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@@ -272,7 +272,7 @@ If you have CUDA hardware, you can experiment with the `large` `whisper` model o
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#### Transcription and translation of `speech` audio events
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Any `speech` events in Explore can be transcribed and/or translated through the Transcribe button in the Tracked Object Details pane.
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Any `speech` events in Explore can be transcribed and/or translated through the Transcribe button (the microphone icon) in the Tracked Object Details pane.
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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).
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@@ -294,7 +294,7 @@ Recorded `speech` events will always use a `whisper` model, regardless of the `m
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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.
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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.
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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.
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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.
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@@ -232,7 +232,21 @@ Once front-facing images are performing well, start choosing slightly off-angle
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Start with the [Usage](#usage) section and re-read the [Model Requirements](#model-requirements) above.
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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.
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1. Enable debug logs to see exactly what Frigate is doing.
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- Enable debug logs for face recognition by adding `frigate.data_processing.real_time.face: debug` to your `logger` configuration. Restart Frigate after this change.
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```yaml
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logger:
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default: info
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logs:
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# highlight-next-line
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frigate.data_processing.real_time.face: debug
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```
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- 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.
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- 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.
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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.
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If you are using a Frigate+ or `face` detecting model:
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- Watch the [debug view](/usage/live#the-single-camera-view) to ensure that `face` is being detected along with `person`.
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@@ -242,7 +256,7 @@ Start with the [Usage](#usage) section and re-read the [Model Requirements](#mod
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- Check your `detect` stream resolution and ensure it is sufficiently high enough to capture face details on `person` objects.
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- You may need to lower your `detection_threshold` if faces are not being detected.
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2. Any detected faces will then be _recognized_.
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3. Any detected faces will then be _recognized_.
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- Make sure you have trained at least one face per the recommendations above.
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- Adjust `recognition_threshold` settings per the suggestions [above](#advanced-configuration).
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@@ -1936,7 +1936,8 @@
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"inputDimensionsNotDetectResolution": "Model input width and height are the input dimensions of the object detection model, not your camera's detect resolution. They should match the dimensions of the model you're using — typically a square size like 320x320 or 640x640."
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},
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"ffmpeg": {
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"hwaccelManualNotRecommended": "Manual hardware acceleration arguments are not recommended. Unless a specific requirement exists, select the preset that matches your hardware."
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"hwaccelManualNotRecommended": "Manual hardware acceleration arguments are not recommended. Unless a specific requirement exists, select the preset that matches your hardware.",
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"inputsMissingGo2rtcStream": "An input below points at a go2rtc restream that no longer exists. Select an existing restream or enter the camera's URL manually, otherwise this camera will fail to connect."
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},
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"objects": {
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"genaiNoDescriptionsProvider": "You must configure a GenAI provider with the 'descriptions' role for descriptions to be generated."
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@@ -1,3 +1,4 @@
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import { parseRestreamStreamName } from "../theme/fields/streamSource";
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import type { SectionConfigOverrides } from "./types";
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const arrayAsTextWidget = {
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@@ -42,6 +43,29 @@ const ffmpeg: SectionConfigOverrides = {
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return false;
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},
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},
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{
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key: "inputs-missing-go2rtc-stream",
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field: "inputs",
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position: "before",
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messageKey: "configMessages.ffmpeg.inputsMissingGo2rtcStream",
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severity: "warning",
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docLink: "/configuration/restream",
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condition: (ctx) => {
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const streams = ctx.fullConfig?.go2rtc?.streams;
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const inputs = ctx.formData?.inputs;
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if (!Array.isArray(inputs)) {
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return false;
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}
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return inputs.some((input) => {
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const path = (input as { path?: unknown } | null)?.path;
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const streamName = parseRestreamStreamName(
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typeof path === "string" ? path : undefined,
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);
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return streamName !== undefined && !(streamName in (streams ?? {}));
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});
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},
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},
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],
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fieldDocs: {
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hwaccel_args: "/configuration/ffmpeg_presets#hwaccel-presets",
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@@ -170,6 +170,12 @@ export function CameraInputsField(props: FieldProps) {
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[go2rtcStreamNames],
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);
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useEffect(() => {
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setSourceModeByIndex((previous) =>
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Object.keys(previous).length > 0 ? {} : previous,
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);
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}, [formContext?.cameraName]);
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useEffect(() => {
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setOpenByIndex((previous) => {
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const next: Record<number, boolean> = {};
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@@ -222,18 +228,12 @@ export function CameraInputsField(props: FieldProps) {
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const handleSourceModeChange = useCallback(
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(index: number, nextMode: StreamSourceMode) => {
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const input = inputs[index];
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const currentPath =
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typeof input?.path === "string" ? input.path : undefined;
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if (nextMode === "manual") {
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// Only revert the preset we set ourselves; never clobber custom args.
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if (input?.input_args === RESTREAM_PRESET) {
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handleFieldValuesChange(index, { input_args: undefined });
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}
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} else if (!parseRestreamStreamName(currentPath)) {
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// Entering restream with a non-restream path: clear it so the dropdown
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// shows its placeholder until a stream is chosen.
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handleFieldValuesChange(index, { path: undefined });
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// Only revert the preset we set ourselves; never clobber custom args.
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// The path is left alone until a stream is picked, so switching modes
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// never discards a typed URL or empties a required field.
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if (nextMode === "manual" && input?.input_args === RESTREAM_PRESET) {
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handleFieldValuesChange(index, { input_args: undefined });
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}
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setSourceModeByIndex((previous) => ({ ...previous, [index]: nextMode }));
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