WaveKat VAD

Voice Activity Detection library for Rust with multiple backend support.

Quick Start

use wavekat_vad::VoiceActivityDetector;
use wavekat_vad::backends::webrtc::{WebRtcVad, WebRtcVadMode};

let mut vad = WebRtcVad::new(16000, WebRtcVadMode::Quality).unwrap();
let samples: Vec<i16> = vec![0; 160]; // 10ms at 16kHz
let probability = vad.process(&samples, 16000).unwrap();

Backends

[dependencies]
wavekat-vad = "0.1"                    # WebRTC only (default)
wavekat-vad = { version = "0.1", features = ["silero"] }
wavekat-vad = { version = "0.1", features = ["ten-vad"] }
wavekat-vad = { version = "0.1", features = ["webrtc", "silero", "ten-vad"] }  # all backends

Benchmarks

Performance measured against the TEN-VAD testset — 30 audio files from LibriSpeech, GigaSpeech, and DNS Challenge with manual speech/non-speech annotations. Threshold: 0.5.

v0.1.5

Accuracy metrics are deterministic; inference times are approximate and vary by hardware. Measured with --release on GitHub Actions ubuntu-latest runners. Run locally: make accuracy or make bench

WebRTC

Google's WebRTC VAD. Fast and lightweight, returns binary speech/silence detection. Supports four aggressiveness modes.

use wavekat_vad::VoiceActivityDetector;
use wavekat_vad::backends::webrtc::{WebRtcVad, WebRtcVadMode};

// Default 30ms frame duration
let mut vad = WebRtcVad::new(16000, WebRtcVadMode::Quality).unwrap();

// Or specify frame duration (10, 20, or 30ms)
let mut vad = WebRtcVad::with_frame_duration(16000, WebRtcVadMode::Aggressive, 20).unwrap();

let samples = vec![0i16; 320]; // 20ms at 16kHz
let result = vad.process(&samples, 16000).unwrap(); // 0.0 or 1.0

Silero

Neural network (LSTM) via ONNX Runtime. Returns continuous probability, higher accuracy than WebRTC. Only supports 8kHz and 16kHz.

use wavekat_vad::VoiceActivityDetector;
use wavekat_vad::backends::silero::SileroVad;

let mut vad = SileroVad::new(16000).unwrap();
let samples = vec![0i16; 512]; // 32ms at 16kHz
let probability = vad.process(&samples, 16000).unwrap(); // 0.0–1.0

// Or load a custom model
let vad = SileroVad::from_file("path/to/model.onnx", 16000).unwrap();

TEN-VAD

Agora's TEN-VAD with pure Rust preprocessing (no C dependency). Returns continuous probability, 16kHz only.

use wavekat_vad::VoiceActivityDetector;
use wavekat_vad::backends::ten_vad::TenVad;

let mut vad = TenVad::new().unwrap();
let samples = vec![0i16; 256]; // 16ms at 16kHz
let probability = vad.process(&samples, 16000).unwrap(); // 0.0–1.0

The VoiceActivityDetector Trait

All backends implement a common trait, so you can write code that is generic over backends:

use wavekat_vad::{VoiceActivityDetector, VadCapabilities};

fn detect_speech(vad: &mut dyn VoiceActivityDetector, audio: &[i16], sample_rate: u32) {
    let caps = vad.capabilities();
    // caps.sample_rate  — required sample rate
    // caps.frame_size   — required frame size in samples
    // caps.frame_duration_ms — frame duration

    for frame in audio.chunks_exact(caps.frame_size) {
        let probability = vad.process(frame, sample_rate).unwrap();
        if probability > 0.5 {
            println!("Speech detected!");
        }
    }
}

FrameAdapter

Real-world audio arrives in arbitrary chunk sizes. FrameAdapter buffers incoming samples and feeds correctly-sized frames to the backend automatically.

use wavekat_vad::FrameAdapter;
use wavekat_vad::backends::silero::SileroVad;

let vad = SileroVad::new(16000).unwrap();
let mut adapter = FrameAdapter::new(Box::new(vad));

// Feed arbitrary-sized chunks — adapter handles buffering
let chunk = vec![0i16; 1000]; // not a multiple of 512

// Get all complete frame results at once
let probabilities = adapter.process_all(&chunk, 16000).unwrap();

// Or get just the latest result (convenient for real-time)
let latest = adapter.process_latest(&chunk, 16000).unwrap();

// Or process one frame at a time
let result = adapter.process(&chunk, 16000).unwrap(); // Some(prob) or None

Preprocessing

Optional audio preprocessing to improve VAD accuracy. Available stages: high-pass filter, noise suppression, and amplitude normalization.

use wavekat_vad::preprocessing::{Preprocessor, PreprocessorConfig};

// Use a preset
let config = PreprocessorConfig::raw_mic();     // 80Hz HP + normalize + denoise
// let config = PreprocessorConfig::telephony(); // 200Hz HP only

// Or configure manually
let config = PreprocessorConfig {
    high_pass_hz: Some(80.0),       // remove low-frequency rumble
    denoise: false,                  // requires "denoise" feature
    normalize_dbfs: Some(-20.0),     // normalize amplitude
};

let mut preprocessor = Preprocessor::new(&config, 16000);
let raw_audio: Vec<i16> = vec![0; 512];
let cleaned = preprocessor.process(&raw_audio);
// feed `cleaned` to your VAD

Feature Flags

ONNX Model Downloads

Silero and TEN-VAD models are downloaded automatically at build time. For offline or CI builds, point to a local model file:

SILERO_MODEL_PATH=/path/to/silero_vad.onnx cargo build --features silero
TEN_VAD_MODEL_PATH=/path/to/ten-vad.onnx cargo build --features ten-vad

Error Handling

All backends return Result<f32, VadError>. The error type covers:

• VadError::InvalidSampleRate(u32) — unsupported sample rate for the backend
• VadError::InvalidFrameSize { got, expected } — wrong number of samples
• VadError::BackendError(String) — backend-specific error (e.g., ONNX failure)

Use capabilities() to check a backend's requirements before processing.

vad-lab

Dev tool for live VAD experimentation. Captures audio server-side and streams results to a web UI.

Quick Start

make setup         # Install dependencies (once)
make dev-backend   # Terminal 1
make dev-frontend  # Terminal 2

License

Apache-2.0

TEN-VAD model notice

The TEN-VAD ONNX model (used by the ten-vad feature) is licensed under Apache-2.0 with a non-compete clause by the TEN-framework / Agora. It restricts deployment that competes with Agora's offerings and limits deployment to "solely for your benefit and the benefit of your direct End Users." This is not standard open-source despite the Apache-2.0 label. Review the TEN-VAD license before using in production.

Third-party notices

This project uses nnnoiseless (BSD-3-Clause) for noise suppression via the denoise feature.