Voice Activity Detection — Overview - rapida.ai documentation

Voice Activity Detection (VAD) runs on every audio frame before STT. It determines whether the caller is speaking, emits interruption signals for barge-in, and sends speech activity heartbeats that keep the End of Speech timer from firing during active speech. All three VAD providers run locally using ONNX or native C libraries. No external API calls, no credentials, no added latency.

VAD Interface

Every provider implements the Vad interface:

// api/assistant-api/internal/type/vad.go
type Vad interface {
    Name() string
    Process(ctx context.Context, pkt UserAudioPacket) error
    Close() error
}

Input audio is always 16 kHz LINEAR16 mono — the platform’s internal format. Resampling from 8 kHz telephony audio happens upstream before VAD sees it.

Factory Function

// api/assistant-api/internal/vad/vad.go
func GetVAD(ctx, logger, callback, options) (Vad, error)

The factory reads microphone.vad.provider from the assistant’s audio options and returns the matching implementation. If no provider is set, Silero VAD is used as the default.

Provider Identifiers

Identifier	Provider	Model
`silero_vad`	Silero VAD	`silero_vad_20251001.onnx` (~2 MB)
`ten_vad`	TEN VAD	Native C library (no model file)
`firered_vad`	FireRed VAD	`fireredvad_stream_vad_with_cache.onnx` (~5 MB)

Shared Parameters

All three providers use the same configuration keys:

Option Key	Type	Default	Description
`microphone.vad.provider`	string	`silero_vad`	Provider selection
`microphone.vad.threshold`	float	`0.5`	Speech probability threshold (0.3–1.0). Lower = more sensitive.
`microphone.vad.min_silence_frame`	float	`20`	Consecutive silence frames before ending a speech segment. Each frame = 10 ms. Default 20 = 200 ms.
`microphone.vad.min_speech_frame`	float	`8`	Consecutive speech frames before confirming speech onset. Each frame = 10 ms. Default 8 = 80 ms.

Model Files

VAD models are bundled in the Docker image at build time. For source builds, models are resolved from the source tree relative to each provider’s Go package directory.

Docker

Models are copied into the runtime image and referenced via environment variables:

SILERO_MODEL_PATH=./models/silero_vad/silero_vad_20251001.onnx
FIRERED_VAD_MODEL_PATH=./models/firered_vad/fireredvad_stream_vad_with_cache.onnx

TEN VAD uses a native shared library instead of an ONNX model:

LD_LIBRARY_PATH="/opt/ten_vad/lib:..."

These are set automatically in the Dockerfile — no manual configuration needed when running via Docker Compose.

From Source

When running go run cmd/assistant/assistant.go, each provider resolves its model path using runtime.Caller to find the source directory:

Provider	Default path (relative to package dir)
Silero VAD	`api/assistant-api/internal/vad/internal/silero_vad/models/silero_vad_20251001.onnx`
FireRed VAD	`api/assistant-api/internal/vad/internal/firered_vad/models/fireredvad_stream_vad_with_cache.onnx`
TEN VAD	No model file — requires `libten_vad.so` in the library path

Silero and FireRed models are checked into the repository. TEN VAD requires the shared library to be installed or available in LD_LIBRARY_PATH / DYLIB_LIBRARY_PATH. To override model paths, set the environment variables:

export SILERO_MODEL_PATH=/path/to/silero_vad.onnx
export FIRERED_VAD_MODEL_PATH=/path/to/fireredvad.onnx

CGO Dependencies

All VAD providers use CGO for inference:

Provider	CGO dependency	Build flags
Silero VAD	ONNX Runtime (`libonnxruntime`)	`-I/opt/onnxruntime/include -L/opt/onnxruntime/lib -lonnxruntime`
FireRed VAD	ONNX Runtime (`libonnxruntime`)	Same as Silero
TEN VAD	TEN VAD library (`libten_vad`)	`-I/opt/ten_vad/include -L/opt/ten_vad/lib -lten_vad`

The Docker base image (rapidaai/rapida-golang) includes all CGO dependencies pre-installed. For local source builds, you need ONNX Runtime and the TEN VAD shared library installed on your system.

Providers

Provider	Approach	Best for
Silero VAD	ONNX model, 100+ languages	General purpose — the default
TEN VAD	Native C library, 16 ms frame hops	Lowest per-frame latency
FireRed VAD	DFSMN model, 4-state postprocessor	Noisy environments, precise boundaries

See the VAD concepts guide for detailed parameter tuning guidance and use-case recommendations.

Getting Started

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Voice Activity Detection — Overview

VAD Interface

Factory Function

Provider Identifiers

Shared Parameters

Model Files

Docker

From Source

CGO Dependencies

Providers

​VAD Interface

​Factory Function

​Provider Identifiers

​Shared Parameters

​Model Files

​Docker

​From Source

​CGO Dependencies

​Providers

VAD Interface

Factory Function

Provider Identifiers

Shared Parameters

Model Files

Docker

From Source

CGO Dependencies

Providers