frigate/docs/docs/configuration/audio_detectors.md

id	title
audio_detectors	Audio Detectors

Frigate provides a builtin audio detector which runs on the CPU. Compared to object detection in images, audio detection is a relatively lightweight operation so the only option is to run the detection on a CPU.

Configuration

Audio events work by detecting a type of audio and creating an event, the event will end once the type of audio has not been heard for the configured amount of time. Audio events save a snapshot at the beginning of the event as well as recordings throughout the event. The recordings are retained using the configured recording retention.

Enabling Audio Events

Audio events can be enabled for all cameras or only for specific cameras.

audio: # <- enable audio events for all camera
  enabled: True

cameras:
  front_camera:
    ffmpeg:
    ...
    audio:
      enabled: True # <- enable audio events for the front_camera

If you are using multiple streams then you must set the audio role on the stream that is going to be used for audio detection, this can be any stream but the stream must have audio included.

:::note

The ffmpeg process for capturing audio will be a separate connection to the camera along with the other roles assigned to the camera, for this reason it is recommended that the go2rtc restream is used for this purpose. See the restream docs for more information.

:::

cameras:
  front_camera:
    ffmpeg:
      inputs:
        - path: rtsp://.../main_stream
          roles:
            - record
        - path: rtsp://.../sub_stream # <- this stream must have audio enabled
          roles:
            - audio
            - detect

Configuring Minimum Volume

The audio detector uses volume levels in the same way that motion in a camera feed is used for object detection. This means that frigate will not run audio detection unless the audio volume is above the configured level in order to reduce resource usage. Audio levels can vary widely between camera models so it is important to run tests to see what volume levels are. The Debug view in the Frigate UI has an Audio tab for cameras that have the audio role assigned where a graph and the current levels are is displayed. The min_volume parameter should be set to the minimum the RMS level required to run audio detection.

:::tip

Volume is considered motion for recordings, this means when the record -> retain -> mode is set to motion any time audio volume is > min_volume that recording segment for that camera will be kept.

:::

Configuring Audio Events

The included audio model has over 500 different types of audio that can be detected, many of which are not practical. By default bark, fire_alarm, scream, speech, and yell are enabled but these can be customized.

audio:
  enabled: True
  listen:
    - bark
    - fire_alarm
    - scream
    - speech
    - yell

Audio Transcription

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.

Transcription accuracy also depends heavily on the quality of your camera's microphone and recording conditions. Many cameras use inexpensive microphones, and distance to the speaker, low audio bitrate, or background noise can significantly reduce transcription quality. If you need higher accuracy, more robust long-running queues, or large-scale automatic transcription, consider using the HTTP API in combination with an automation platform and a cloud transcription service.

Configuration

To enable transcription, enable it in your config. Note that audio detection must also be enabled as described above in order to use audio transcription features.

audio_transcription:
  enabled: True
  device: ...
  model_size: ...

Disable audio transcription for select cameras at the camera level:

cameras:
  back_yard:
    ...
    audio_transcription:
      enabled: False

:::note

Audio detection must be enabled and configured as described above in order to use audio transcription features.

:::

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.
• 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.
• 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. Recorded speech events will always use a different whisper model (and can be accelerated for CUDA hardware if available with device: GPU).
• language: Defines the language used by whisper to translate speech audio events (and live audio only if using the large model).
  • Default: en
  • You must use a valid language code.
  • 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.

Live transcription

The single camera Live view in the Frigate UI supports live transcription of audio for streams defined with the audio role. Use the Enable/Disable Live Audio Transcription button/switch to toggle transcription processing. When speech is heard, the UI will display a black box over the top of the camera stream with text. The MQTT topic frigate/<camera_name>/audio/transcription will also be updated in real-time with transcribed text.

Results can be error-prone due to a number of factors, including:

• Poor quality camera microphone
• Distance of the audio source to the camera microphone
• Low audio bitrate setting in the camera
• Background noise
• Using the small model - it's fast, but not accurate for poor quality audio

For speech sources close to the camera with minimal background noise, use the small model.

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

Any speech events in Explore can be transcribed and/or translated through the Transcribe button 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 in your config. To have speech events translated into the language of your choice, set the language config parameter with the correct language code.

The transcribed/translated speech will appear in the description box in the Tracked Object Details pane. If Semantic Search is enabled, embeddings are generated for the transcription text and are fully searchable using the description search type.

:::note

Only one speech event may be transcribed at a time. Frigate does not automatically transcribe speech events or implement a queue for long-running transcription model inference.

:::

Recorded speech events will always use a whisper model, regardless of the model_size config setting. Without a supported Nvidia GPU, generating transcriptions for longer speech events may take a fair amount of time, so be patient.

FAQ

1. Why doesn't Frigate automatically transcribe all speech events?

   Frigate does not implement a queue mechanism for speech transcription, and adding one is not trivial. A proper queue would need backpressure, prioritization, memory/disk buffering, retry logic, crash recovery, and safeguards to prevent unbounded growth when events outpace processing. That’s a significant amount of complexity for a feature that, in most real-world environments, would mostly just churn through low-value noise.

   Because transcription is serialized (one event at a time) and speech events can be generated far faster than they can be processed, an auto-transcribe toggle would very quickly create an ever-growing backlog and degrade core functionality. For the amount of engineering and risk involved, it adds very little practical value for the majority of deployments, which are often on low-powered, edge hardware.

   If you hear speech that’s actually important and worth saving/indexing for the future, just press the transcribe button in Explore on that specific speech event - that keeps things explicit, reliable, and under your control.

   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.

2. Why don't you save live transcription text and use that for speech events?

   There’s no guarantee that a speech event is even created from the exact audio that went through the transcription model. Live transcription and speech event creation are separate, asynchronous processes. Even when both are correctly configured, trying to align the precise start and end time of a speech event with whatever audio the model happened to be processing at that moment is unreliable.

   Automatically persisting that data would often result in misaligned, partial, or irrelevant transcripts, while still incurring all of the CPU, storage, and privacy costs of transcription. That’s why Frigate treats transcription as an explicit, user-initiated action rather than an automatic side-effect of every speech event.