TEN VAD – AI-powered real-time voice activity detection with low latency, lightweight, and high precision

What is TEN VAD？

TEN VAD is a high-performance, real-time Voice Activity Detection (VAD) system designed for enterprise-level applications. It accurately detects speech activity in audio streams with low latency, lightweight architecture, and high precision. Powered by advanced AI technologies such as deep learning models, TEN VAD swiftly distinguishes between speech and non-speech signals, significantly reducing response latency in dialogue systems.

TEN VAD supports multiple platforms (including Linux, Windows, macOS, Android, and iOS) and provides both Python and C interfaces, making it easy for developers to integrate. It is well-suited for use cases like intelligent assistants and customer service bots, helping build more efficient and smarter conversational systems.

Key Features of TEN VAD

• High-Accuracy Voice Detection: Precisely distinguishes speech from non-speech signals, delivering frame-level voice activity detection with high accuracy.

• Low Latency Processing: Detects voice activity quickly, significantly reducing end-to-end response time—ideal for real-time dialogue systems.

• Lightweight Design: Resource-efficient and low in computational complexity, enabling deployment on a wide range of hardware platforms.

• Multi-Platform Support: Compatible with Linux, Windows, macOS, Android, and iOS, offering broad platform support.

• Multi-Language Interfaces: Provides Python and C interfaces for use across different development environments.

• Flexible Configuration: Supports 16kHz audio input and configurable frame skipping, allowing adaptation to various application scenarios.

Technical Principles of TEN VAD

• Deep Learning Models: Utilizes deep neural networks (such as convolutional or recurrent neural networks) trained on large volumes of labeled audio data to learn distinguishing features of speech and non-speech signals.

• Feature Extraction: Extracts key features from audio input, such as Mel spectrograms and energy features, to effectively differentiate between speech and non-speech.

• Real-Time Processing: Incorporates efficient algorithms and optimized model architectures to enable real-time voice activity detection with minimal computational delay.

• Adaptive Thresholding: Dynamically adjusts detection thresholds to suit different application contexts and speech characteristics, enhancing accuracy and robustness.

• Optimized Architecture: Designed with an emphasis on computational efficiency and low memory usage, leveraging optimized algorithms and structures to ensure low-latency, lightweight performance.

Project Repositories

• GitHub Repository: https://github.com/ten-framework/ten-vad

• HuggingFace Model Hub: https://huggingface.co/TEN-framework/ten-vad

Application Scenarios for TEN VAD

• Smart Voice Assistants: Instantly detect user voice commands to enable real-time responses and improved user interaction.

• Online Customer Service Systems: Accurately recognize customer speech to help service bots respond more efficiently.

• Video Conferencing Software: Precisely detect speakers’ voices to enhance meeting transcription and note-taking.

• Speech Recognition Front-End: Filter out non-speech segments to improve the accuracy and efficiency of speech recognition systems.

• Interactive Voice Toys: Detect children’s voice commands in real time, boosting interactivity and engagement.