fast-vad 0.2.0

Extremely fast voice activity detection in Rust with Python bindings and streaming mode support.

fast-vad

Extremely fast voice activity detection in Rust with Python bindings and streaming mode support.

Supports 16 kHz and 8 kHz audio. Fixed frame width of 32 ms (512 samples at 16 kHz and 256 samples at 8 kHz).

If you are interested in benchmark comparisons, see docs/README.md.

Benchmarking

Python benchmarks live in bench_py/ and Rust benchmarks live in bench_rs/.

uv run pytest bench_py/bench_vad.py bench_py/bench_feature_extractor.py --benchmark-sort=mean --benchmark-group-by=group
cargo bench --manifest-path bench_rs/Cargo.toml

Install

Python

pip install fast-vad

Or with uv:

uv add fast-vad

Rust

cargo add fast-vad

Architecture

fast_vad is a small fixed-frame DSP pipeline with a hardcoded lightweight classifier.

audio
  -> 32 ms frames
     - 16 kHz: 512 samples
     - 8 kHz: 256 samples
  -> Hann window
  -> real FFT
  -> 8 log-energy bands
  -> feature engineering
     - raw bands          (8)
     - noise-normalized   (8)
     - first deltas       (8)
     - second deltas      (8)
     = 32 total features
  -> hardcoded logistic regression
  -> threshold + smoothing
  -> speech / silence labels

At a glance:

• VAD (offline / batch) splits audio into 32 ms frames and uses rayon to process complete frames in parallel while extracting the 8-band features.
• VadStateful (streaming) runs the same per-frame pipeline one frame at a time and reuses scratch buffers instead of paying thread-pool overhead.
• The detector keeps a running 8-band noise floor, then derives 32 total features from each frame: raw band energies, noise-normalized energies, first-order deltas, and second-order deltas.
• Classification is a tiny hardcoded logistic-regression-style model with fixed weights and bias compiled into the crate.
• The final decision is shaped by simple temporal rules: thresholding, minimum speech length, minimum silence length, and hangover.
• Hot loops are SIMD-accelerated with the wide crate for windowing, spectral power computation, band-energy math, and detector feature math.

frame features (8 bands)
    | raw
    | raw - noise_floor
    | delta
    | delta2
    v
32 engineered features
    v
linear score + bias
    v
speech / silence

Build from source

Python (with uv)

Requires uv and a Rust toolchain.

git clone https://github.com/AtharvBhat/fast-vad
cd fast-vad
uv venv
uv pip install maturin
uv run maturin develop --release

The package is then importable inside the virtual environment.

Rust

cargo build --release

Add as a dependency in another crate:

[dependencies]
fast-vad = { path = "/path/to/fast-vad" }

Python usage

Config is set at construction time. VAD() and VadStateful() default to Normal mode; use with_mode or with_config to customise.

import numpy as np
import soundfile as sf
import fast_vad

audio, sr = sf.read("audio.wav", dtype="float32")
assert sr in (8000, 16000)

# Default (Normal mode)
vad = fast_vad.VAD(sr)

# Explicit mode
vad = fast_vad.VAD.with_mode(sr, fast_vad.mode.aggressive)

# Custom parameters
vad = fast_vad.VAD.with_config(
    sr,
    threshold_probability=0.7,
    min_speech_ms=100,
    min_silence_ms=300,
    hangover_ms=100,
)

# Per-sample labels
labels = vad.detect(audio)

# Per-frame labels
frame_labels = vad.detect_frames(audio)

# Speech segments as a (N, 2) uint64 numpy array of [start, end] sample indices
segments = vad.detect_segments(audio)
for start, end in segments:
    print(f"speech: {start/sr:.2f}s – {end/sr:.2f}s")

Streaming

# Default (Normal mode)
vad = fast_vad.VadStateful(sr)

# Explicit mode
vad = fast_vad.VadStateful.with_mode(sr, fast_vad.mode.normal)

# Custom parameters
vad = fast_vad.VadStateful.with_config(sr, 0.7, 100, 300, 100)

frame_size = vad.frame_size  # 512 at 16 kHz, 256 at 8 kHz

for i in range(0, len(audio) - frame_size + 1, frame_size):
    is_speech = vad.detect_frame(audio[i : i + frame_size])
    print(f"frame {i // frame_size}: {'speech' if is_speech else 'silence'}")

vad.reset_state()  # reuse for another stream

Feature extraction

fe = fast_vad.FeatureExtractor(sr)
features = fe.extract_features(audio)  # shape: (num_frames, 8)

Modes

Constant	Description
fast_vad.mode.permissive	Low false-negative rate; more speech accepted
fast_vad.mode.normal	Balanced, general-purpose
fast_vad.mode.aggressive	Low false-positive rate; stricter

Rust usage

Config is set at construction. VAD::new and VadStateful::new default to Normal mode; use with_mode or with_config to customise.

use fast_vad::vad::detector::{VAD, VADModes, VadConfig};

fn main() -> Result<(), fast_vad::VadError> {
    let audio = vec![0.0f32; 16000]; // 1 second of silence

    // Default (Normal mode)
    let vad = VAD::new(16000)?;

    // Explicit mode
    let vad = VAD::with_mode(16000, VADModes::Aggressive)?;

    // Custom parameters
    let vad = VAD::with_config(16000, VadConfig {
        threshold_probability: 0.7,
        min_speech_ms: 100,
        min_silence_ms: 300,
        hangover_ms: 100,
    })?;

    let labels = vad.detect(&audio);           // one bool per sample
    let frame_labels = vad.detect_frames(&audio); // one bool per frame
    let segments = vad.detect_segments(&audio);   // Vec<[start, end]>

    Ok(())
}

Streaming

use fast_vad::vad::detector::{VadStateful, VADModes, VadConfig};

fn main() -> Result<(), fast_vad::VadError> {
    let audio = vec![0.0f32; 16000];

    // Default (Normal mode)
    let mut vad = VadStateful::new(16000)?;

    // Explicit mode
    let mut vad = VadStateful::with_mode(16000, VADModes::Normal)?;

    // Custom parameters
    let mut vad = VadStateful::with_config(16000, VadConfig {
        threshold_probability: 0.7,
        min_speech_ms: 100,
        min_silence_ms: 300,
        hangover_ms: 100,
    })?;

    let frame_size = vad.frame_size();
    for frame in audio.chunks_exact(frame_size) {
        let is_speech = vad.detect_frame(frame)?;
        println!("{is_speech}");
    }

    vad.reset_state(); // reuse for another stream
    Ok(())
}

License

Licensed under either of

• Apache License, Version 2.0 (LICENSE-APACHE)
• MIT license (LICENSE-MIT)

at your option.