github.com/TEN-framework/ten-vad @v1.0

![TEN VAD banner][ten-vad-banner]

Latest News 🔥 - [2025/06] We are excited to announce the release of WASM+JS for Web WASM Support.

Table of Contents

• Welcome to TEN
• TEN Hugging Face Space
• Introduction
• Key Features
• High-Performance
• Agent-Friendly
• Lightweight
• Multiple Programming Languages and Platforms
• Supported Sampling Rate and Hop Size
• Installation
• Quick Start
• Python Usage
  • Linux
• JS Usage
  • Web
• C Usage
  • Linux
  • Windows
  • macOS
  • Android
  • iOS
• TEN Ecosystem
• Ask Questions
• Citations
• Usage Guidance
• Future Open Source Plan

Welcome to TEN

TEN is a collection of open-source projects for building real-time, multimodal conversational voice agents. It includes TEN Framework , TEN Turn Detection , TEN Agent, TMAN Designer, and TEN Portal , all fully open-source. TEN VAD is not fully open-source yet, but it is open for public use.

Community Channel	Purpose
	Follow TEN Framework on X for updates and announcements
	Follow TEN Framework on LinkedIn for updates and announcements
	Join our Discord community to connect with developers
	Join our Hugging Face community to explore our spaces and models
	Join our WeChat group for Chinese community discussions

[!IMPORTANT]

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TEN Hugging Face Space

https://github.com/user-attachments/assets/725a8318-d679-4b17-b9e4-e3dce999b298

You are more than welcome to Visit TEN Hugging Face Space to try VAD and Turn Detection together.

Introduction

TEN VAD is a real-time voice activity detection system designed for enterprise use, providing accurate frame-level speech activity detection. It shows superior precision compared to both WebRTC VAD and Silero VAD, which are commonly used in the industry. Additionally, TEN VAD offers lower computational complexity and reduced memory usage compared to Silero VAD. Meanwhile, the architecture's temporal efficiency enables rapid voice activity detection, significantly reducing end-to-end response and turn detection latency in conversational AI systems.

Key Features

1. High-Performance:

The precision-recall curves comparing the performance of WebRTC VAD (pitch-based), Silero VAD, and TEN VAD are shown below. The evaluation is conducted on the precisely manually annotated testset. The audio files are from librispeech, gigaspeech, DNS Challenge etc. As demonstrated, TEN VAD achieves the best performance. Additionally, cross-validation experiments conducted on large internal real-world datasets demonstrate the reproducibility of these findings. The testset with annotated labels is released in directory "testset" of this repository.

Note that the default threshold of 0.5 is used to generate binary speech indicators (0 for non-speech signal, 1 for speech signal). This threshold needs to be tuned according to your domain-specific task. The precision-recall curve can be obtained by executing the following script on Linux x64. The output figure will be saved in the same directory as the script.

cd ./examples
python plot_pr_curves.py

2. Agent-Friendly:

As illustrated in the figure below, TEN VAD rapidly detects speech-to-non-speech transitions, whereas Silero VAD suffers from a delay of several hundred milliseconds, resulting in increased end-to-end latency in human-agent interaction systems. In addition, as demonstrated in the 6.5s-7.0s audio segment, Silero VAD fails to identify short silent durations between adjacent speech segments.

3. Lightweight:

We evaluated the RTF (Real-Time Factor) across five distinct platforms, each equipped with varying CPUs. TEN VAD demonstrates much lower computational complexity and smaller library size than Silero VAD.

Platform	CPU	RTF	Lib Size
TEN VAD	Silero VAD	TEN VAD	Silero VAD
Linux	AMD Ryzen 9 5900X 12-Core	0.0150	/	306KB	2.16MB(JIT) / 2.22MB(ONNX)
Intel(R) Xeon(R) Platinum 8253	0.0136
Intel(R) Xeon(R) Gold 6348 CPU @ 2.60GHz	0.0086	0.0127
Windows	Intel i7-10710U	0.0150	/	464KB(x86) / 508KB(x64)
macOS	M1	0.0160	731KB
Web	macOS(M1)	0.010	277KB
Android	Galaxy J6+ (32bit, 425)	0.0570	373KB(v7a) / 532KB(v8a)
Oppo A3s (450)	0.0490
iOS	iPhone6 (A8)	0.0210	320KB
iPhone8 (A11)	0.0050

4. Multiple programming languages and platforms:

TEN VAD provides cross-platform C compatibility across five operating systems (Linux x64, Windows, macOS, Android, iOS), with Python bindings optimized for Linux x64, with wasm for Web.

5. Supproted sampling rate and hop size:

TEN VAD operates on 16kHz audio input with configurable hop sizes (optimized frame configurations: 160/256 samples=10/16ms). Other sampling rates must be resampled to 16kHz.

Installation

git clone https://github.com/TEN-framework/ten-vad.git

Quick Start

The project supports five major platforms with dynamic library linking.

Platform	Dynamic Lib	Supported Arch	Interface Language	Header	Comment
Linux	libten_vad.so	x64	Python, C	ten_vad.h ten_vad.py ten_vad.js
Windows	ten_vad.dll	x64, x86	C
macOS	ten_vad.framework	arm64, x86_64	C
Web	ten_vad.wasm	/	JS
Android	libten_vad.so	arm64-v8a, armeabi-v7a	C
iOS	ten_vad.framework	arm64	C	1. not simulator 2. not iPad

Python Usage

1. Linux

Requirements

• numpy (Version 1.17.4/1.26.4 verified)
• scipy (Version >= 1.5.0)
• scikit-learn (Version 1.2.2/1.5.0 verified, for plotting PR curves)
• matplotlib (Version 3.1.3/3.10.0 verified, for plotting PR curves)

• torchaudio (Version 2.2.2 verified, for plotting PR curves)

• Python version 3.8.19/3.10.14 verified

Note: You could use other versions of above packages, but we didn't test other versions.

The lib only depend on numpy, you have to install the dependency via requirements.txt:

pip install -r requirements.txt

For running demo or plotting PR curves, you have to install the dependencies:

pip install -r ./examples/requirements.txt

Note that if you did not install libc++1, you have to run the code below to install it:

sudo apt update
sudo apt install libc++1

Usage

Note: For usage in python, you can either use it by git clone or pip.

By using git clone:

1. Clone the repository

git clone https://github.com/TEN-framework/ten-vad.git

1. Enter examples directory

cd ./examples

1. Test

python test.py s0724-s0730.wav out.txt

By using pip:

1. Install via pip

pip install -U --force-reinstall -v git+https://github.com/TEN-framework/ten-vad.git

1. Write your own use cases and import the class, the attributes of class TenVAD you can refer to ten_vad.py

from ten_vad import TenVad

JS Usage

1. Web

Requirements

• Node.js (macOS v14.18.2, Linux v16.20.2 verified)
• Terminal

Usage

1) cd ./examples
2) node test_node.js s0724-s0730.wav out.txt

C Usage

Build Scripts

Located in examples/ directory:

• Linux: build-and-deploy-linux.sh
• Windows: build-and-deploy-windows.bat
• macOS: build-and-deploy-mac.sh
• Android: build-and-deploy-android.sh
• iOS: build-and-deploy-ios.sh

Dynamic Library Configuration

Runtime library path configuration: - Linux/Android: LD_LIBRARY_PATH - macOS: DYLD_FRAMEWORK_PATH - Windows: DLL in