wait
        self._wake_recv, self._wake_send = socket.socketpair()
        self._wake_recv.setblocking(False)
        self._wake_send.setblocking(False)
    def subscribe(self, receiver: Callable) -> None:
        """Wrapper for allowing dispatcher to subscribe."""
        self._dispatcher = receiver
@@ -92,7 +85,6 @@ class MqttClient(Communicator):
            return
        self._publish_queue.put(QueuedPublish(full_topic, payload, retain))
        self._wake_worker()
    def stop(self) -> None:
        if self._worker is None:
@@ -108,11 +100,9 @@ class MqttClient(Communicator):
                    publish_done,
                )
            )
            self._wake_worker()
            publish_done.wait(MQTT_SHUTDOWN_FLUSH_TIMEOUT)
        self._stop_event.set()
        self._wake_worker()
        if self.client is not None:
            try:
@@ -368,7 +358,7 @@ class MqttClient(Communicator):
            deadline = time.monotonic() + MQTT_SHUTDOWN_FLUSH_TIMEOUT
            while not message_info.is_published() and time.monotonic() < deadline:
                if (
-                    self._loop_client(timeout=MQTT_PUBLISH_WAIT_INTERVAL)
+                    self.client.loop(timeout=MQTT_PUBLISH_WAIT_INTERVAL)
                    != mqtt.MQTT_ERR_SUCCESS
                ):
                    break
@@ -378,51 +368,6 @@ class MqttClient(Communicator):
                exc_info=True,
            )
    def _wake_worker(self) -> None:
        try:
            self._wake_send.send(b"\0")
        except BlockingIOError:
            # the buffer is full, so a wake is already pending
            pass
    def _loop_client(self, timeout: float) -> int:
        """Drive Paho without select()'s limit on socket file descriptors."""
        assert self.client is not None
        client = self.client
        sock = client.socket()
        if sock is None:
            return mqtt.MQTT_ERR_NO_CONN
        events = selectors.EVENT_READ
        if client.want_write():
            events |= selectors.EVENT_WRITE
        # TLS can have decrypted bytes buffered even when the socket is not ready.
        pending = hasattr(sock, "pending") and sock.pending() > 0
        with selectors.DefaultSelector() as selector:
            selector.register(sock, events)
            selector.register(self._wake_recv, selectors.EVENT_READ)
            ready = {
                key.fileobj: mask
                for key, mask in selector.select(0.0 if pending else timeout)
            }
        if self._wake_recv in ready:
            try:
                self._wake_recv.recv(4096)
            except BlockingIOError:
                pass
        ready_events = ready.get(sock, 0)
        if pending or ready_events & selectors.EVENT_READ:
            result = client.loop_read()
            if result != mqtt.MQTT_ERR_SUCCESS or client.socket() is None:
                return result
        if ready_events & selectors.EVENT_WRITE:
            result = client.loop_write()
            if result != mqtt.MQTT_ERR_SUCCESS or client.socket() is None:
                return result
        return client.loop_misc()
    def _mqtt_loop_worker(self) -> None:
        # The worker owns all socket I/O so reconnect, subscribe, and publish
        # ordering stays serialized in one place.
@@ -439,7 +384,7 @@ class MqttClient(Communicator):
            assert self.client is not None
            try:
-                result = self._loop_client(timeout=MQTT_LOOP_TIMEOUT)
+                result = self.client.loop(timeout=MQTT_LOOP_TIMEOUT)
            except (OSError, mqtt.WebsocketConnectionError) as err:
                logger.warning("MQTT loop error: %s", err)
                self._schedule_reconnect()
@@ -672,7 +617,7 @@ class MqttClient(Communicator):
                return
            try:
-                result = self._loop_client(timeout=MQTT_PUBLISH_WAIT_INTERVAL)
+                result = self.client.loop(timeout=MQTT_PUBLISH_WAIT_INTERVAL)
            except (OSError, mqtt.WebsocketConnectionError) as err:
                logger.warning("MQTT publish wait failed: %s", err)
                self._schedule_reconnect()
@@ -6,7 +6,7 @@ import logging
 import os
 import queue
 import threading
-from collections.abc import Callable, Iterator
+from collections.abc import Callable
 from dataclasses import dataclass
 from multiprocessing.synchronize import Event as MpEvent
 from typing import Any
@@ -72,7 +72,7 @@ class WebPushClient(Communicator):
        # Pull keys from PEM or generate if they do not exist
        self.vapid = Vapid01.from_file(os.path.join(CONFIG_DIR, "notifications.pem"))
-        users: Iterator[dict[str, Any]] = (
+        users: list[dict[str, Any]] = (
            User.select(User.username, User.notification_tokens).dicts().iterator()
        )
        for user in users:
@@ -3,9 +3,6 @@
 import errno
 import json
 import logging
 import queue
 import socket
 import struct
 import threading
 from collections.abc import Callable
 from typing import Any
@@ -79,12 +76,6 @@ _WS_VIEWER_TOPICS = frozenset(
 # Camera-scoped command topics a camera-authorized (non-admin) user may send.
 _WS_CAMERA_COMMAND_TOPICS = frozenset({"ptz"})
 # Max outbound messages waiting on a client's writer thread.
 WS_MAX_PENDING_MESSAGES = 256
 # Seconds a single send() may wait for a client to free socket buffer space.
 WS_SEND_TIMEOUT = 10
 def _check_ws_authorization(
    topic: str,
@@ -473,78 +464,6 @@ def _materialize_for_ws(
 class WebSocket(WebSocket_):  # type: ignore[misc]
    def __init__(self, *args: Any, **kwargs: Any) -> None:
        super().__init__(*args, **kwargs)
        self._send_queue: queue.Queue[tuple[Any, bool] | None] = queue.Queue(
            maxsize=WS_MAX_PENDING_MESSAGES
        )
        self._writer: threading.Thread | None = None
        self._aborted = False
    def opened(self) -> None:
        """Set a send timeout on the socket and start this client's writer thread."""
        # ws4py writes close and pong frames on the shared manager thread, so a
        # stalled client must not block sendall forever. SO_SNDTIMEO leaves recv
        # (and idle clients) untouched, unlike settimeout().
        self.sock.setsockopt(
            socket.SOL_SOCKET, socket.SO_SNDTIMEO, struct.pack("ll", WS_SEND_TIMEOUT, 0)
        )
        # every client gets its own writer so a client that stops reading only
        # blocks itself, never the thread that called publish()
        self._writer = threading.Thread(
            target=self._drain_send_queue, name="ws_writer", daemon=True
        )
        self._writer.start()
    def send(self, payload: Any, binary: bool = False) -> None:
        """Queue a message for this client's writer thread.
        Returns without waiting for delivery. Disconnects the client if its queue
        is full.
        """
        try:
            self._send_queue.put_nowait((payload, binary))
        except queue.Full:
            self._abort("Websocket client is not keeping up, disconnecting it")
    def _drain_send_queue(self) -> None:
        while True:
            item = self._send_queue.get()
            if item is None or self.terminated or self.sock is None:
                return
            try:
                super().send(*item)
            except Exception:
                self._abort()
                return
    def _abort(self, reason: str | None = None) -> None:
        # publish() keeps hitting a full queue until the manager thread removes
        # the connection, so only act (and log) the first time
        if self._aborted:
            return
        self._aborted = True
        if reason:
            logger.warning(reason)
        # shutdown rather than close so the ws4py manager thread sees EOF and
        # runs its normal unregister/terminate; this also unblocks a stuck sendall
        sock = self.sock
        if sock is not None:
            try:
                sock.shutdown(socket.SHUT_RDWR)
            except OSError:
                pass
    def closed(self, code: int, reason: str | None = None) -> None:
        """Signal the writer thread to exit."""
        try:
            self._send_queue.put_nowait(None)
        except queue.Full:
            pass
    def unhandled_error(self, error: Any) -> None:
        """
        Handles the unfriendly socket closures on the server side
@@ -678,7 +597,10 @@ class WebSocketClient(Communicator):
            )
            if message is None:
                continue
-            ws.send(message)
+            try:
                ws.send(message)
            except (ConnectionResetError, BrokenPipeError, ValueError):
                pass
    def stop(self) -> None:
        if self.websocket_server is not None:
@@ -1,6 +1,6 @@
 from pydantic import Field, model_validator
-from frigate.detectors.detector_config import DEFAULT_SCENE, SCENE_PATTERN
+from frigate.detectors.detector_config import SceneEnum
 from ..base import FrigateBaseModel
@@ -62,11 +62,10 @@ class DetectConfig(FrigateBaseModel):
        title="Detect width",
        description="Width (pixels) of frames used for the detect stream; leave empty to use the native stream resolution.",
    )
-    scene: str = Field(
+    scene: SceneEnum = Field(
-        default=DEFAULT_SCENE,
+        default=SceneEnum.all,
        pattern=SCENE_PATTERN,
        title="Detect scene",
-        description="The environment this camera looks at, used to pick which of the configured models runs on it. Cameras left on 'default' run the model configured with a scene of 'default'.",
+        description="The environment this camera looks at, used to pick which of the configured models runs on it. Cameras left on 'all' run the model configured with a scene of 'all'.",
    )
    fps: int = Field(
        default=5,
@@ -3,9 +3,9 @@ from enum import Enum
 from pydantic import Field, field_validator
 from frigate.util.config import resolve_ffmpeg_path
 from frigate.util.env import EnvString
 from ..base import FrigateBaseModel
 from ..env import EnvString
 __all__ = [
    "CameraFfmpegConfig",
@@ -1,11 +1,10 @@
 from enum import Enum
-from typing import Any, Self
+from typing import Any
-from pydantic import Field, model_validator
+from pydantic import Field
 from frigate.util.env import EnvString
 from ..base import FrigateBaseModel
 from ..env import EnvString
 __all__ = ["GenAIConfig", "GenAIProviderEnum", "GenAIRoleEnum"]
@@ -22,17 +21,6 @@ class GenAIRoleEnum(str, Enum):
    chat = "chat"
    descriptions = "descriptions"
    embeddings = "embeddings"
    transcribe = "transcribe"
 # Providers that can accept audio input for the transcribe role. Ollama has no
 # audio input support, so claiming the role there would fail at request time.
 TRANSCRIBE_CAPABLE_PROVIDERS = {
    GenAIProviderEnum.openai,
    GenAIProviderEnum.azure_openai,
    GenAIProviderEnum.gemini,
    GenAIProviderEnum.llamacpp,
 }
 class GenAIConfig(FrigateBaseModel):
@@ -64,7 +52,7 @@ class GenAIConfig(FrigateBaseModel):
            GenAIRoleEnum.chat,
        ],
        title="Roles",
-        description="GenAI roles (chat, descriptions, embeddings, transcribe); one provider per role. Only chat, descriptions, and embeddings are granted by default; transcribe must be listed explicitly.",
+        description="GenAI roles (chat, descriptions, embeddings); one provider per role.",
    )
    provider_options: dict[str, Any] = Field(
        default={},
@@ -78,17 +66,3 @@ class GenAIConfig(FrigateBaseModel):
        description="Runtime options passed to the provider for each inference call.",
        json_schema_extra={"additionalProperties": {}},
    )
    @model_validator(mode="after")
    def validate_transcribe_provider(self) -> Self:
        """Reject the transcribe role on providers that cannot accept audio input."""
        if (
            GenAIRoleEnum.transcribe in self.roles
            and self.provider not in TRANSCRIBE_CAPABLE_PROVIDERS
        ):
            raise ValueError(
                f"GenAI provider '{self.provider.value}' does not support audio input "
                "and cannot be given the 'transcribe' role."
            )
        return self
@@ -1,57 +1,8 @@
-from pydantic import Field, field_validator
+from pydantic import Field
 from frigate.util.live_streams import DEFAULT_TRANSCODE_QUALITIES
 from ..base import FrigateBaseModel
-__all__ = ["CameraLiveConfig", "LiveTranscodeConfig", "LiveTranscodeQualityConfig"]
+__all__ = ["CameraLiveConfig"]
 class LiveTranscodeQualityConfig(FrigateBaseModel):
    height: int = Field(
        ge=144,
        le=2160,
        title="Height",
        description="Output height in pixels; width follows the source aspect ratio.",
    )
    bitrate: int = Field(
        ge=64,
        title="Bitrate",
        description="Target and maximum video bitrate in kbps.",
    )
 class LiveTranscodeConfig(FrigateBaseModel):
    enabled: bool = Field(
        default=False,
        title="Enable transcoded streams",
        description="Add lower-quality live streams that go2rtc transcodes in real time while someone is watching.",
    )
    source: str | None = Field(
        default=None,
        title="Source stream",
        description="go2rtc stream to transcode. Defaults to the first live stream.",
    )
    qualities: list[LiveTranscodeQualityConfig] = Field(
        default_factory=lambda: [
            LiveTranscodeQualityConfig(**quality)
            for quality in DEFAULT_TRANSCODE_QUALITIES
        ],
        title="Qualities",
        description="One transcoded stream is added per quality.",
    )
    @field_validator("qualities")
    @classmethod
    def validate_unique_heights(
        cls, qualities: list[LiveTranscodeQualityConfig]
    ) -> list[LiveTranscodeQualityConfig]:
        heights = [quality.height for quality in qualities]
        if len(heights) != len(set(heights)):
            raise ValueError("Transcoded stream heights must be unique.")
        return qualities
 class CameraLiveConfig(FrigateBaseModel):
@@ -60,11 +11,6 @@ class CameraLiveConfig(FrigateBaseModel):
        title="Live stream names",
        description="Mapping of configured stream names to restream/go2rtc names used for live playback.",
    )
    transcode: LiveTranscodeConfig = Field(
        default_factory=LiveTranscodeConfig,
        title="Transcoded streams",
        description="Lower-quality live streams transcoded on demand by go2rtc.",
    )
    height: int = Field(
        default=720,
        title="Live height",
@@ -1,7 +1,5 @@
 from pydantic import Field
 from frigate.util.env import EnvString
 from ..base import FrigateBaseModel
 __all__ = ["NotificationConfig"]
@@ -13,7 +11,7 @@ class NotificationConfig(FrigateBaseModel):
        title="Enable notifications",
        description="Enable or disable notifications for all cameras; can be overridden per-camera.",
    )
-    email: EnvString | None = Field(
+    email: str | None = Field(
        default=None,
        title="Notification email",
        description="Email address used for push notifications or required by certain notification providers.",
@@ -2,9 +2,8 @@ from enum import Enum
 from pydantic import Field, field_validator
 from frigate.util.env import EnvString
 from ..base import FrigateBaseModel
 from ..env import EnvString
 from .objects import DEFAULT_TRACKED_OBJECTS
 __all__ = ["OnvifConfig", "PtzAutotrackConfig", "ZoomingModeEnum"]
@@ -9,7 +9,6 @@ __all__ = [
    "DetectionsConfig",
    "AlertsConfig",
    "ImageSourceEnum",
    "ReviewFrameModeEnum",
    "ReviewResponseStyleEnum",
 ]
@@ -21,13 +20,6 @@ class ImageSourceEnum(str, Enum):
    recordings = "recordings"
 class ReviewFrameModeEnum(str, Enum):
    """How review frames are presented to the GenAI provider."""
    frames = "frames"
    annotated_frames = "annotated_frames"
 class ReviewResponseStyleEnum(str, Enum):
    """Writing style presets for GenAI review descriptions."""
@@ -161,11 +153,6 @@ class GenAIReviewConfig(FrigateBaseModel):
        description="Preferred language to request from the GenAI provider for generated responses.",
        default=None,
    )
    frame_mode: ReviewFrameModeEnum = Field(
        default=ReviewFrameModeEnum.frames,
        title="Frame mode",
        description="How frames are presented to the model. 'frames' sends the prompt followed by the frames, which suits models that track a sequence well on their own. 'annotated_frames' labels each frame and interleaves notes derived from object tracking, which helps models that lose track of activity that repeats or reverses.",
    )
    response_style: ReviewResponseStyleEnum = Field(
        default=ReviewResponseStyleEnum.default,
        title="Response style",
@@ -5,7 +5,6 @@ from pydantic import ConfigDict, Field, field_validator
 from .base import FrigateBaseModel
 __all__ = [
    "AudioTranscriptionModelEnum",
    "CameraFaceRecognitionConfig",
    "CameraLicensePlateRecognitionConfig",
    "CameraAudioTranscriptionConfig",
@@ -21,10 +20,6 @@ class SemanticSearchModelEnum(str, Enum):
    jinav2 = "jinav2"
 class AudioTranscriptionModelEnum(str, Enum):
    whisper = "whisper"
 class EnrichmentsDeviceEnum(str, Enum):
    GPU = "GPU"
    CPU = "CPU"
@@ -58,35 +53,10 @@ class AudioTranscriptionConfig(FrigateBaseModel):
        description="Enable or disable automatic audio transcription for all cameras; can be overridden per-camera.",
    )
    language: str = Field(
-        default="auto",
+        default="en",
        title="Transcription language",
-        description="Language code used for transcription/translation (for example 'en' for English), or 'auto' to let the model detect it. See https://whisper-api.com/docs/languages/ for supported language codes.",
+        description="Language code used for transcription/translation (for example 'en' for English). See https://whisper-api.com/docs/languages/ for supported language codes.",
    )
    model: AudioTranscriptionModelEnum | str | None = Field(
        default=AudioTranscriptionModelEnum.whisper,
        title="Audio transcription model or GenAI provider name",
        description="The transcription backend: 'whisper' for Frigate's built-in local models, or the name of a GenAI provider with the transcribe role.",
    )
    @field_validator("model", mode="before")
    @classmethod
    def coerce_model_enum(cls, v):
        # An absent value ("model:" with nothing after it, or an explicit null)
        # means unspecified, so fall back to the built-in backend. Left as None
        # it would pass the GenAI-provider validation, which only inspects
        # strings, and then be treated as a provider name that resolves to no
        # client, turning transcription into a silent no-op.
        if v is None or (isinstance(v, str) and not v.strip()):
            return AudioTranscriptionModelEnum.whisper
        if isinstance(v, str):
            try:
                return AudioTranscriptionModelEnum(v)
            except ValueError:
                return v
        return v
    device: EnrichmentsDeviceEnum = Field(
        default=EnrichmentsDeviceEnum.CPU,
        title="Transcription device",
@@ -19,7 +19,7 @@ from ruamel.yaml import YAML
 from frigate.const import REGEX_JSON
 from frigate.detectors import ModelConfig
-from frigate.detectors.detector_config import DEFAULT_SCENE
+from frigate.detectors.detector_config import SceneEnum
 from frigate.detectors.device import DeviceParseError, DeviceSpec, parse_device
 from frigate.plus import PlusApi
 from frigate.util.builtin import (
@@ -34,15 +34,7 @@ from frigate.util.config import (
    get_relative_coordinates,
    migrate_frigate_config,
 )
 from frigate.util.env import EnvVars, reload_sources
 from frigate.util.image import create_mask
 from frigate.util.live_streams import (
    default_transcode_source,
    is_transcode_stream_name,
    transcode_stream_name,
    transcode_streams,
 )
 from frigate.util.runtime_deps import sha256_of
 from frigate.util.services import auto_detect_hwaccel
 from .auth import AuthConfig
@@ -64,7 +56,6 @@ from .camera.timestamp import TimestampStyleConfig
 from .camera_group import CameraGroupConfig
 from .classification import (
    AudioTranscriptionConfig,
    AudioTranscriptionModelEnum,
    ClassificationConfig,
    FaceRecognitionConfig,
    LicensePlateRecognitionConfig,
@@ -72,6 +63,7 @@ from .classification import (
    SemanticSearchModelEnum,
 )
 from .database import DatabaseConfig
 from .env import EnvVars, reload_sources
 from .logger import LoggerConfig
 from .mqtt import MqttConfig
 from .network import NetworkingConfig
@@ -289,78 +281,11 @@ def verify_config_roles(camera_config: CameraConfig) -> None:
        )
 def apply_live_transcode_streams(
    frigate_config: FrigateConfig, camera_config: CameraConfig
 ) -> None:
    """Fold a camera's transcoded streams into its live stream list.
    Enabled qualities missing from live.streams are appended, and entries the
    user placed keep their position. Transcoded names that are no longer
    generated are dropped unless they name a real go2rtc stream.
    """
    live = camera_config.live
    transcode = live.transcode
    go2rtc_streams = frigate_config.go2rtc.model_dump().get("streams") or {}
    generated: dict[str, str] = {}
    if transcode.enabled:
        if transcode.source is None:
            transcode.source = default_transcode_source(
                camera_config.name, live.streams
            )
        if transcode.source not in go2rtc_streams:
            raise ValueError(
                f"Camera {camera_config.name} has transcoded streams enabled, but its source {transcode.source} is not a go2rtc stream."
            )
        generated = transcode_streams(
            camera_config.name,
            transcode.source,
            [quality.model_dump() for quality in transcode.qualities],
        )
        for name in generated:
            if name in go2rtc_streams:
                raise ValueError(
                    f"Camera {camera_config.name} generates transcoded stream {name}, which collides with a go2rtc stream of the same name."
                )
    streams = {
        label: name
        for label, name in live.streams.items()
        if name in generated
        or name in go2rtc_streams
        or not is_transcode_stream_name(camera_config.name, name)
    }
    placed = set(streams.values())
    for quality in transcode.qualities if transcode.enabled else []:
        name = transcode_stream_name(camera_config.name, quality.height)
        if name in placed:
            continue
        label = f"{quality.height}p"
        if label in streams:
            raise ValueError(
                f"Camera {camera_config.name} already has a live stream named {label}; rename it or place the transcoded stream under another name."
            )
        streams[label] = name
    live.streams = streams
 def verify_valid_live_stream_names(
    frigate_config: FrigateConfig, camera_config: CameraConfig
 ) -> ValueError | None:
    """Verify that a restream exists to use for live view."""
    for _, stream_name in camera_config.live.streams.items():
        if is_transcode_stream_name(camera_config.name, stream_name):
            continue
        if (
            stream_name
            not in frigate_config.go2rtc.model_dump().get("streams", {}).keys()
@@ -610,7 +535,7 @@ class FrigateConfig(FrigateBaseModel):
    models: list[ModelConfig] = Field(
        default_factory=_default_models,
        title="Detection models",
-        description="Object detection models and the hardware each one runs on. Cameras pick a model by matching their detect.scene against a model's scene, falling back to the 'default' model.",
+        description="Object detection models and the hardware each one runs on. Cameras pick a model by matching their detect.scene against a model's scene.",
    )
    # GenAI config (named provider configs: name -> GenAIConfig)
@@ -725,9 +650,7 @@ class FrigateConfig(FrigateBaseModel):
    )
    _plus_api: PlusApi
-    _model_devices: dict[str, list[DeviceSpec]]
+    _model_devices: dict[SceneEnum, list[DeviceSpec]]
    # scene -> model, including the scenes of duplicate models folded into another
    _scene_models: dict[str, ModelConfig]
    _camera_models: dict[str, ModelConfig]
    _all_attributes: list[str]
    _all_attribute_logos: list[str]
@@ -762,7 +685,7 @@ class FrigateConfig(FrigateBaseModel):
    def primary_model(self) -> ModelConfig:
        """The model used when no specific camera is in play."""
        for model in self.models:
-            if model.scene == DEFAULT_SCENE:
+            if model.scene == SceneEnum.all:
                return model
        return self.models[0]
@@ -784,7 +707,7 @@ class FrigateConfig(FrigateBaseModel):
        if model is None:
            camera = self.cameras.get(camera_name)
-            scene = camera.detect.scene if camera is not None else DEFAULT_SCENE
+            scene = camera.detect.scene if camera is not None else SceneEnum.all
            model = self._resolve_camera_model(camera_name, scene)
            self._camera_models[camera_name] = model
@@ -844,14 +767,14 @@ class FrigateConfig(FrigateBaseModel):
        if not self.models:
            raise ValueError("At least one model must be configured under models")
-        model_devices: dict[str, list[DeviceSpec]] = {}
+        model_devices: dict[SceneEnum, list[DeviceSpec]] = {}
        # device string -> the scene of the model that already claimed it
-        claimed_devices: dict[str, str] = {}
+        claimed_devices: dict[str, SceneEnum] = {}
        for index, model in enumerate(self.models):
-            scene = model.scene
+            scene = model.scene.value
-            if scene in model_devices:
+            if model.scene in model_devices:
                raise ValueError(
                    f"Multiple models are configured with a scene of '{scene}'. Each model must use a different scene."
                )
@@ -880,20 +803,17 @@ class FrigateConfig(FrigateBaseModel):
                    other = claimed_devices[device.raw]
                    where = (
                        f"twice by model '{scene}'"
-                        if other == scene
+                        if other == model.scene
-                        else f"by both the '{other}' and '{scene}' models"
+                        else f"by both the '{other.value}' and '{scene}' models"
                    )
                    raise ValueError(
                        f"Device '{device.raw}' is used {where}, but it can only run one detection process."
                    )
-                claimed_devices[device.raw] = scene
+                claimed_devices[device.raw] = model.scene
            self.models[index] = self._load_model(model, devices[0].detector)
-            model_devices[scene] = devices
+            model_devices[model.scene] = devices
        self._scene_models = {model.scene: model for model in self.models}
        self._consolidate_duplicate_models(model_devices)
        attributes: set[str] = set()
        attribute_logos: set[str] = set()
@@ -917,107 +837,36 @@ class FrigateConfig(FrigateBaseModel):
        }
        self._all_labels = labels
-    def _consolidate_duplicate_models(
+    def _resolve_camera_model(self, name: str, scene: SceneEnum) -> ModelConfig:
        self, model_devices: dict[str, list[DeviceSpec]]
    ) -> None:
        """Fold models that load the same model file into a single model.
        Separate scenes for one model only split the same work across separate
        detection queues, so each device serves fewer cameras and is slower
        overall than one shared model. The duplicate's devices are moved to the
        model it duplicates and its scene resolves to that model.
        Args:
            model_devices: Scene to parsed devices, updated in place
        """
        kept: list[ModelConfig] = []
        hashes: dict[str, str | None] = {}
        def file_hash(path: str) -> str | None:
            if path not in hashes:
                hashes[path] = sha256_of(path) if os.path.isfile(path) else None
            return hashes[path]
        def same_model(a: ModelConfig, b: ModelConfig) -> bool:
            # a model's devices all share a detector, so folding across
            # detectors would produce an invalid model
            if model_devices[a.scene][0].detector != model_devices[b.scene][0].detector:
                return False
            if not a.path or not b.path:
                return False
            if os.path.realpath(a.path) == os.path.realpath(b.path):
                return True
            a_hash = file_hash(a.path)
            return a_hash is not None and a_hash == file_hash(b.path)
        for model in self.models:
            original = next((other for other in kept if same_model(other, model)), None)
            if original is None:
                kept.append(model)
                continue
            # keep the default model so cameras without a scene still find it
            if model.scene == DEFAULT_SCENE:
                kept[kept.index(original)] = model
                original, model = model, original
            logger.warning(
                "Models '%s' and '%s' use the same model file, so they have been combined into the '%s' model. Defining one model under several scenes to assign detectors to specific cameras is slower and less efficient than letting every detector serve every camera. Remove the '%s' model and list its devices under the '%s' model instead",
                original.scene,
                model.scene,
                original.scene,
                model.scene,
                original.scene,
            )
            original.devices = [*original.devices, *model.devices]
            model_devices[original.scene] = [
                *model_devices[original.scene],
                *model_devices.pop(model.scene),
            ]
            self._scene_models[model.scene] = original
            # anything already folded into the duplicate follows it
            for scene, target in self._scene_models.items():
                if target is model:
                    self._scene_models[scene] = original
        self.models = kept
    def _resolve_camera_model(self, name: str, scene: str) -> ModelConfig:
        """Resolve which model a camera runs on.
        A camera may name a scene no model is configured for, which is valid as
-        long as a 'default' model is there to fall back to.
+        long as an 'all' model is there to fall back to.
        Args:
            name: Name of the camera
-            scene: The camera's detect scene, which defaults to 'default'
+            scene: The camera's detect scene, which defaults to 'all'
        Returns:
            The model the camera runs on
        """
-        model = self._scene_models.get(scene)
+        by_scene = {model.scene: model for model in self.models}
        model = by_scene.get(scene)
        if model is not None:
            return model
-        default = self._scene_models.get(DEFAULT_SCENE)
+        default = by_scene.get(SceneEnum.all)
        if default is None:
            raise ValueError(
-                f"Camera '{name}' has a detect scene of '{scene}', but no model is configured for that scene or for '{DEFAULT_SCENE}'."
+                f"Camera '{name}' has a detect scene of '{scene.value}', but no model is configured for that scene or for 'all'."
            )
        logger.warning(
-            "Camera '%s' has a detect scene of '%s', but no model is configured for that scene, so the '%s' model is used",
+            "Camera '%s' has a detect scene of '%s', but no model is configured for that scene, so the 'all' model is used",
            name,
-            scene,
+            scene.value,
            DEFAULT_SCENE,
        )
        return default
@@ -1035,7 +884,7 @@ class FrigateConfig(FrigateBaseModel):
        # set notifications state
        self.notifications.enabled_in_config = self.notifications.enabled
-        # validate genai: each role (chat, descriptions, embeddings, transcribe) at most once
+        # validate genai: each role (chat, descriptions, embeddings) at most once
        role_to_name: dict[GenAIRoleEnum, str] = {}
        for name, genai_cfg in self.genai.items():
            for role in genai_cfg.roles:
@@ -1128,8 +977,6 @@ class FrigateConfig(FrigateBaseModel):
                "face_recognition": ["enabled", "min_area"],
                "lpr": ["enabled", "expire_time", "min_area", "enhancement"],
                "audio_transcription": ["enabled", "live_enabled"],
                # transcode is camera-level only
                "live": ["streams", "height", "quality"],
            }
            for section in allowed_fields_map:
@@ -1150,10 +997,6 @@ class FrigateConfig(FrigateBaseModel):
            camera_model = self._resolve_camera_model(name, camera_config.detect.scene)
            self._camera_models[name] = camera_model
            # point cameras at the model their duplicate scene was folded into
            if camera_config.detect.scene in self._scene_models:
                camera_config.detect.scene = camera_model.scene
            if camera_config.ffmpeg.hwaccel_args == "auto":
                camera_config.ffmpeg.hwaccel_args = self.ffmpeg.hwaccel_args
@@ -1362,8 +1205,6 @@ class FrigateConfig(FrigateBaseModel):
            if not camera_config.live.streams:
                camera_config.live.streams = {name: name}
            apply_live_transcode_streams(self, camera_config)
            # generate the ffmpeg commands
            camera_config.create_ffmpeg_cmds()
            self.cameras[name] = camera_config
@@ -1404,35 +1245,6 @@ class FrigateConfig(FrigateBaseModel):
        for model in self.models:
            model.create_colormap(colored_labels)
        # validate audio_transcription.model when it is a GenAI provider name.
        # this runs here rather than beside the semantic_search check because the
        # global->camera merge above is what resolves camera-level enablement.
        transcription_active = self.audio_transcription.enabled or any(
            camera.audio_transcription.enabled for camera in self.cameras.values()
        )
        if (
            transcription_active
            and isinstance(self.audio_transcription.model, str)
            and not isinstance(
                self.audio_transcription.model, AudioTranscriptionModelEnum
            )
        ):
            if self.audio_transcription.model not in self.genai:
                raise ValueError(
                    f"audio_transcription.model '{self.audio_transcription.model}' is not a "
                    "valid GenAI config key. Must match a key in genai config."
                )
            if (
                GenAIRoleEnum.transcribe
                not in self.genai[self.audio_transcription.model].roles
            ):
                raise ValueError(
                    f"GenAI provider '{self.audio_transcription.model}' must have "
                    "'transcribe' in its roles for audio transcription."
                )
        # Check audio transcription and audio detection requirements
        if self.audio_transcription.enabled:
            # If audio transcription is enabled globally, at least one camera must have audio detection enabled
@@ -3,9 +3,9 @@ from typing import Self
 from pydantic import Field, ValidationInfo, model_validator
 from frigate.const import FREQUENCY_STATS_POINTS
 from frigate.util.env import EnvString
 from .base import FrigateBaseModel
 from .env import EnvString
 __all__ = ["MqttConfig"]
@@ -1,8 +1,7 @@
 from pydantic import Field, field_validator
 from frigate.util.env import EnvString
 from .base import FrigateBaseModel
 from .env import EnvString
 __all__ = ["ProxyConfig", "HeaderMappingConfig"]
@@ -8,7 +8,6 @@ MODEL_CACHE_DIR = f"{CONFIG_DIR}/model_cache"
 BASE_DIR = "/media/frigate"
 CLIPS_DIR = f"{BASE_DIR}/clips"
 EXPORT_DIR = f"{BASE_DIR}/exports"
 EXPORT_STAGING_DIR = f"{EXPORT_DIR}/staging"
 FACE_DIR = f"{CLIPS_DIR}/faces"
 THUMB_DIR = f"{CLIPS_DIR}/thumbs"
 RECORD_DIR = f"{BASE_DIR}/recordings"
@@ -176,7 +176,6 @@ class LicensePlateProcessingMixin:
        """
        input_shape = [3, 48, 320]
        num_images = len(images)
        outputs: list[np.ndarray] = []
        for index in range(0, num_images, self.batch_size):
            input_h, input_w = input_shape[1], input_shape[2]
@@ -196,11 +195,11 @@ class LicensePlateProcessingMixin:
                norm_image = norm_image[np.newaxis, :]
                norm_images.append(norm_image)
-            try:
+        try:
-                outputs.extend(self.model_runner.recognition_model(norm_images))  # type: ignore[arg-type]
+            outputs = self.model_runner.recognition_model(norm_images)  # type: ignore[arg-type]
-            except Exception as e:
+        except Exception as e:
-                logger.warning(f"Error running LPR recognition model: {e}")
+            logger.warning(f"Error running LPR recognition model: {e}")
-                return [], []
+            return [], []
        return self.ctc_decoder(outputs)
@@ -580,8 +579,8 @@ class LicensePlateProcessingMixin:
        boxes = []
        scores = []
-        for index in range(len(contours)):
+        for index in range(len(contours)):  # type: ignore[arg-type]
-            contour = contours[index]
+            contour = contours[index]  # type: ignore[index]
            # get minimum bounding box (rotated rectangle) around the contour and the smallest side length.
            points, sside = self._get_min_boxes(contour)
@@ -1222,7 +1221,7 @@ class LicensePlateProcessingMixin:
        """Look for license plates in image."""
        self.metrics.alpr_pps.value = self.plates_rec_second.eps()
        self.metrics.yolov9_lpr_pps.value = self.plates_det_second.eps()
-        camera: str = obj_data if dedicated_lpr else obj_data["camera"]  # type: ignore[assignment]
+        camera = obj_data if dedicated_lpr else obj_data["camera"]
        current_time = int(datetime.datetime.now().timestamp())
        debug_frame_id = int(datetime.datetime.now().timestamp() * 1000)
@@ -10,7 +10,6 @@ from peewee import DoesNotExist
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.config import FrigateConfig
 from frigate.config.classification import AudioTranscriptionModelEnum
 from frigate.const import (
    CACHE_DIR,
    MODEL_CACHE_DIR,
@@ -19,13 +18,8 @@ from frigate.const import (
 )
 from frigate.data_processing.types import PostProcessDataEnum
 from frigate.embeddings.embeddings import Embeddings
 from frigate.genai.manager import GenAIClientManager
 from frigate.types import TrackedObjectUpdateTypesEnum
-from frigate.util.audio import (
+from frigate.util.audio import get_audio_from_recording
    clean_transcript,
    get_audio_from_recording,
    resolve_language,
 )
 from ..types import DataProcessorMetrics
 from .api import PostProcessorApi
@@ -40,25 +34,15 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
        requestor: InterProcessRequestor,
        embeddings: Embeddings,
        metrics: DataProcessorMetrics,
        genai_manager: GenAIClientManager | None = None,
    ):
        super().__init__(config, metrics, None)
        self.config = config
        self.requestor = requestor
        self.embeddings = embeddings
        self.genai_manager = genai_manager
        self.recognizer = None
        self.transcription_lock = threading.Lock()
        self.transcription_thread: threading.Thread | None = None
        self.transcription_running = False
        self._use_genai = not isinstance(
            config.audio_transcription.model, AudioTranscriptionModelEnum
        )
        if self._use_genai:
            # never build the local recognizer on the GenAI path; WhisperModel
            # downloads several hundred MB on first use
            return
        # faster-whisper handles model downloading automatically
        self.model_path = os.path.join(MODEL_CACHE_DIR, "whisper")
@@ -163,31 +147,6 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
            logger.error(f"Error in audio transcription post-processing: {e}")
    def __transcribe_audio(self, audio_data: bytes) -> str | None:
        """Transcribe WAV audio data with the configured backend."""
        if self._use_genai:
            return self.__transcribe_audio_genai(audio_data)
        return self.__transcribe_audio_whisper(audio_data)
    def __transcribe_audio_genai(self, audio_data: bytes) -> str | None:
        """Hand the WAV bytes to the GenAI provider holding the transcribe role."""
        client = self.genai_manager.transcribe_client if self.genai_manager else None
        if not client:
            logger.error(
                "audio_transcription.model is '%s' (GenAI provider) but no transcribe "
                "client is configured. Ensure the GenAI provider has 'transcribe' in its roles",
                self.config.audio_transcription.model,
            )
            return None
        text = client.transcribe(
            audio_data,
            language=resolve_language(self.config.audio_transcription.language),
        )
        return clean_transcript(text) or None
    def __transcribe_audio_whisper(self, audio_data: bytes) -> str | None:
        """Transcribe WAV audio data using faster-whisper."""
        if not self.recognizer:
            logger.debug("Recognizer not initialized")
@@ -201,7 +160,7 @@ class AudioTranscriptionPostProcessor(PostProcessorApi):
            segments, info = self.recognizer.transcribe(
                temp_wav,
-                language=resolve_language(self.config.audio_transcription.language),
+                language=self.config.audio_transcription.language,
                beam_size=5,
            )
@@ -1,416 +0,0 @@
 """Frame annotations derived from object tracking data.
 Builds short notes describing what changed during a review item, keyed to the
 frames sampled from it. Everything here comes from data already recorded
 (each event's `path_data` trajectory, the timeline's stationary/active
 changes, and the review item's state classification changes), so the notes
 can be stated to the model as fact rather than as something it must perceive.
 """
 import logging
 import math
 from collections.abc import Sequence
 from typing import Any
 from frigate.models import Event, Timeline
 logger = logging.getLogger(__name__)
 # Movement smaller than this (normalized frame units) between two path points
 # is treated as the object holding still rather than travelling.
 STILL_THRESHOLD = 0.02
 # A heading change beyond this (dot product against the leg's own heading)
 # counts as the object turning back rather than curving.
 REVERSAL_DOT = -0.3
 # A run of travel shorter than this (normalized frame units) is treated as
 # milling about rather than going somewhere. Without it, a subject pacing in
 # one spot produces a burst of contradictory "turns around" notes on a single
 # frame.
 MIN_LEG_DISTANCE = 0.08
 # Movement that begins within this many seconds of detection is folded into
 # the detection note, so each arrival reads as one event instead of several.
 DETECT_MOVE_MERGE_SECONDS = 2.0
 STATE_CHANGE_PHRASES = {
    "stationary": "has stopped moving",
    "active": "starts moving again",
 }
 Point = tuple[float, float, float]
 Leg = tuple[int, int]
 def describe_position(x: float, y: float) -> str:
    """Name a normalized frame position in plain terms."""
    horizontal = "left" if x < 0.34 else ("right" if x > 0.66 else "center")
    vertical = "top" if y < 0.34 else ("bottom" if y > 0.66 else "middle")
    if horizontal == "center" and vertical == "middle":
        return "the middle of the frame"
    if horizontal == "center":
        return f"the {vertical} of the frame"
    if vertical == "middle":
        return f"the {horizontal} of the frame"
    return f"the {vertical} {horizontal} of the frame"
 def describe_heading(dx: float, dy: float) -> str:
    """Name a direction of travel in frame terms.
    y grows downward in normalized coordinates, so a falling y reads as moving
    toward the top of the frame.
    """
    parts = []
    if abs(dy) > abs(dx) * 0.4:
        parts.append("down" if dy > 0 else "up")
    if abs(dx) > abs(dy) * 0.4:
        parts.append("right" if dx > 0 else "left")
    return " and ".join(parts) if parts else "in place"
 def event_name(event: dict[str, Any]) -> str:
    """Name an object for the notes, e.g. 'a person' or 'waste bin "Compost"'.
    Objects are never numbered or given track identifiers. Frigate opens a new
    tracked object whenever a subject is re-detected, so the tracking data
    cannot say whether two entries are the same subject, and the notes stay
    ambiguous rather than implying either answer.
    """
    label = str(event["label"]).replace("_", " ").replace("-verified", "")
    sub_label = event.get("sub_label")
    if sub_label:
        return f'{label} "{sub_label}"'
    article = "an" if label[:1].lower() in "aeiou" else "a"
    return f"{article} {label}"
 def describe_classification_change(change: dict[str, Any]) -> str:
    """Phrase a state classification change, e.g. 'front gate changed from
    closed to open'."""
    model = str(change["model"]).replace("_", " ")
    before = str(change["from"]).replace("_", " ")
    after = str(change["to"]).replace("_", " ")
    return f"{model} changed from {before} to {after}"
 def path_legs(points: list[Point]) -> list[Leg]:
    """Split a trajectory into runs of travel in a consistent direction.
    A leg ends when the subject starts moving back against the direction that
    leg established, and only once the leg has covered MIN_LEG_DISTANCE, so
    jitter around a standing subject does not register as a turn.
    Returns (start, end) index pairs into `points`.
