TEN VAD: Low-Latency, High-Performance Voice Activity Detector
Summary
TEN VAD is a low-latency, high-performance, and lightweight Voice Activity Detector (VAD) designed for real-time enterprise use. It provides accurate frame-level speech activity detection, outperforming common alternatives like WebRTC VAD and Silero VAD. This system is crucial for enhancing conversational AI by reducing end-to-end latency and improving speech segment extraction.
Repository Info
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Introduction
TEN VAD is a cutting-edge Voice Activity Detector (VAD) developed by the TEN-framework. It stands out for its low-latency, high-performance, and lightweight design, making it ideal for real-time applications, especially in conversational AI systems. This VAD system delivers accurate frame-level speech activity detection, offering superior precision compared to industry-standard solutions like WebRTC VAD and Silero VAD, while also boasting lower computational complexity and reduced memory usage.
Installation
Getting started with TEN VAD is straightforward. Simply clone the repository to your local machine:
git clone https://github.com/TEN-framework/ten-vad.git
For detailed build instructions and platform-specific requirements, refer to the official GitHub repository.
Examples
TEN VAD offers extensive cross-platform compatibility and supports multiple programming languages. Developers can integrate TEN VAD using Python, JavaScript (JS), Java, Go (Golang), and C. The repository provides comprehensive examples and build scripts for various operating systems, including Linux, Windows, macOS, Android, and iOS. For detailed usage instructions and code snippets for each language and platform, please consult the 'Quick Start' section in the project's README.
Why Use
TEN VAD is engineered with several key advantages that make it a compelling choice for voice activity detection:
- High-Performance: Achieves superior precision-recall performance compared to WebRTC VAD and Silero VAD, validated on extensive test sets.
- Agent-Friendly: Rapidly detects speech-to-non-speech transitions, significantly reducing end-to-end latency in human-agent interaction systems.
- Lightweight: Demonstrates much lower computational complexity and smaller library size across various platforms, ensuring efficient resource utilization.
- Multiple Programming Languages and Platforms: Provides robust C compatibility across Linux, Windows, macOS, Android, and iOS, along with Python bindings, WASM for Web, Java, and Go support, enabling broad deployment flexibility.
Links
Explore TEN VAD and the broader TEN Ecosystem through these official links: