Ten Vad

Latest News 🔥

• [2025/11] WASM build guide and browser test demo are now available in lib/Web and examples.
• [2025/11] We supported Python inference with ONNX model on Linux, macOS thanks to Guy Nicholson!
• [2025/11] We supported Golang on Linux, macOS and Windows with usage of the prebuilt-libs thanks to hylarucoder!
• [2025/11] We supported Java on Linux, macOS, Windows, Android with usage of the prebuilt-libs thanks to ZhangYang!
• [2025/07] 🎉 Exciting news! TEN VAD is now integrated into k2-fsa/sherpa-onnx, thanks to the fantastic work by Fangjun Kuang! You can now achieve more precise speech segment extraction and enjoy an enhanced ASR experience! Refer to the documentation and give it a try!
• [2025/07] We support Python inference on macOS and Windows with usage of the prebuilt-lib!
• [2025/06] We finally released and open-sourced the ONNX model and the corresponding preprocessing code! Now you can deploy TEN VAD on any platform and any hardware architecture!
• [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
    • Performance Comparison
  • Agent-Friendly
  • Lightweight
  • Multiple Programming Languages and Platforms
  • Supported Sampling Rate and Hop Size
• Developers Testimonial
• Installation
• Quick Start
  • Python Usage
    • Linux
  • JS Usage
    • Web
  • C Usage
    • Linux
    • Windows
    • macOS
    • Android
    • iOS
• TEN Ecosystem
• Ask Questions
• Citations
• License

Welcome to TEN

TEN is a collection of open-source projects for building real-time, multimodal conversational voice agents. It includes TEN Framework , TEN VAD , TEN Turn Detection , TEN Agent, TMAN Designer, and TEN Portal , all fully open-source.

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

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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.

**1.1 Performance Comparison**

Developers can reproduce the performance comparison PR curves for TEN VAD and Silero VAD on the open-source testset (as shown in the figure above) by executing the following script on Linux x64 with a simply one line of code. 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.

**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.

**Developers Testimonial**

"We selected TEN VAD because it provides faster and more accurate sentence-end detection in Japanese compared to other VADs, while still being lightweight and fast enough for live use." - LiveCap,Hakase shojo.

"TEN VAD's overall performance is better than Silero VAD. Its high accuracy and low resource consumption helped us improve efficiency and significantly reduce costs." - Rustpbx.

**Installation**

git clone https://huggingface.co/TEN-framework/ten-vad

**Quick Start**

The project supports five major platforms with dynamic library linking.

**Python Usage**

**1. Linux / macOS / Windows**

**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

2. Enter examples directory

cd ./examples

3. 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

2. 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 and examples_onnx (for ONNX usage on Linux):

• 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 executable directory or system PATH

**Customization**

• Modify platform-specific build scripts
• Adjust CMakeLists.txt
• Configure toolchain and architecture settings

**Overview of Usage**

• Navigate to examples/ or examples_onx/ (for ONNX usage on Linux)
• Execute platform-specific build script
• Configure dynamic library path
• Run demo with sample audio s0724-s0730.wav
• Processed results saved to out.txt

The detailed usage methods of each platform are as follows

**1. Linux**

**Requirements**

• Clang (e.g. 6.0.0-1ubuntu2 verified)
• CMake
• Terminal

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

sudo apt update
sudo apt install libc++1

**Usage (prebuilt-lib)**

1) cd ./examples
2) ./build-and-deploy-linux.sh

**Usage (ONNX)**

You have to download the onnxruntime packages from the microsoft official onnxruntime github website. Note that the version of onnxruntime must be higher than or equal to 1.17.1 (e.g. onnxruntime-linux-x64-1.17.1.tgz).
You can check the official ONNX Runtime releases from this website. And for example, to download version 1.17.1 (Linux x64), use this link. After extracting the compressed file, you'll find two important directories:include/ - header files, lib/ - library files

1) cd examples_onnx/
2) ./build-and-deploy-linux.sh --ort-path /absolute/path/to/your/onnxruntime/root/dir

Note 1: If executing the onnx demo from a different directory than the one used when running build-and-deploy-linux.sh, ensure to create a symbolic link to src/onnx_model/ to prevent ONNX model file loading failures.
Note 2: The ONNX model locates in src/onnx_model directory.