    """
    legs: list[Leg] = []
    start = 0
    for i in range(1, len(points)):
        lx = points[i][0] - points[start][0]
        ly = points[i][1] - points[start][1]
        leg_distance = (lx * lx + ly * ly) ** 0.5
        if leg_distance < MIN_LEG_DISTANCE:
            continue
        sx = points[i][0] - points[i - 1][0]
        sy = points[i][1] - points[i - 1][1]
        step = (sx * sx + sy * sy) ** 0.5
        if step < STILL_THRESHOLD:
            continue
        dot = (lx / leg_distance) * (sx / step) + (ly / leg_distance) * (sy / step)
        if dot < REVERSAL_DOT:
            legs.append((start, i - 1))
            start = i - 1
    if start < len(points) - 1:
        legs.append((start, len(points) - 1))
    return [
        (a, b)
        for a, b in legs
        if ((points[b][0] - points[a][0]) ** 2 + (points[b][1] - points[a][1]) ** 2)
        ** 0.5
        >= MIN_LEG_DISTANCE
    ]
 def path_points(path_data: list[Any]) -> list[Point]:
    """Flatten path_data into (x, y, timestamp) tuples, or [] if malformed."""
    try:
        return [(p[0][0], p[0][1], p[1]) for p in path_data or []]
    except (IndexError, TypeError):
        logger.debug("Malformed path_data, skipping trajectory notes")
        return []
 def leg_start_time(points: list[Point], leg: Leg) -> float:
    """When a leg's movement actually began.
    path_data always keeps an object's first two samples, so a leg can open
    with points recorded long before the object moved. The first sample that
    has left the leg's origin is the earliest evidence of movement.
    """
    a, b = leg
    x0, y0, t0 = points[a]
    for x, y, t in points[a + 1 : b + 1]:
        if math.hypot(x - x0, y - y0) >= STILL_THRESHOLD:
            return t
    return t0
 def leg_heading(points: list[Point], leg: Leg) -> str:
    a, b = leg
    return describe_heading(points[b][0] - points[a][0], points[b][1] - points[a][1])
 def leg_phrase(points: list[Point], leg: Leg, first: bool) -> str:
    """Describe the start of a leg, e.g. 'turns around at ... and heads left'."""
    heading = leg_heading(points, leg)
    place = describe_position(points[leg[0]][0], points[leg[0]][1])
    if first:
        return f"starts moving {heading} from {place}"
    return f"turns around at {place} and heads {heading}"
 def path_moments(path_data: list[Any]) -> list[tuple[float, str]]:
    """Key moments in one trajectory as (timestamp, phrase).
    Emits one note per leg of travel. Where the last leg ends is left out:
    path_data only records significant movement, so its final point cannot
    distinguish an object coming to rest from one leaving the frame.
    """
    points = path_points(path_data)
    if len(points) < 2:
        return []
    return [
        (leg_start_time(points, leg), leg_phrase(points, leg, index == 0))
        for index, leg in enumerate(path_legs(points))
    ]
 def build_timeline(
    events: list[dict[str, Any]],
    span_end: float,
    state_changes: Sequence[dict[str, Any]] = (),
 ) -> list[tuple[float, str]]:
    """All annotated moments across every event, in time order.
    Only changes are noted, since those are what sparse frames miss; an
    object's state at the end of the clip is visible in the last frame.
    `span_end` is the timestamp of the last sampled frame, and moments past it
    describe nothing the model can see. A track ending means the object
    stopped being detected, which may or may not mean it left the frame.
    `state_changes` are timeline rows (timestamp, source_id, class_type); the
    stationary and active ones become "has stopped moving" / "starts moving
    again". Frigate only marks an object stationary after it has been still
    for a while, which the past-tense wording reflects.
    Each object keeps its own notes. Folding an object into the note of the
    person moving it ("alongside ...") was tried and made models lose track of
    where the object went.
    """
    changes_by_event: dict[str, list[tuple[float, str]]] = {}
    for change in state_changes:
        phrase = STATE_CHANGE_PHRASES.get(change["class_type"])
        if phrase:
            changes_by_event.setdefault(change["source_id"], []).append(
                (change["timestamp"], phrase)
            )
    timeline: list[tuple[float, str]] = []
    for event in sorted(events, key=lambda e: e["start_time"]):
        # Frame extraction can come up short at the end of a clip, leaving
        # objects that only appear after the last frame we actually have.
        if event["start_time"] > span_end:
            continue
        points = path_points(event.get("path_data") or [])
        legs = path_legs(points) if len(points) >= 2 else []
        name = event_name(event)
        detected_at = event["start_time"]
        where = describe_position(points[0][0], points[0][1]) if points else "the frame"
        merges = bool(legs) and leg_start_time(points, legs[0]) - detected_at <= (
            DETECT_MOVE_MERGE_SECONDS
        )
        remaining = list(enumerate(legs))
        moments: list[tuple[float, str]] = []
        if merges:
            heading = leg_heading(points, legs[0])
            timeline.append(
                (detected_at, f"{name} first detected at {where}, moving {heading}")
            )
            remaining = remaining[1:]
        else:
            timeline.append((detected_at, f"{name} first detected at {where}"))
        for index, leg in remaining:
            moments.append(
                (
                    leg_start_time(points, leg),
                    leg_phrase(points, leg, index == 0),
                )
            )
        moments.extend(
            (timestamp, phrase)
            for timestamp, phrase in changes_by_event.get(event["id"], [])
            if timestamp >= detected_at
        )
        for timestamp, phrase in moments:
            if timestamp <= span_end:
                timeline.append((timestamp, f"{name} {phrase}"))
        if event["end_time"] and event["end_time"] <= span_end:
            timeline.append((event["end_time"], f"{name} is no longer detected"))
    return sorted(timeline, key=lambda m: m[0])
 def annotations_by_frame(
    timeline: list[tuple[float, str]], frame_times: list[float]
 ) -> dict[int, list[str]]:
    """Bucket timeline moments onto the frame that follows each one.
    A moment is attached to the first frame at or after it happened, so the
    note always precedes the image in which the change becomes visible.
    """
    buckets: dict[int, list[str]] = {}
    if not frame_times:
        return buckets
    for timestamp, phrase in timeline:
        index = next(
            (i for i, ft in enumerate(frame_times) if ft >= timestamp),
            len(frame_times) - 1,
        )
        buckets.setdefault(index, []).append(phrase)
    return buckets
 def get_tracked_events(detection_ids: list[str]) -> list[dict[str, Any]]:
    """Load the tracked objects behind a review item's detections."""
    if not detection_ids:
        return []
    rows = list(
        Event.select(
            Event.id,
            Event.label,
            Event.sub_label,
            Event.start_time,
            Event.end_time,
            Event.data,
        )
        .where(Event.id << detection_ids)
        .dicts()
        .iterator()
    )
    return [
        {
            "id": row["id"],
            "label": row["label"],
            "sub_label": row["sub_label"],
            "start_time": row["start_time"],
            "end_time": row["end_time"],
            "path_data": (row["data"] or {}).get("path_data") or [],
        }
        for row in rows
        if row["start_time"] is not None
    ]
 def get_state_changes(detection_ids: list[str]) -> list[dict[str, Any]]:
    """Stationary/active changes the timeline recorded for these objects."""
    if not detection_ids:
        return []
    return list(
        Timeline.select(Timeline.timestamp, Timeline.source_id, Timeline.class_type)
        .where(
            (Timeline.source_id << detection_ids)
            & (Timeline.class_type << list(STATE_CHANGE_PHRASES))
        )
        .dicts()
        .iterator()
    )
 def build_frame_captions(
    detection_ids: list[str],
    frame_times: list[float],
    classification_changes: Sequence[dict[str, Any]] = (),
 ) -> list[str]:
    """A caption for each sampled frame, in frame order.
    Every frame gets its index and elapsed time so the model can tell them
    apart; frames where something changed also carry the tracker and state
    classification notes for that moment. Returns an empty list when there is
    nothing to say, which callers treat as a reason to fall back to sending
    plain frames.
    """
    if not frame_times:
        return []
    span_end = frame_times[-1]
    timeline: list[tuple[float, str]] = []
    events = get_tracked_events(detection_ids)
    # audio and manual review items can have state changes but no tracked objects
    if events:
        timeline.extend(
            (timestamp, f"[tracker] {note}")
            for timestamp, note in build_timeline(
                events, span_end, get_state_changes(detection_ids)
            )
        )
    timeline.extend(
        (change["timestamp"], f"[state] {describe_classification_change(change)}")
        for change in classification_changes
        if change["timestamp"] <= span_end
    )
    buckets = annotations_by_frame(sorted(timeline, key=lambda m: m[0]), frame_times)
    if not buckets:
        return []
    total = len(frame_times)
    origin = frame_times[0]
    captions: list[str] = []
    for index, timestamp in enumerate(frame_times):
        lines = [f"Frame {index + 1} of {total} (+{timestamp - origin:.1f}s):"]
        lines.extend(buckets.get(index, []))
        captions.append("\n".join(lines))
    return captions
@@ -19,11 +19,7 @@ from frigate.comms.embeddings_updater import EmbeddingsRequestEnum
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.config import FrigateConfig
 from frigate.config.camera import CameraConfig
-from frigate.config.camera.review import (
+from frigate.config.camera.review import GenAIReviewConfig, ImageSourceEnum
    GenAIReviewConfig,
    ImageSourceEnum,
    ReviewFrameModeEnum,
 )
 from frigate.const import (
    ATTRIBUTE_LABEL_DISPLAY_MAP,
    CACHE_DIR,
@@ -40,7 +36,6 @@ from frigate.util.image import get_image_from_recording
 from ..post.api import PostProcessorApi
 from ..types import DataProcessorMetrics
 from .review_annotations import build_frame_captions, describe_classification_change
 logger = logging.getLogger(__name__)
@@ -48,7 +43,6 @@ RECORDING_BUFFER_EXTENSION_PERCENT = 0.10
 MIN_RECORDING_DURATION = 10
 MAX_IMAGE_TOKENS = 24000
 MAX_FRAMES_PER_SECOND = 1
 MAX_ANNOTATED_FRAMES = 28
 class ReviewDescriptionProcessor(PostProcessorApi):
@@ -73,7 +67,6 @@ class ReviewDescriptionProcessor(PostProcessorApi):
        duration: float,
        image_source: ImageSourceEnum = ImageSourceEnum.preview,
        height: int = 480,
        frame_mode: ReviewFrameModeEnum = ReviewFrameModeEnum.frames,
    ) -> int:
        """Calculate optimal number of frames based on event duration, context size,
        image source, and resolution.
@@ -87,8 +80,6 @@ class ReviewDescriptionProcessor(PostProcessorApi):
          - MAX_FRAMES_PER_SECOND x duration, to avoid drowning short events in
            near-duplicate frames where the model latches onto the redundant middle
            and skips the start/end action
          - MAX_ANNOTATED_FRAMES in annotated mode, where the tracking notes
            already carry the sequence
        """
        client = self.genai_manager.description_client
@@ -134,10 +125,6 @@ class ReviewDescriptionProcessor(PostProcessorApi):
        max_frames_by_tokens = int(image_token_budget / tokens_per_image)
        max_frames_by_duration = int(duration * MAX_FRAMES_PER_SECOND)
        max_frames = min(max_frames_by_tokens, max_frames_by_duration)
        if frame_mode == ReviewFrameModeEnum.annotated_frames:
            max_frames = min(max_frames, MAX_ANNOTATED_FRAMES)
        return max(max_frames, 3)
    def process_data(
@@ -179,7 +166,6 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                return
            image_source = camera_config.review.genai.image_source
            frame_mode = camera_config.review.genai.frame_mode
            if image_source == ImageSourceEnum.recordings:
                buffer_extension = get_recording_buffer_extension(
@@ -188,43 +174,40 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                final_data["start_time"] -= buffer_extension
                final_data["end_time"] += buffer_extension
-                frames = self.get_recording_frames(
+                thumbs = self.get_recording_frames(
                    camera,
                    final_data["start_time"],
                    final_data["end_time"],
                    height=480,  # Use 480p for good balance between quality and token usage
                    frame_mode=frame_mode,
                )
-                if not frames:
+                if not thumbs:
                    # Fallback to preview frames if no recordings available
                    logger.warning(
                        f"No recording frames found for {camera}, falling back to preview frames"
                    )
-                    frames = self.get_preview_frames_as_bytes(
+                    thumbs = self.get_preview_frames_as_bytes(
                        camera,
                        final_data["start_time"],
                        final_data["end_time"],
                        final_data["thumb_path"],
                        id,
                        camera_config.review.genai.debug_save_thumbnails,
                        frame_mode,
                    )
                elif camera_config.review.genai.debug_save_thumbnails:
-                    self.save_debug_recording_frames(id, frames)
+                    self.save_debug_recording_frames(id, thumbs)
            else:
                # Use preview frames
-                frames = self.get_preview_frames_as_bytes(
+                thumbs = self.get_preview_frames_as_bytes(
                    camera,
                    final_data["start_time"],
                    final_data["end_time"],
                    final_data["thumb_path"],
                    id,
                    camera_config.review.genai.debug_save_thumbnails,
                    frame_mode,
                )
-            self.start_analysis(camera_config, final_data, frames)
+            self.start_analysis(camera_config, final_data, thumbs)
    def handle_request(self, topic: str, request_data: dict[str, Any]) -> str | None:
        if topic == EmbeddingsRequestEnum.regenerate_review_description.value:
@@ -254,10 +237,6 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                    "start_time": r["start_time"],
                    "end_time": r["end_time"],
                    "metadata": r["data"]["metadata"],
                    "state_changes": [
                        describe_classification_change(change)
                        for change in sorted_classification_state_changes(r["data"])
                    ],
                }
                for r in (
                    ReviewSegment.select(
@@ -302,9 +281,6 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                primary_item["start_time"] = primary_seg["start_time"]
                primary_item["end_time"] = primary_seg["end_time"]
                if primary_seg["state_changes"]:
                    primary_item["state_changes"] = primary_seg["state_changes"]
                # Find overlapping contextual items from other cameras
                primary_start = primary_seg["start_time"]
                primary_end = primary_seg["end_time"]
@@ -325,25 +301,14 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                    seg_end = seg["end_time"]
                    if seg_start < primary_end and primary_start < seg_end:
-                        # Avoid duplicates if same camera has multiple overlapping
+                        # Avoid duplicates if same camera has multiple overlapping segments
-                        # segments. One with state changes is kept as its own item
+                        if seg_camera not in seen_contextual_cameras:
-                        # so each change stays within its item's time range.
+                            contextual_item = copy.deepcopy(seg["metadata"])
-                        if (
+                            contextual_item["camera"] = seg_camera
-                            seg_camera in seen_contextual_cameras
+                            contextual_item["start_time"] = seg_start
-                            and not seg["state_changes"]
+                            contextual_item["end_time"] = seg_end
-                        ):
+                            contextual_items.append(contextual_item)
-                            continue
+                            seen_contextual_cameras.add(seg_camera)
                        contextual_item = copy.deepcopy(seg["metadata"])
                        contextual_item["camera"] = seg_camera
                        contextual_item["start_time"] = seg_start
                        contextual_item["end_time"] = seg_end
                        if seg["state_changes"]:
                            contextual_item["state_changes"] = seg["state_changes"]
                        contextual_items.append(contextual_item)
                        seen_contextual_cameras.add(seg_camera)
                # Add context array to primary item
                primary_item["context"] = contextual_items
@@ -421,51 +386,31 @@ class ReviewDescriptionProcessor(PostProcessorApi):
        buffer_extension = get_recording_buffer_extension(
            final_data["end_time"] - final_data["start_time"]
        )
-        frames = self.get_recording_frames(
+        thumbs = self.get_recording_frames(
            str(review.camera),
            final_data["start_time"] - buffer_extension,
            final_data["end_time"] + buffer_extension,
            height=480,
            frame_mode=camera_config.review.genai.frame_mode,
        )
-        if not frames:
+        if not thumbs:
            logger.error(
                "No recording frames are available for review item %s", review_id
            )
            return
        if camera_config.review.genai.debug_save_thumbnails:
-            self.save_debug_recording_frames(review_id, frames)
+            self.save_debug_recording_frames(review_id, thumbs)
-        self.start_analysis(camera_config, final_data, frames)
+        self.start_analysis(camera_config, final_data, thumbs)
    def start_analysis(
        self,
        camera_config: CameraConfig,
        final_data: dict[str, Any],
-        frames: list[tuple[bytes, float]],
+        thumbs: list[bytes],
    ) -> None:
        """Kick off description generation for a review item in the background."""
        thumbs = [frame for frame, _ in frames]
        captions: list[str] = []
        if (
            camera_config.review.genai.frame_mode
            == ReviewFrameModeEnum.annotated_frames
        ):
            captions = build_frame_captions(
                final_data["data"].get("detections") or [],
                [timestamp for _, timestamp in frames],
                sorted_classification_state_changes(final_data["data"]),
            )
            if not captions:
                logger.debug(
                    "No tracking annotations for review item %s, sending plain frames",
                    final_data["id"],
                )
        self.review_desc_dps.update()
        threading.Thread(
            target=run_analysis,
@@ -476,22 +421,19 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                camera_config,
                final_data,
                thumbs,
                captions,
                camera_config.review.genai,
                sorted(self.config.all_labels),
                self.config.all_attributes,
            ),
        ).start()
-    def save_debug_recording_frames(
+    def save_debug_recording_frames(self, review_id: str, thumbs: list[bytes]) -> None:
        self, review_id: str, frames: list[tuple[bytes, float]]
    ) -> None:
        """Write the recording frames sent to the provider out for debugging."""
        Path(os.path.join(CLIPS_DIR, "genai-requests", review_id)).mkdir(
            parents=True, exist_ok=True
        )
-        for idx, (frame_bytes, _) in enumerate(frames):
+        for idx, frame_bytes in enumerate(thumbs):
            with open(
                os.path.join(CLIPS_DIR, f"genai-requests/{review_id}/{idx}.jpg"),
                "wb",
@@ -503,9 +445,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
        camera: str,
        start_time: float,
        end_time: float,
-        frame_mode: ReviewFrameModeEnum = ReviewFrameModeEnum.frames,
+    ) -> list[str]:
    ) -> list[tuple[str, float]]:
        """Preview frame paths paired with the time each one was captured."""
        preview_dir = os.path.join(CACHE_DIR, "preview_frames")
        file_start = f"preview_{camera}-"
        start_file = f"{file_start}{start_time}.webp"
@@ -537,29 +477,18 @@ class ReviewDescriptionProcessor(PostProcessorApi):
        frame_count = len(all_frames)
        desired_frame_count = self.calculate_frame_count(
-            camera,
+            camera, duration=end_time - start_time
            duration=end_time - start_time,
            frame_mode=frame_mode,
        )
        def with_timestamp(path: str) -> tuple[str, float]:
            # Preview frames are named preview_<camera>-<timestamp>.webp
            stem = os.path.basename(path).removesuffix(".webp")
            try:
                return (path, float(stem.removeprefix(file_start)))
            except ValueError:
                return (path, start_time)
        if frame_count <= desired_frame_count:
-            return [with_timestamp(f) for f in all_frames]
+            return all_frames
        selected_frames = []
        step_size = (frame_count - 1) / (desired_frame_count - 1)
        for i in range(desired_frame_count):
            index = round(i * step_size)
-            selected_frames.append(with_timestamp(all_frames[index]))
+            selected_frames.append(all_frames[index])
        return selected_frames
@@ -569,12 +498,11 @@ class ReviewDescriptionProcessor(PostProcessorApi):
        start_time: float,
        end_time: float,
        height: int = 480,
-        frame_mode: ReviewFrameModeEnum = ReviewFrameModeEnum.frames,
+    ) -> list[bytes]:
-    ) -> list[tuple[bytes, float]]:
+        """Get frames from recordings at specified timestamps."""
        """Get frames from recordings paired with the time each was captured."""
        duration = end_time - start_time
        desired_frame_count = self.calculate_frame_count(
-            camera, duration, ImageSourceEnum.recordings, height, frame_mode
+            camera, duration, ImageSourceEnum.recordings, height
        )
        # Calculate evenly spaced timestamps throughout the duration
@@ -612,7 +540,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
            except DoesNotExist:
                return None
-        frames: list[tuple[bytes, float]] = []
+        frames = []
        for timestamp in timestamps:
            try:
@@ -625,7 +553,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                    image_data = extract_frame_from_recording(rounded_timestamp)
                if image_data:
-                    frames.append((image_data, timestamp))
+                    frames.append(image_data)
                else:
                    logger.warning(
                        f"No recording found for {camera} at timestamp {timestamp}"
@@ -646,8 +574,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
        thumb_path_fallback: str,
        review_id: str,
        save_debug: bool,
-        frame_mode: ReviewFrameModeEnum = ReviewFrameModeEnum.frames,
+    ) -> list[bytes]:
    ) -> list[tuple[bytes, float]]:
        """Get preview frames and convert them to JPEG bytes.
        Args:
@@ -659,14 +586,14 @@ class ReviewDescriptionProcessor(PostProcessorApi):
            save_debug: Whether to save debug thumbnails
        Returns:
-            List of (JPEG image bytes, capture timestamp) pairs
+            List of JPEG image bytes
        """
-        frame_paths = self.get_cache_frames(camera, start_time, end_time, frame_mode)
+        frame_paths = self.get_cache_frames(camera, start_time, end_time)
        if not frame_paths:
-            frame_paths = [(thumb_path_fallback, start_time)]
+            frame_paths = [thumb_path_fallback]
-        thumbs: list[tuple[bytes, float]] = []
+        thumbs = []
-        for idx, (thumb_path, timestamp) in enumerate(frame_paths):
+        for idx, thumb_path in enumerate(frame_paths):
            thumb_data = cv2.imread(thumb_path)
            if thumb_data is None:
@@ -679,7 +606,7 @@ class ReviewDescriptionProcessor(PostProcessorApi):
                ".jpg", thumb_data, [int(cv2.IMWRITE_JPEG_QUALITY), 100]
            )
            if ret:
-                thumbs.append((jpg.tobytes(), timestamp))
+                thumbs.append(jpg.tobytes())
            if save_debug:
                Path(os.path.join(CLIPS_DIR, "genai-requests", review_id)).mkdir(
@@ -707,39 +634,6 @@ def get_recording_buffer_extension(duration: float) -> float:
    return buffer_extension
 def sorted_classification_state_changes(
    review_data: dict[str, Any],
 ) -> list[dict[str, Any]]:
    """A review item's state classification changes in time order."""
    return sorted(
        review_data.get("classification_state_changes") or [],
        key=lambda change: change["timestamp"],
    )
 def format_classification_state_changes(
    changes: list[dict[str, Any]], start_time: float, end_time: float
 ) -> list[str]:
    """Phrase state classification changes with their timing in the activity.
    Changes are attached while the review item is active, which runs past its
    end_time by the review cutoff, and a few seconds before its start.
    """
    lines = []
    for change in changes:
        if change["timestamp"] < start_time:
            when = "just before the activity started"
        elif change["timestamp"] > end_time:
            when = "after the activity ended"
        else:
            when = f"{round(change['timestamp'] - start_time)}s into the activity"
        lines.append(f"{describe_classification_change(change)}, {when}")
    return lines
 def run_analysis(
    requestor: InterProcessRequestor,
    genai_client: GenAIClient,
@@ -747,7 +641,6 @@ def run_analysis(
    camera_config: CameraConfig,
    final_data: dict[str, Any],
    thumbs: list[bytes],
    frame_captions: list[str],
    genai_config: GenAIReviewConfig,
    labelmap_objects: list[str],
    attribute_labels: list[str],
@@ -795,13 +688,6 @@ def run_analysis(
                unified_objects.append(object_type)
    analytics_data["unified_objects"] = unified_objects
    analytics_data["classification_state_changes"] = (
        format_classification_state_changes(
            sorted_classification_state_changes(final_data["data"]),
            final_data["start_time"],
            final_data["end_time"],
        )
    )
    metadata = genai_client.generate_review_description(
        analytics_data,
@@ -811,7 +697,6 @@ def run_analysis(
        genai_config.debug_save_thumbnails,
        genai_config.activity_context_prompt,
        genai_config.response_style,
        frame_captions,
    )
    review_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -1,19 +1,16 @@
 """Handle processing audio for speech transcription using sherpa-onnx with FFmpeg pipe."""
 import collections
 import logging
 import os
 import queue
 import threading
-import time
+from typing import Any
 from typing import TYPE_CHECKING, Any
 import numpy as np
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.config import CameraConfig, FrigateConfig
-from frigate.config.classification import AudioTranscriptionModelEnum
+from frigate.const import MODEL_CACHE_DIR
 from frigate.const import AUDIO_DURATION, MODEL_CACHE_DIR
 from frigate.data_processing.common.audio_transcription.model import (
    AudioTranscriptionModelRunner,
 )
@@ -21,38 +18,12 @@ from frigate.data_processing.real_time.whisper_online import (
    FasterWhisperASR,
    OnlineASRProcessor,
 )
 from frigate.util.audio import (
    clean_transcript,
    pcm16_to_wav,
    resolve_language,
    stitch_transcripts,
 )
 from ..types import DataProcessorMetrics
 from .api import RealTimeProcessorApi
 if TYPE_CHECKING:
    # importing frigate.genai eagerly would pull the provider SDKs into the
    # audio process even when transcription runs on a local model
    from frigate.genai.manager import GenAIClientManager
 logger = logging.getLogger(__name__)
 # Number of ~0.975s audio detector chunks per GenAI request. The window advances
 # one chunk at a time, so two chunks means a 50% overlap: every word lands whole
 # in at least one window, which whisper-family models need to avoid hallucinating
 # on a clipped clip. The cadence is fixed by the audio detector's frame size, so
 # this is a constant rather than a config knob.
 GENAI_WINDOW_CHUNKS = 2
 # Bound the queue at ~30s of audio so a slow or hung provider cannot grow it
 # without limit. The producer is the ffmpeg read thread and must never block.
 AUDIO_QUEUE_MAXSIZE = int(30 / AUDIO_DURATION)
 # A backed-up queue drops a chunk per cycle, so warning on each one would spam
 # the log once a second per camera for as long as the provider stays slow.
 AUDIO_DROP_WARN_INTERVAL = 10.0
 class AudioTranscriptionRealTimeProcessor(RealTimeProcessorApi):
    def __init__(
@@ -60,10 +31,9 @@ class AudioTranscriptionRealTimeProcessor(RealTimeProcessorApi):
        config: FrigateConfig,
        camera_config: CameraConfig,
        requestor: InterProcessRequestor,
-        model_runner: AudioTranscriptionModelRunner | None,
+        model_runner: AudioTranscriptionModelRunner,
        metrics: DataProcessorMetrics,
        stop_event: threading.Event,
        genai_manager: "GenAIClientManager | None" = None,
    ):
        super().__init__(config, metrics)
        self.config = config
@@ -72,31 +42,11 @@ class AudioTranscriptionRealTimeProcessor(RealTimeProcessorApi):
        self.stream: Any = None
        self.whisper_model: FasterWhisperASR | None = None
        self.model_runner = model_runner
        self.genai_manager = genai_manager
        self.transcription_segments: list[str] = []
-        self.audio_queue: queue.Queue[tuple[dict[str, Any], np.ndarray]] = queue.Queue(
+        self.audio_queue: queue.Queue[tuple[dict[str, Any], np.ndarray]] = queue.Queue()
            maxsize=AUDIO_QUEUE_MAXSIZE
        )
        self.stop_event = stop_event
        self._use_genai = not isinstance(
            config.audio_transcription.model, AudioTranscriptionModelEnum
        )
        # sliding window of raw int16 chunks; the deque's maxlen is what evicts
        # the oldest chunk and so produces the overlap
        self._genai_window: collections.deque[np.ndarray] = collections.deque(
            maxlen=GENAI_WINDOW_CHUNKS
        )
        self._genai_committed = ""
        # set by the producer when it discards a chunk, so the consumer knows the
        # audio it is about to receive is not contiguous with what it buffered
        self._audio_dropped = threading.Event()
        self._last_drop_warning = 0.0
    def __build_recognizer(self) -> None:
        if self._use_genai:
            # nothing local to load; never import sherpa or FasterWhisperASR
            return
        try:
            if self.config.audio_transcription.model_size == "large":
                # Whisper models need to be per-process and can only run one stream at a time
@@ -114,7 +64,7 @@ class AudioTranscriptionRealTimeProcessor(RealTimeProcessorApi):
                self.stream = OnlineASRProcessor(
                    asr=self.whisper_model,
                )
-            elif self.model_runner is not None:
+            else:
                logger.debug(f"Loading sherpa stream for {self.camera_config.name}")
                self.stream = self.model_runner.model.create_stream()
            logger.debug(
@@ -126,15 +76,6 @@ class AudioTranscriptionRealTimeProcessor(RealTimeProcessorApi):
            )
    def __process_audio_stream(self, audio_data: np.ndarray) -> tuple[str, bool] | None:
        # must precede both the model_runner guard (model_runner is None on this
        # path) and the float32 normalization below (GenAI wants untouched int16)
        if self._use_genai:
            return self.__process_audio_genai(audio_data)
        if self.model_runner is None:
            logger.debug("Audio transcription (live) model runner not initialized")
            return None
        if (
            self.model_runner.model is None
            and self.config.audio_transcription.model_size == "small"
@@ -199,82 +140,6 @@ class AudioTranscriptionRealTimeProcessor(RealTimeProcessorApi):
            logger.error(f"Error processing audio stream: {e}")
            return None
    def __process_audio_genai(self, audio_data: np.ndarray) -> tuple[str, bool] | None:
        """Transcribe a sliding overlapped window through the GenAI provider."""
