Miscellaneous fixes (0.18 beta) (#23763)

docs/docs/configuration/audio_detectors.md

@@ -272,7 +272,7 @@ If you have CUDA hardware, you can experiment with the `large` `whisper` model o
 
 #### Transcription and translation of `speech` audio events
 
-Any `speech` events in Explore can be transcribed and/or translated through the Transcribe button in the Tracked Object Details pane.
+Any `speech` events in Explore can be transcribed and/or translated through the Transcribe button (the microphone icon) in the Tracked Object Details pane.
 
 In order to use transcription and translation for past events, you must enable audio detection and define `speech` as an audio type to listen for. To have `speech` events translated into the language of your choice, set the `language` config parameter with the correct [language code](https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10).
 
@@ -294,7 +294,7 @@ Recorded `speech` events will always use a `whisper` model, regardless of the `m
 
    Because transcription is **serialized (one event at a time)** and speech events can be generated far faster than they can be processed, an auto-transcribe toggle would very quickly create an ever-growing backlog and degrade core functionality. For the amount of engineering and risk involved, it adds **very little practical value** for the majority of deployments, which are often on low-powered, edge hardware.
 
-   If you hear speech that's actually important and worth saving/indexing for the future, **just press the transcribe button in Explore** on that specific `speech` event - that keeps things explicit, reliable, and under your control.
+   If you hear speech that's actually important and worth saving/indexing for the future, **just press the transcribe button (the microphone icon) in Explore** on that specific `speech` event - that keeps things explicit, reliable, and under your control.
 
    Other options are being considered for future versions of Frigate to add transcription options that support external `whisper` Docker containers. A single transcription service could then be shared by Frigate and other applications (for example, Home Assistant Voice), and run on more powerful machines when available.
 

docs/docs/configuration/face_recognition.md

@@ -232,7 +232,21 @@ Once front-facing images are performing well, start choosing slightly off-angle
 
 Start with the [Usage](#usage) section and re-read the [Model Requirements](#model-requirements) above.
 
-1. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.
+1. Enable debug logs to see exactly what Frigate is doing.
+   - Enable debug logs for face recognition by adding `frigate.data_processing.real_time.face: debug` to your `logger` configuration. Restart Frigate after this change.
+
+     ```yaml
+     logger:
+       default: info
+       logs:
+         # highlight-next-line
+         frigate.data_processing.real_time.face: debug
+     ```
+
+   - These logs report where the pipeline stopped for each `person` object, such as no face being found within the person's bounding box, the detected face being smaller than `min_area`, or a face being recognized but scoring too low.
+   - If you see no face-related messages at all, also add `frigate.embeddings.maintainer: debug` to confirm that the face processor was created at startup and that `person` updates are reaching it.
+
+2. Ensure `person` is being _detected_. A `person` will automatically be scanned by Frigate for a face. Any detected faces will appear in the Recent Recognitions tab in the Frigate UI's Face Library.
 
    If you are using a Frigate+ or `face` detecting model:
    - Watch the [debug view](/usage/live#the-single-camera-view) to ensure that `face` is being detected along with `person`.
@@ -242,7 +256,7 @@ Start with the [Usage](#usage) section and re-read the [Model Requirements](#mod
    - Check your `detect` stream resolution and ensure it is sufficiently high enough to capture face details on `person` objects.
    - You may need to lower your `detection_threshold` if faces are not being detected.
 
-2. Any detected faces will then be _recognized_.
+3. Any detected faces will then be _recognized_.
    - Make sure you have trained at least one face per the recommendations above.
    - Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
 

docs/docs/configuration/genai/config.md

@@ -78,7 +78,7 @@ All llama.cpp native options can be passed through `provider_options`, including
    - Set **Provider** to `llamacpp`
    - Set **Base URL** to your llama.cpp server address (e.g., `http://localhost:8080`)
    - Set **Model** to the name of your model
-   - Under **Provider Options**, set `context_size` to tell Frigate your context size so it can send the appropriate amount of information
+   - Optionally, under **Provider Options**, set `context_size` to override the context size Frigate detects from the server
 
 </TabItem>
 <TabItem value="yaml">
@@ -89,12 +89,14 @@ genai:
   base_url: http://localhost:8080
   model: your-model-name
   provider_options:
-    context_size: 16000 # Tell Frigate your context size so it can send the appropriate amount of information.
+    context_size: 16000 # Optional, overrides the context size reported by the server.
 ```
 
 </TabItem>
 </ConfigTabs>
 
+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.
+
 ### 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.