**2. Windows**

**Requirements**

• Visual Studio (2017, 2019, 2022 verified)
• CMake (3.26.0-rc6 verified)
• Terminal (MINGW64 or powershell)

**Usage**

1) cd ./examples
2) Configure "build-and-deploy-windows.bat" with your preferred:
    - Architecture (default: x64)
    - Visual Studio version (default: 2019)
3) ./build-and-deploy-windows.bat

**3. macOS**

**Requirements**

• Xcode (15.2 verified)
• CMake (3.19.2 verified)

**Usage**

1) cd ./examples
2) Configure "build-and-deploy-mac.sh" with your target architecture:
  - Default: arm64 (Apple Silicon)
  - Alternative: x86_64 (Intel)
3) ./build-and-deploy-mac.sh

**4. Android**

**Requirements**

• NDK (r25b, macOS verified)
• CMake (3.19.2, macOS verified)
• adb (1.0.41, macOS verified)

**Usage**

1) cd ./examples
2) export ANDROID_NDK=/path/to/android-ndk  # Replace it with your NDK installation path
3) Configure "build-and-deploy-android.sh" with your build settings:
  - Architecture: arm64-v8a (default) or armeabi-v7a
  - Toolchain: aarch64-linux-android-clang (default) or custom NDK toolchain
4) ./build-and-deploy-android.sh

**5. iOS**

**Requirements**

Xcode (15.2, macOS verified) CMake (3.19.2, macOS verified)

**Usage**

1. Enter examples directory

cd ./examples

2. Creates Xcode project files for iOS build

./build-and-deploy-ios.sh

3. Follow the steps below to build and test on iOS device:

   3.1. Use Xcode to open .xcodeproj files: a) cd ./build-ios, b) open ./ten_vad_demo.xcodeproj

   3.2. In Xcode IDE, select ten_vad_demo target (should check: Edit Scheme → Run → Release), then select your iOS Device (not simulator).

   

   3.3. Drag ten_vad/lib/iOS/ten_vad.framework to "Frameworks, Libraries, and Embedded Content"

   • (in TARGETS → ten_vad_demo → ten_vad_demo → General, should set Embed to "Embed & Sign").

   • or add it directly in this way: "Frameworks, Libraries, and Embedded Content" → "+" → Add Other... → Add Files →...

   • Note: If this step is not completed, you may encounter the following runtime error: "dyld: Library not loaded: @rpath/ten_vad.framework/ten_vad".

     

   3.4. Configure iOS device Signature

   • in TARGETS → ten_vad_demo → Signing & Capabilities → Signing

     • Modify Bundle Identifier: modify "com.yourcompany" to yours;

     • Specify Provisioning Profile

   • In TARGETS → ten_vad_demo → Build Settings → Signing → Code Signing Identity:

     • Specify your Certification

   3.5. Build in Xcode and run demo on your device.

TEN Ecosystem

Project	Preview
🏚️ TEN Framework TEN is an open-source framework for real-time, multimodal conversational AI.
️🔂 TEN Turn Detection TEN is for full-duplex dialogue communication.
🔉 TEN VAD TEN VAD is a low-latency, lightweight and high-performance streaming voice activity detector (VAD).
🎙️ TEN Agent TEN Agent is a showcase of TEN Framewrok.
🎨 TMAN Designer TMAN Designer is low/no code option to make a voice agent with easy to use workflow UI.
📒 TEN Portal The official site of TEN framework, it has documentation and blog.

Ask Questions

Most questions can be answered by using DeepWiki, it is fast, intutive to use and supports multiple languages.

**Citations**

@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 = {https://github.com/TEN-framework/ten-vad.git},
  email = {[email protected]}
}

License

This project is licensed pursuant to the Apache 2.0 with additional conditions. Refer to the "LICENSE" file in the root directory for detailed information. Note that pitch_est.cc contains modified code derived from LPCNet, which is BSD-2-Clause and BSD-3-Clause licensed, refer to the NOTICES file in the root directory for detailed information.