        client = self.genai_manager.transcribe_client if self.genai_manager else None
        if not client:
            logger.error(
                "audio_transcription.model is '%s' (GenAI provider) but no transcribe "
                "client is configured. Ensure the GenAI provider has 'transcribe' in its roles",
                self.config.audio_transcription.model,
            )
            return None
        if self._audio_dropped.is_set():
            self._audio_dropped.clear()
            # Chunks were discarded between what is buffered and this one, so
            # concatenating them would splice non-adjacent audio into one window
            # and destroy the overlap the stitcher depends on.
            self._genai_window.clear()
            if self._genai_committed:
                # the transcript has a gap in it; close the utterance out rather
                # than stitching across missing speech
                return self.__end_genai_utterance()
        self._genai_window.append(audio_data)
        if len(self._genai_window) < GENAI_WINDOW_CHUNKS:
            # wait for a full window so the first request is never a clipped clip
            return None
        window = np.concatenate(list(self._genai_window))
        # Silence gate, using the same threshold audio detection uses. Gate the
        # whole window rather than individual chunks; this is the primary cost
        # and privacy brake and is what keeps a quiet camera near zero requests.
        window_as_float = window.astype(np.float32)
        rms = float(np.sqrt(np.mean(np.absolute(np.square(window_as_float)))))
        if rms < self.camera_config.audio.min_volume:
            logger.debug(
                f"Window RMS {rms:.1f} below min_volume, skipping transcription"
            )
            return self.__end_genai_utterance()
        text = client.transcribe(
            pcm16_to_wav(window),
            language=resolve_language(self.config.audio_transcription.language),
        )
        # cleaning has to come first: a silent window often comes back as the
        # model's preamble alone, which is silence, not a word to commit
        cleaned = clean_transcript(text)
        if not cleaned:
            return self.__end_genai_utterance()
        self._genai_committed = stitch_transcripts(self._genai_committed, cleaned)
        # no VAD on this path, so mirror the whisper branch's heuristic endpoint
        is_endpoint = (
            self._genai_committed.endswith((".", "!", "?"))
            and len(self._genai_committed) > 300
        )
        logger.debug(f"GenAI transcription: '{self._genai_committed}'")
        return self._genai_committed, is_endpoint
    def __end_genai_utterance(self) -> tuple[str, bool] | None:
        """Close out the current utterance when a window carries no speech."""
        if not self._genai_committed:
            return None
        return self._genai_committed, True
    def process_frame(self, obj_data: dict[str, Any], frame: np.ndarray) -> None:
        pass
@@ -283,38 +148,8 @@ class AudioTranscriptionRealTimeProcessor(RealTimeProcessorApi):
            logger.debug("No audio data provided for transcription")
            return None
-        # enqueue audio data for processing in the thread. never block: the
+        # enqueue audio data for processing in the thread
-        # producer is the ffmpeg read thread that audio detection depends on,
+        self.audio_queue.put((obj_data, audio))
        # so on a backlog drop the oldest chunk instead.
        try:
            self.audio_queue.put_nowait((obj_data, audio))
        except queue.Full:
            try:
                self.audio_queue.get_nowait()
                self.audio_queue.task_done()
            except queue.Empty:
                pass
            # the stream now has a hole in it, which the consumer has to know
            # about before it splices the next chunk onto what it already holds
            self._audio_dropped.set()
            now = time.monotonic()
            if now - self._last_drop_warning >= AUDIO_DROP_WARN_INTERVAL:
                self._last_drop_warning = now
                logger.warning(
                    "Audio transcription queue for %s is full, dropping audio. The "
                    "provider is not keeping up with the %.2fs chunk rate",
                    self.camera_config.name,
                    AUDIO_DURATION,
                )
            try:
                self.audio_queue.put_nowait((obj_data, audio))
            except queue.Full:
                pass
        return None
    def run(self) -> None:
@@ -370,14 +205,6 @@ class AudioTranscriptionRealTimeProcessor(RealTimeProcessorApi):
                break
    def reset(self) -> None:
        if self._use_genai:
            self._genai_committed = ""
            # stale audio carried across an utterance boundary would be
            # re-transcribed into the next one
            self._genai_window.clear()
            logger.debug("Stream reset")
            return
        if self.config.audio_transcription.model_size == "large":
            # get final output from whisper
            output = self.stream.finish()
@@ -391,7 +218,7 @@ class AudioTranscriptionRealTimeProcessor(RealTimeProcessorApi):
            # reset whisper
            self.stream.init()
            self.transcription_segments = []
-        elif self.model_runner is not None:
+        else:
            # reset sherpa
            self.model_runner.model.reset(self.stream)
@@ -399,24 +226,6 @@ class AudioTranscriptionRealTimeProcessor(RealTimeProcessorApi):
    def check_unload_model(self) -> None:
        # regularly called in the loop in audio maintainer
        if self._use_genai:
            # no model to unload, but this is the hook that fires when
            # live_enabled flips off. guard on emptiness: called ~1x/s per camera.
            if self._genai_committed or self._genai_window:
                logger.debug(
                    f"Clearing GenAI transcription state for {self.camera_config.name}"
                )
                self.clear_audio_queue()
                self._genai_committed = ""
                self._genai_window.clear()
                self.requestor.send_data(
                    f"{self.camera_config.name}/audio/transcription",
                    "",
                )
            return
        if (
            self.config.audio_transcription.model_size == "large"
            and self.whisper_model is not None
@@ -461,10 +270,6 @@ class AudioTranscriptionRealTimeProcessor(RealTimeProcessorApi):
        self, topic: str, request_data: dict[str, Any]
    ) -> dict[str, Any] | None:
        if topic == "clear_audio_recognizer":
            if self._use_genai:
                self.reset()
                return {"message": "Audio transcription state cleared", "success": True}
            self.stream = None
            self.__build_recognizer()
            return {"message": "Audio recognizer cleared and rebuilt", "success": True}
@@ -91,23 +91,8 @@ class CustomStateClassificationProcessor(DeferredRealtimeProcessorApi):
        self.tensor_input_details = self.interpreter.get_input_details()
        self.tensor_output_details = self.interpreter.get_output_details()
        self.labelmap = load_labels(labelmap_path, prefill=0, indexed=False)
        self._forget_unknown_states()
        self.classifications_per_second.start()
    def _forget_unknown_states(self) -> None:
        """Drop verified states that are not labels of the loaded model.
        A retrained model can rename or remove labels. Keeping a state it can
        no longer produce would report its first verified state as a change
        from that obsolete label.
        """
        labels = set(self.labelmap.values())
        self.state_history = {
            camera: history
            for camera, history in self.state_history.items()
            if history["current_state"] in labels
        }
    def __update_metrics(self, duration: float) -> None:
        self.classifications_per_second.update()
        if self.inference_speed:
@@ -149,20 +134,15 @@ class CustomStateClassificationProcessor(DeferredRealtimeProcessorApi):
        # Don't save if state is stable (detected_state == current_state) AND score is 100%
        return False
-    def verify_state_change(
+    def verify_state_change(self, camera: str, detected_state: str) -> str | None:
        self, camera: str, detected_state: str, timestamp: float
    ) -> tuple[str | None, float] | None:
        """
        Verify state change requires 3 consecutive identical states before publishing.
-        Returns (previous state, time the new state was first seen) once verified,
+        Returns state to publish or None if verification not complete.
        or None if verification not complete. The previous state is None for the
        first state verified on a camera.
        """
        if camera not in self.state_history:
            self.state_history[camera] = {
                "current_state": None,
                "pending_state": None,
                "pending_since": 0.0,
                "consecutive_count": 0,
            }
@@ -177,14 +157,12 @@ class CustomStateClassificationProcessor(DeferredRealtimeProcessorApi):
            verification["consecutive_count"] += 1
            if verification["consecutive_count"] >= 3:
                previous_state = verification["current_state"]
                verification["current_state"] = detected_state
                verification["pending_state"] = None
                verification["consecutive_count"] = 0
-                return previous_state, verification["pending_since"]
+                return detected_state
        else:
            verification["pending_state"] = detected_state
            verification["pending_since"] = timestamp
            verification["consecutive_count"] = 1
            logger.debug(
                f"New state '{detected_state}' detected for {camera}, need {3 - verification['consecutive_count']} more consecutive detections"
@@ -362,19 +340,16 @@ class CustomStateClassificationProcessor(DeferredRealtimeProcessorApi):
            )
            return
-        verified = self.verify_state_change(camera, detected_state, timestamp)
+        verified_state = self.verify_state_change(camera, detected_state)
-        if verified is not None:
+        if verified_state is not None:
            previous_state, changed_at = verified
            self._emit_result(
                {
                    "type": "classification",
                    "processor": "state",
                    "model_name": self.model_config.name,
                    "camera": camera,
-                    "state": detected_state,
+                    "state": verified_state,
                    "previous_state": previous_state,
                    "timestamp": changed_at,
                }
            )
@@ -365,7 +365,7 @@ class FaceRealTimeProcessor(RealTimeProcessorApi):
                img = cv2.imread(current_file)
            if img is None:
-                return {
+                return {  # type: ignore[unreachable]
                    "message": "Invalid image file.",
                    "success": False,
                }
@@ -1,10 +1,8 @@
 import logging
 import sqlite3
 import threading
 from typing import Any
 import regex
 from peewee import DatabaseError
 from playhouse.sqliteq import SqliteQueueDatabase
 logger = logging.getLogger(__name__)
@@ -19,7 +17,6 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
        self.load_vec_extension: bool = load_vec_extension
        # no extension necessary, sqlite will load correctly for each platform
        self.sqlite_vec_path = "/usr/local/lib/vec0"
        self.upsert_lock = threading.Lock()
        super().__init__(*args, **kwargs)
    def _connect(self, *args: Any, **kwargs: Any) -> sqlite3.Connection:
@@ -56,22 +53,6 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
        conn.create_function("REGEXP", 2, regexp)
    def execute_write(self, sql: str, params: Any = None) -> None:
        """Run a write and wait for it, so that failures are raised here.
        SqliteQueueDatabase hands non-SELECT statements to a writer thread and
        stores any exception on the cursor it returns, so callers that ignore
        that cursor never learn the write failed.
        """
        self.execute_sql(sql, params).fetchall()
    def _table_exists(self, table: str) -> bool:
        cursor = self.execute_sql(
            "SELECT name FROM sqlite_master WHERE type = 'table' AND name = ?",
            (table,),
        )
        return cursor.fetchone() is not None
    def _delete_embeddings(self, table: str, event_ids: list[str]) -> None:
        """Delete embeddings for the given events, if the table exists.
@@ -82,17 +63,17 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
            return
        # the embeddings tables are only created once semantic search has run
-        if not self._table_exists(table):
+        cursor = self.execute_sql(
            "SELECT name FROM sqlite_master WHERE type = 'table' AND name = ?",
            (table,),
        )
        if cursor.fetchone() is None:
            logger.debug("Skipping %s cleanup, table does not exist", table)
            return
        ids = ",".join(["?" for _ in event_ids])
-
+        self.execute_sql(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)
        # callers treat cleanup as best effort, so log rather than propagate
        try:
            self.execute_write(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)
        except DatabaseError:
            logger.exception("Failed to delete embeddings from %s", table)
    def delete_embeddings_thumbnail(self, event_ids: list[str]) -> None:
        self._delete_embeddings("vec_thumbnails", event_ids)
@@ -100,67 +81,25 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
    def delete_embeddings_description(self, event_ids: list[str]) -> None:
        self._delete_embeddings("vec_descriptions", event_ids)
    def _restore_vec_info_table(self, table: str) -> None:
        """Recreate the _info shadow table a legacy vec0 table is missing.
        sqlite-vec added _info in 0.1.6 and drops it unconditionally when a
        table is destroyed, so tables written by Frigate 0.17 and earlier fail
        to drop. An empty stub is enough, and leaving it unseeded keeps the
        table reading as pre-0.1.10 if the drop does not follow.
        """
        if not self._table_exists(table) or self._table_exists(f"{table}_info"):
            return
        logger.debug("Restoring the %s_info shadow table before dropping", table)
        self.execute_write(
            f'CREATE TABLE "{table}_info" (key TEXT PRIMARY KEY, value ANY)'
        )
    def drop_embeddings_tables(self) -> None:
-        for table in ("vec_descriptions", "vec_thumbnails"):
+        self.execute_sql("""
-            self._restore_vec_info_table(table)
+            DROP TABLE vec_descriptions;
-            self.execute_write(f"DROP TABLE IF EXISTS {table}")
+        """)
        self.execute_sql("""
            DROP TABLE vec_thumbnails;
        """)
    def create_embeddings_tables(self) -> None:
        """Create vec0 virtual table for embeddings"""
-        self.execute_write("""
+        self.execute_sql("""
            CREATE VIRTUAL TABLE IF NOT EXISTS vec_thumbnails USING vec0(
                id TEXT PRIMARY KEY,
                thumbnail_embedding FLOAT[768] distance_metric=cosine
            );
        """)
-        self.execute_write("""
+        self.execute_sql("""
            CREATE VIRTUAL TABLE IF NOT EXISTS vec_descriptions USING vec0(
                id TEXT PRIMARY KEY,
                description_embedding FLOAT[768] distance_metric=cosine
            );
        """)
    def upsert_embeddings(
        self, table: str, column: str, embeddings: dict[str, bytes]
    ) -> None:
        """Write embeddings for the given event ids, replacing any that exist.
        vec0 implements neither REPLACE nor UPSERT, so rows that are already
        there have to be deleted first.
