livekit-plugins-tenvad provides seamless integration of the TEN-framework/ten-vad voice activity detection (VAD) plugin into the LiveKit ecosystem.
This plugin enables real-time speech activity detection with low-latency inference, optimized for streaming, conversational AI, and livekit-agents integration.
- 🔌 LiveKit plugin integration — plug-and-play support for LiveKit workflows
- 🤖 Compatible with livekit-agents — extend agents with real-time VAD capabilities
- 🎤 Accurate voice activity detection powered by TEN VAD
- ⚡ Low-latency inference (~0.17ms avg per frame) suitable for real-time use
- 📊 Benchmark validated against Silero VAD (faster and more continuous speech detection)
- 🛠️ Configurable & extensible within the LiveKit plugin system
# from PyPI
uv pip install livekit-plugins-tenvad
# from source
uv pip install git+https://github.com/dangvansam/livekit-plugins-tenvad.gitfrom livekit.plugins import tenvad
vad = tenvad.VAD.load(
activation_threshold=0.5,
min_speech_duration=0.1,
min_silence_duration=0.3,
max_buffered_speech=60,
prefix_padding_duration=0.3
)git clone https://github.com/dangvansam/livekit-plugins-tenvad.git
cd livekit-plugins-tenvad
# install dependencies for testing
uv pip install .[test]
python test/benchmark.py -i test/sample.wav| Metric | Silero VAD | TEN VAD |
|---|---|---|
| Speech segments | 95 | 41 |
| Total speech | 19.01s (13.0%) | 114.98s (78.8%) |
| Avg inference time | 0.22ms | 0.17ms |
| Min inference time | 0.18ms | 0.14ms |
| Max inference time | 9.76ms | 0.78ms |
Highlights:
- TEN VAD is ~1.27× faster per frame
- Detects longer continuous speech compared to Silero
- Provides lower latency with fewer false segment splits
@misc{TEN VAD,
author = {TEN Team},
title = {TEN VAD: A Low-Latency, Lightweight and High-Performance Streaming Voice Activity Detector (VAD)},
year = {2025},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/TEN-framework/ten-vad.git}},
email = {[email protected]}
}