Project Overview
Silero VAD is an enterprise-grade, pre-trained voice activity detector. The tool is designed to identify active speech segments in audio files and is part of Silero's suite of speech-related models, which also includes speech-to-text models.
Getting Started
Silero VAD is designed to be easy to set up and use. To run Python examples on most systems, users will need Python 3.8 or higher, at least 1GB of RAM, and a CPU with modern instruction sets like AVX. The main dependencies are torch, torchaudio, and onnxruntime. Audio input and output are handled through torchaudio, which requires an audio backend such as FFmpeg or Sox.
Installation
The simplest way to install Silero VAD is through pip. Users can run pip install silero-vad to download and set up the tool. After installation, users can write Python scripts to load the VAD model, read audio files, and obtain timestamps for speech segments.
Example code snippets are provided for using Silero VAD with both pip and torch.hub, which facilitates its integration into existing Python environments.
Key Features
- High Accuracy: Silero VAD excels in detecting speech accurately across various audio scenarios.
- Fast Processing: The detector can process 30ms audio chunks in less than 1ms on a single CPU thread. Performance can be further enhanced using GPUs or batching.
- Lightweight: The model is compact, approximately two megabytes.
- Generalization: It performs well across different languages and audio qualities, having been trained on data from over 6000 languages.
- Flexible Sampling Rates: Supports 8000 Hz and 16000 Hz sampling rates, making it versatile for various applications.
- Highly Portable: Compatible with PyTorch and ONNX, it can run on multiple platforms where these technologies are supported.
- Free and Open: Licensed under the MIT license, it features an open-use policy without restrictions like telemetry or registration.
Typical Use Cases
Silero VAD is suited for a range of applications, including:
- Voice activity detection in IoT and mobile applications.
- Data cleaning and preparation involving speech detection.
- Automation in telephony and call centers, including voice bots.
- Implementing voice interfaces.
Community and Resources
The project is well-documented, and the community provides various examples and usage cases in multiple programming languages such as C++, Rust, Go, and Java. Users can leverage these examples for integrating Silero VAD into their projects.
Contact and Contributions
Silero invites users to engage with the project community by creating issues, participating in discussions, or joining their Telegram chat. The team is accessible via email for any inquiries or support.
For further information, users can explore Silero's extensive wiki, which is filled with resources, FAQs, and more detailed documentation.
Silero VAD represents a robust and efficient solution for voice activity detection, offering high performance and adaptability across various applications and environments.