        """
        if not embeddings:
            return
        event_ids = list(embeddings.keys())
        ids = ",".join(["?" for _ in event_ids])
        params: list[Any] = []
        for event_id in event_ids:
            params.extend((event_id, embeddings[event_id]))
        values = ", ".join(["(?, ?)"] * len(event_ids))
        # reindexing and live embedding run on separate threads, and each write
        # is queued separately, so the delete and the insert have to be held
        # together or an interleaved pair fails on the vec0 primary key
        with self.upsert_lock:
            self.execute_write(f"DELETE FROM {table} WHERE id IN ({ids})", event_ids)
            self.execute_write(
                f"INSERT INTO {table}(id, {column}) VALUES {values}", params
            )
@@ -46,42 +46,8 @@ _PROVIDER_LABELS = {
    "MIGraphXExecutionProvider": "MIGraphX",
    "OpenVINOExecutionProvider": "OpenVINO",
    "CPUExecutionProvider": "CPU",
    "LighterANE": "Neural Engine",
 }
 # lighter (https://github.com/fieldwork-ai/lighter) places an ONNX Runtime plugin
 # execution provider in a container started with --device lighter.sh/ane=all,
 # which runs models on a Mac's Neural Engine; LIGHTER_ANE_EP names where it is
 LIGHTER_ANE_EP_NAME = "LighterANE"
 LIGHTER_ANE_LIBRARY = "/usr/lib/lighter/liblighter_ane_ep.so"
 def get_lighter_ane_devices() -> list[Any]:
    """Get the Neural Engine devices lighter's provider offers, registering it once.
    Returns:
        The provider's ONNX Runtime devices, or an empty list without lighter's device
    """
    library = os.environ.get("LIGHTER_ANE_EP", LIGHTER_ANE_LIBRARY)
    if not os.path.exists(library):
        return []
    devices = [d for d in ort.get_ep_devices() if d.ep_name == LIGHTER_ANE_EP_NAME]
    if not devices:
        try:
            ort.register_execution_provider_library(LIGHTER_ANE_EP_NAME, library)
        except Exception as e:
            logger.warning(
                f"Failed to load the Neural Engine provider from {library}: {e}"
            )
            return []
        devices = [d for d in ort.get_ep_devices() if d.ep_name == LIGHTER_ANE_EP_NAME]
    return devices
 def is_arm64_platform() -> bool:
    """Check if we're running on an ARM platform."""
@@ -89,46 +55,30 @@ def is_arm64_platform() -> bool:
    return machine in ("aarch64", "arm64", "armv8", "armv7l")
-def get_ort_session_options(
+def get_ort_session_options(model_type: str | None = None) -> ort.SessionOptions | None:
    model_type: str | None = None, cpu: bool = False
 ) -> ort.SessionOptions | None:
    """Get ONNX Runtime session options with appropriate settings.
    Args:
        model_type: Model being loaded, used to pin its graph optimization level.
        cpu: Whether the session runs on the CPU execution provider.
    Returns:
-        SessionOptions for enrichment models, or None for default settings.
+        SessionOptions with a pinned optimization level, or None for default settings.
    """
    # Import here to avoid circular imports
-    from frigate.embeddings.types import (
+    from frigate.embeddings.types import EnrichmentModelTypeEnum
        ENRICHMENT_FOR_MODEL_TYPE,
        EnrichmentModelTypeEnum,
    )
    if model_type not in ENRICHMENT_FOR_MODEL_TYPE:
        return None
    sess_options = ort.SessionOptions()
    if cpu:
        # idle intra-op threads otherwise spin for ~1s after every run, keeping
        # most cores busy while an enrichment runs even once a second
        sess_options.add_session_config_entry("session.intra_op.allow_spinning", "0")
    if model_type == EnrichmentModelTypeEnum.jina_v2.value:
        # below EXTENDED the CUDA EP returns an identical vector for every image,
        # and ORT_ENABLE_ALL fails to build on CPU with a SimplifiedLayerNormFusion error
-        sess_options.graph_optimization_level = (
+        level = ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED
            ort.GraphOptimizationLevel.ORT_ENABLE_EXTENDED
        )
    elif model_type == EnrichmentModelTypeEnum.jina_v1.value:
        # aggressive optimizations create or expect nodes that don't exist
-        sess_options.graph_optimization_level = (
+        level = ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
-            ort.GraphOptimizationLevel.ORT_ENABLE_BASIC
+    else:
-        )
+        return None
    sess_options = ort.SessionOptions()
    sess_options.graph_optimization_level = level
    return sess_options
@@ -739,23 +689,6 @@ def get_optimized_runner(
        if rknn_path:
            return _record_runner(model_path, model_type, RKNNModelRunner(rknn_path))
    if device != "CPU" and (ane_devices := get_lighter_ane_devices()):
        sess_options = get_ort_session_options(model_type) or ort.SessionOptions()
        sess_options.add_provider_for_devices(ane_devices, {})
        try:
            session = ort.InferenceSession(model_path, sess_options=sess_options)
        except Exception as e:
            logger.warning(
                f"Failed to load {model_path} on the Neural Engine, using the default providers: {e}"
            )
        else:
            return _record_runner(
                model_path,
                model_type,
                ONNXModelRunner(session, model_type=model_type),
            )
    providers, options = get_ort_providers(device == "CPU", device, **kwargs)
    if providers[0] == "CPUExecutionProvider":
@@ -805,9 +738,7 @@ def get_optimized_runner(
        ONNXModelRunner(
            ort.InferenceSession(
                model_path,
-                sess_options=get_ort_session_options(
+                sess_options=get_ort_session_options(model_type),
                    model_type, cpu=providers[0] == "CPUExecutionProvider"
                ),
                providers=providers,
                provider_options=options,
            ),
@@ -47,17 +47,21 @@ class ModelTypeEnum(str, Enum):
    yologeneric = "yolo-generic"
-# the scene of the model used by cameras that don't name one
+class SceneEnum(str, Enum):
-DEFAULT_SCENE = "default"
+    """The camera environment a detection model is intended for."""
-SCENE_PATTERN = r"^[A-Za-z0-9_-]+$"
+
    all = "all"
    indoor = "indoor"
    outdoor = "outdoor"
    indoor_thermal = "indoor_thermal"
    outdoor_thermal = "outdoor_thermal"
 class ModelConfig(BaseModel):
-    scene: str = Field(
+    scene: SceneEnum = Field(
-        default=DEFAULT_SCENE,
+        default=SceneEnum.all,
        pattern=SCENE_PATTERN,
        title="Model scene",
-        description="A name for the camera environment this model is used for, such as 'thermal'. Cameras select a model by setting detect.scene to a matching value, and the 'default' model is used by any camera that does not set one.",
+        description="The camera environment this model is used for. Cameras select a model by setting detect.scene to a matching value, and 'all' is used by any camera that does not set one.",
    )
    devices: list[str] = Field(
        default_factory=list,
@@ -119,7 +123,6 @@ class ModelConfig(BaseModel):
    _all_attributes: list[str] = PrivateAttr()
    _all_attribute_logos: list[str] = PrivateAttr()
    _model_hash: str = PrivateAttr()
    _plus_id: str | None = PrivateAttr(default=None)
    @property
    def merged_labelmap(self) -> dict[int, str]:
@@ -145,11 +148,6 @@ class ModelConfig(BaseModel):
    def model_hash(self) -> str:
        return self._model_hash
    @property
    def plus_id(self) -> str | None:
        """The Frigate+ model id, once a plus:// path has been resolved."""
        return self._plus_id
    def __init__(self, **config):
        super().__init__(**config)
@@ -180,34 +178,20 @@ class ModelConfig(BaseModel):
        os.makedirs(MODEL_CACHE_DIR, exist_ok=True)
        model_id = self.path[7:]
        self._plus_id = model_id
        self.path = os.path.join(MODEL_CACHE_DIR, model_id)
        model_info_path = f"{self.path}.json"
        # download the model if it doesn't exist
        if not os.path.isfile(self.path):
-            try:
+            download_url = plus_api.get_model_download_url(model_id)
-                download_url = plus_api.get_model_download_url(model_id)
+            r = requests.get(download_url)
                r = requests.get(download_url)
            except requests.exceptions.ConnectionError as e:
                raise ValueError(
                    f"Unable to connect to Frigate+ to download model {model_id}"
                ) from e
            with open(self.path, "wb") as f:
                f.write(r.content)
        # download the model info if it doesn't exist
        if not os.path.isfile(model_info_path):
            try:
                model_info = plus_api.get_model_info(model_id)
            except requests.exceptions.ConnectionError as e:
                raise ValueError(
                    f"Unable to connect to Frigate+ to download model info for {model_id}"
                ) from e
            with open(model_info_path, "w") as f:
-                json.dump(model_info, f)
+                json.dump(plus_api.get_model_info(model_id), f)
        model_info = load_plus_model_info(model_id)
@@ -25,7 +25,6 @@ SYS_ROOT = "/sys"
 DEV_ROOT = "/dev"
 PROC_ROOT = "/proc"
 ETC_ROOT = "/etc"
 LIB_ROOT = "/usr/lib"
 # a Coral reports as Global Unichip until its firmware is loaded, then as Google
 CORAL_USB_IDS = {("1a6e", "089a"), ("18d1", "9302")}
@@ -274,16 +273,6 @@ def detect_memryx() -> DetectionHardware | None:
    return _hardware("memryx", "memryx", "MemryX MX3", units)
 def detect_deepx() -> DetectionHardware | None:
    """Find DEEPX NPUs by their device nodes."""
    units = _dev_units("dxrt*", "deepx:PCIe:{index}", "PCIe")
    if not units:
        return None
    return _hardware("deepx", "deepx", "DEEPX NPU", units)
 def detect_rockchip() -> DetectionHardware | None:
    """Find a Rockchip NPU by reading the SoC from the device tree."""
    compatible = _read(f"{PROC_ROOT}/device-tree/compatible")
@@ -318,19 +307,6 @@ def detect_synaptics() -> DetectionHardware | None:
    return _hardware("synaptics", "synaptics", "Synaptics NPU", units)
 def detect_lighter_ane() -> DetectionHardware | None:
    """Find a Mac's Neural Engine by the provider library lighter's device places."""
    library = os.environ.get(
        "LIGHTER_ANE_EP", f"{LIB_ROOT}/lighter/liblighter_ane_ep.so"
    )
    if not os.path.exists(library):
        return None
    # runs through onnx, whose session picks lighter's provider when it is present
    units = [HardwareUnit(device="onnx", label="Neural Engine")]
    return _hardware("onnx:lighter", "onnx", "Apple Neural Engine", units)
 def detect_cpu() -> DetectionHardware:
    """The CPU, which is always available."""
    units = [HardwareUnit(device="cpu", label="CPU")]
@@ -343,7 +319,6 @@ PROBES = (
    detect_coral_usb,
    detect_hailo,
    detect_memryx,
    detect_deepx,
    detect_intel_npu,
    detect_intel_gpu,
    detect_nvidia_gpu,
@@ -352,7 +327,6 @@ PROBES = (
    detect_rockchip,
    detect_axengine,
    detect_synaptics,
    detect_lighter_ane,
    detect_cpu,
 )
@@ -1,7 +1,7 @@
 import json
 import logging
 import os
-from typing import Any, ClassVar, Literal
+from typing import Any, Literal
 import numpy as np
 import zmq
@@ -21,7 +21,6 @@ class ZmqDetectorConfig(BaseDetectorConfig):
    model_config = ConfigDict(
        title="ZMQ IPC",
    )
    device_spec_field: ClassVar[str] = "endpoint"
    type: Literal[DETECTOR_KEY]
    endpoint: str = Field(
@@ -10,7 +10,6 @@ from json.decoder import JSONDecodeError
 from multiprocessing.synchronize import Event as MpEvent
 from typing import Any
 import cv2
 import regex
 from pathvalidate import ValidationError, sanitize_filename
@@ -52,11 +51,6 @@ class EmbeddingProcess(FrigateProcess):
        from .maintainer import EmbeddingMaintainer
        self.pre_run_setup(self.config.logger)
        # frame conversions and crops here are one-off calls, and OpenCV's
        # per-core thread pool burns ~8x the CPU of running them inline
        cv2.setNumThreads(1)
        maintainer = EmbeddingMaintainer(
            self.config,
            self.metrics,
@@ -6,10 +6,9 @@ import logging
 import os
 import threading
 import time
 from typing import Any
 import numpy as np
-from peewee import DatabaseError, DoesNotExist, IntegrityError
+from peewee import DoesNotExist, IntegrityError
 from PIL import Image
 from playhouse.shortcuts import model_to_dict
@@ -208,10 +207,12 @@ class Embeddings:
        embedding = self.vision_embedding([thumbnail])[0]
        if upsert:
-            self.db.upsert_embeddings(
+            self.db.execute_sql(
-                "vec_thumbnails",
+                """
-                "thumbnail_embedding",
+                INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
-                {event_id: serialize(embedding)},
+                VALUES(?, ?)
                """,
                (event_id, serialize(embedding)),
            )
        self.image_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -250,12 +251,19 @@ class Embeddings:
        embeddings = self.vision_embedding(valid_thumbs)
        if upsert:
-            items = {}
+            items = []
            for i in range(len(valid_ids)):
-                items[valid_ids[i]] = serialize(embeddings[i])
+                items.append(valid_ids[i])
                items.append(serialize(embeddings[i]))
                self.image_eps.update()
-            self.db.upsert_embeddings("vec_thumbnails", "thumbnail_embedding", items)
+            self.db.execute_sql(
                """
                INSERT OR REPLACE INTO vec_thumbnails(id, thumbnail_embedding)
                VALUES {}
                """.format(", ".join(["(?, ?)"] * len(valid_ids))),
                items,
            )
        duration = datetime.datetime.now().timestamp() - start
        self.image_inference_speed.update(duration / len(valid_ids))
@@ -269,10 +277,12 @@ class Embeddings:
        embedding = self.text_embedding([description])[0]
        if upsert:
-            self.db.upsert_embeddings(
+            self.db.execute_sql(
-                "vec_descriptions",
+                """
-                "description_embedding",
+                INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
-                {event_id: serialize(embedding)},
+                VALUES(?, ?)
                """,
                (event_id, serialize(embedding)),
            )
        self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -292,14 +302,19 @@ class Embeddings:
        if upsert:
            ids = list(event_descriptions.keys())
-            items = {}
+            items = []
            for i in range(len(ids)):
-                items[ids[i]] = serialize(embeddings[i])
+                items.append(ids[i])
                items.append(serialize(embeddings[i]))
                self.text_eps.update()
-            self.db.upsert_embeddings(
+            self.db.execute_sql(
-                "vec_descriptions", "description_embedding", items
+                """
                INSERT OR REPLACE INTO vec_descriptions(id, description_embedding)
                VALUES {}
                """.format(", ".join(["(?, ?)"] * len(ids))),
                items,
            )
        self.text_inference_speed.update(datetime.datetime.now().timestamp() - start)
@@ -307,17 +322,6 @@ class Embeddings:
        return embeddings
    def reindex(self) -> None:
        """Rebuild every tracked object embedding from scratch."""
        totals: dict[str, Any] = {"status": "indexing"}
        try:
            self._reindex(totals)
        except DatabaseError:
            logger.exception("Unable to reindex tracked object embeddings")
            totals["status"] = "failed"
            self.requestor.send_data(UPDATE_EMBEDDINGS_REINDEX_PROGRESS, totals)
    def _reindex(self, totals: dict[str, Any]) -> None:
        logger.info("Indexing tracked object embeddings...")
        self.db.drop_embeddings_tables()
@@ -342,24 +346,17 @@ class Embeddings:
            batch_size = 32
        current_page = 1
-        totals.update(
+        totals = {
-            {
+            "thumbnails": 0,
-                "thumbnails": 0,
+            "descriptions": 0,
-                "descriptions": 0,
+            "processed_objects": total_events - 1 if total_events < batch_size else 0,
-                "processed_objects": total_events - 1
+            "total_objects": total_events,
-                if total_events < batch_size
+            "time_remaining": 0 if total_events < batch_size else -1,
-                else 0,
+            "status": "indexing",
-                "total_objects": total_events,
+        }
                "time_remaining": 0 if total_events < batch_size else -1,
                "status": "indexing",
            }
        )
        self.requestor.send_data(UPDATE_EMBEDDINGS_REINDEX_PROGRESS, totals)
        # a single batch sends no progress, so the first message shows it nearly done
        totals["processed_objects"] = 0
        events = (
            Event.select()
            .order_by(Event.start_time.desc())
@@ -11,11 +11,7 @@ from typing import Any
 from peewee import DoesNotExist
 from frigate.comms.config_updater import ConfigSubscriber
-from frigate.comms.detections_updater import (
+from frigate.comms.detections_updater import DetectionSubscriber, DetectionTypeEnum
    DetectionPublisher,
    DetectionSubscriber,
    DetectionTypeEnum,
 )
 from frigate.comms.embeddings_updater import (
    EmbeddingsRequestEnum,
    EmbeddingsResponder,
@@ -72,7 +68,7 @@ from frigate.events.types import (
    RegenerateDescriptionEnum,
 )
 from frigate.genai import GenAIClientManager
-from frigate.models import Event, Recordings, ReviewSegment, Timeline, Trigger
+from frigate.models import Event, Recordings, ReviewSegment, Trigger
 from frigate.types import TrackedObjectUpdateTypesEnum
 from frigate.util.builtin import serialize
 from frigate.util.file import get_event_thumbnail_bytes
@@ -143,7 +139,7 @@ class EmbeddingMaintainer(threading.Thread):
            ),
            load_vec_extension=True,
        )
-        models = [Event, Recordings, ReviewSegment, Timeline, Trigger]
+        models = [Event, Recordings, ReviewSegment, Trigger]
        db.bind(models)
        self.genai_manager = GenAIClientManager(config)
@@ -172,7 +168,6 @@ class EmbeddingMaintainer(threading.Thread):
        )
        self.review_subscriber = ReviewDataSubscriber("")
        self.detection_subscriber = DetectionSubscriber(DetectionTypeEnum.video.value)
        self.detection_publisher = DetectionPublisher(DetectionTypeEnum.all.value)
        self.embeddings_responder = EmbeddingsResponder()
        self.frame_manager = SharedMemoryFrameManager()
@@ -256,11 +251,7 @@ class EmbeddingMaintainer(threading.Thread):
        ):
            self.post_processors.append(
                AudioTranscriptionPostProcessor(
-                    self.config,
+                    self.config, self.requestor, self.embeddings, metrics
                    self.requestor,
                    self.embeddings,
                    metrics,
                    self.genai_manager,
                )
            )
@@ -332,9 +323,6 @@ class EmbeddingMaintainer(threading.Thread):
        """Apply camera config updates and register newly enabled processors."""
        updated_topics = self.config_updater.check_for_updates()
        for camera in updated_topics.get("remove", []):
            self.frame_manager.close_camera(camera)
        if updated_topics.keys() & GENAI_UPDATE_TOPICS:
            self._sync_genai_processors()
@@ -364,7 +352,6 @@ class EmbeddingMaintainer(threading.Thread):
        self.event_end_subscriber.stop()
        self.recordings_subscriber.stop()
        self.detection_subscriber.stop()
        self.detection_publisher.stop()
        self.event_metadata_publisher.stop()
        self.event_metadata_subscriber.stop()
        self.embeddings_responder.stop()
@@ -520,7 +507,7 @@ class EmbeddingMaintainer(threading.Thread):
        if update is None:
            return
-        source_type, event_type, camera, frame_name, data, frame_time = update
+        source_type, event_type, camera, frame_name, data = update
        logger.debug(
            f"Received update - source_type: {source_type}, camera: {camera}, data label: {data.get('label') if data else 'None'}"
@@ -549,7 +536,7 @@ class EmbeddingMaintainer(threading.Thread):
        # Create our own thumbnail based on the bounding box and the frame time
        try:
            yuv_frame = self.frame_manager.get(
-                frame_name, camera_config.frame_shape_yuv, frame_time
+                frame_name, camera_config.frame_shape_yuv
            )
        except FileNotFoundError:
            logger.debug(f"Frame {frame_name} not found for camera {camera}")
@@ -586,6 +573,8 @@ class EmbeddingMaintainer(threading.Thread):
                    PostProcessDataEnum.tracked_object,
                )
        self.frame_manager.close(frame_name)
    def _process_finalized(self) -> None:
        """Process the end of an event."""
        while True:
@@ -794,7 +783,7 @@ class EmbeddingMaintainer(threading.Thread):
        if topic is None:
            return
-        camera, frame_name, frame_time, _, motion_boxes, _ = data
+        camera, frame_name, _, _, motion_boxes, _ = data
        if not camera or camera not in self.config.cameras:
            return
@@ -818,7 +807,7 @@ class EmbeddingMaintainer(threading.Thread):
        try:
            yuv_frame = self.frame_manager.get(
-                frame_name, camera_config.frame_shape_yuv, frame_time
+                frame_name, camera_config.frame_shape_yuv
            )
        except FileNotFoundError:
            pass
@@ -842,6 +831,8 @@ class EmbeddingMaintainer(threading.Thread):
                    {"camera": camera, "motion": motion_boxes}, yuv_frame
                )
        self.frame_manager.close(frame_name)
    def _process_deferred_results(self) -> None:
        """Drain results from deferred processors and perform IPC side-effects."""
        for processor in self.realtime_processors:
@@ -856,21 +847,6 @@ class EmbeddingMaintainer(threading.Thread):
                        f"{result['camera']}/classification/{result['model_name']}",
                        result["state"],
                    )
                    # the first state verified after startup is not a change
                    if result["previous_state"] is not None:
                        self.detection_publisher.publish(
                            (
                                result["camera"],
                                {
                                    "model": result["model_name"],
                                    "from": result["previous_state"],
                                    "to": result["state"],
                                    "timestamp": result["timestamp"],
                                },
                            ),
                            DetectionTypeEnum.classification_state.value,
                        )
                elif result["processor"] == "object":
                    object_id = result["object_id"]
                    camera = result["camera"]
@@ -2,10 +2,10 @@
 import logging
 import os
 import shutil
 import threading
 import warnings
-from transformers import AutoTokenizer, CLIPImageProcessorPil
+from transformers import AutoFeatureExtractor, AutoTokenizer
 from transformers.utils.logging import disable_progress_bar
 from frigate.comms.inter_process import InterProcessRequestor
@@ -21,6 +21,12 @@ from frigate.util.downloader import ModelDownloader
 from .base_embedding import BaseEmbedding
 warnings.filterwarnings(
    "ignore",
    category=FutureWarning,
    message="The class CLIPFeatureExtractor is deprecated",
 )
 # disables the progress bar for downloading tokenizers and feature extractors
 disable_progress_bar()
 logger = logging.getLogger(__name__)
@@ -52,13 +58,6 @@ class JinaV1TextEmbedding(BaseEmbedding):
        self._lock = threading.Lock()
        files_names = list(self.download_urls.keys()) + [self.tokenizer_file]
        # an interrupted download leaves the hub cache without the saved tokenizer
        tokenizer_path = os.path.join(self.download_path, self.tokenizer_file)
        if os.path.isdir(tokenizer_path) and not os.path.exists(
            os.path.join(tokenizer_path, "tokenizer_config.json")
        ):
            shutil.rmtree(tokenizer_path)
        if not all(
            os.path.exists(os.path.join(self.download_path, n)) for n in files_names
        ):
@@ -93,7 +92,7 @@ class JinaV1TextEmbedding(BaseEmbedding):
                tokenizer = AutoTokenizer.from_pretrained(
                    self.model_name,
-                    trust_remote_code=False,
+                    trust_remote_code=True,
                    cache_dir=f"{MODEL_CACHE_DIR}/{self.model_name}/tokenizer",
                    clean_up_tokenization_spaces=True,
                )
@@ -124,8 +123,9 @@ class JinaV1TextEmbedding(BaseEmbedding):
                f"{MODEL_CACHE_DIR}/{self.model_name}/tokenizer"
            )
            self.tokenizer = AutoTokenizer.from_pretrained(
-                tokenizer_path,
+                self.model_name,
-                trust_remote_code=False,
+                cache_dir=tokenizer_path,
                trust_remote_code=True,
                clean_up_tokenization_spaces=True,
            )
@@ -209,7 +209,7 @@ class JinaV1ImageEmbedding(BaseEmbedding):
            if self.downloader:
                self.downloader.wait_for_download()
-            self.feature_extractor = CLIPImageProcessorPil.from_pretrained(
+            self.feature_extractor = AutoFeatureExtractor.from_pretrained(
                f"{MODEL_CACHE_DIR}/{self.model_name}",
            )
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Josh Hawkins	e0d48c98d9	show runtime overrides in the settings form The settings form read a camera section's saved config value, but its dependent warnings (audio transcription requiring audio detection, snapshots requiring detect, etc.) read the live config instead. A runtime toggle from the live view, MQTT, or an active profile can turn a section off without touching yaml, and that override persists across restarts, so the Enable switch showed on while the warning said the feature wasn't enabled. This adds an "Overridden (Live)" badge to any field whose live value differs from what's saved, and swaps the affected warnings to runtime-specific wording when a runtime override is the actual cause instead of the config.	2026-09-17 20:42:41 -05:00
Josh Hawkins	1ba10bce2a	fix restart failing under non-root restart_frigate() called psutil.Process(1).terminate() to signal s6-svscan, but s6-svscan runs as root while frigate runs as uid 1000, so the call raised AccessDenied. That exception escaped every caller: the UI restart button dropped its websocket client, MQTT restart and Save & Restart just logged and did nothing, and the watchdog crashed its own monitoring thread on a dead detector. This catches AccessDenied and falls through to the existing SIGINT branch, which exits the process for s6 to restart it.	2026-09-17 20:42:15 -05:00
Josh Hawkins	8120a4b117	don't edit a chat message while a reply streams The edit button stayed active while a reply streamed. `submitConversation` returns early while loading, but the message bubble still closed its editor, so the edit was silently lost. The edit button is hidden while a reply streams, and an editor that's already open keeps its draft with send disabled until the reply ends.	2026-09-17 16:55:39 -05:00
Josh Hawkins	03e6f54670	fix train image filtering for a class with a dash The backend writes a class with a `-` as `_` in train file names, since it splits those names on `-`, while a dataset folder keeps the dash. Filtering the Train grid by `half-open` compared it with `half_open` and hid every attempt. Both sides are normalized the same way now.	2026-09-17 16:55:39 -05:00
Josh Hawkins	13a3af1957	don't run page shortcuts for keys a dialog already handled Radix dismisses a dialog on Escape from a capture-phase keydown listener and calls `preventDefault()` without stopping propagation, so `useKeyboardListener` still ran the page's Escape shortcut: cancelling the delete dialog in the face library or a classification model also cleared the whole selection. Keys another shortcut hook handled still get through, since their listener order changes with every render.	2026-09-17 16:55:31 -05:00
Josh Hawkins	8aa0ddf740	find DST transitions to the second `get_dst_transitions` probed the offset once every 24 hours from the start time and reported a change at the first probe after it, up to a day late, so events, review items and recordings near a transition were grouped into days with the old offset. A transition after the last daily probe wasn't found at all. The end of the range is probed too now, and a probe that sees the offset change bisects the interval to the second of the transition.	2026-09-17 16:55:25 -05:00
Josh Hawkins	5fc8d4f1c6	return 403 for a snapshot or thumbnail on another camera The broad `except Exception` handlers in `event_snapshot` and `event_thumbnail` caught the `HTTPException` from `require_camera_access`, so a restricted user asking for another camera's snapshot got a 404 instead of a 403, and for an object still being tracked the snapshot was rendered before the check ran. Both endpoints now look up the event and check access in their own block, the way the other endpoints do, so a denial propagates.	2026-09-17 16:55:19 -05:00
Josh Hawkins	a8c838aee2	fix the has_clip self-heal for events with no recordings `vod_event` looked for a `(body, 404)` tuple, but `vod_ts` returns a `JSONResponse`, so the check never matched and an old event whose recordings are gone kept offering a clip that can't play. It now checks the response status code.	2026-09-17 16:55:12 -05:00
Josh Hawkins	4080c46428	check for a valid frame before using its shape With the camera offline, no preview frame, and `camera-error.jpg` missing, `latest_frame` read `frame.shape` before its `frame is None` check, so it raised `AttributeError` and answered 500 with a traceback instead of the intended "Unable to get valid frame". The check now runs first.	2026-09-17 16:55:06 -05:00