Silero Models — Pre-Trained Speech AI for STT, TTS, and VAD

Introduction

Silero Models is a family of compact, production-ready speech models created by the Silero team. Unlike large cloud-based speech APIs, Silero models are designed to run locally on CPU with low latency, making them practical for edge devices, embedded systems, and privacy-sensitive applications where sending audio to external servers is not an option.

What Silero Models Does

• Detects speech segments in audio streams with the Silero VAD (Voice Activity Detection) model
• Converts speech to text in multiple languages including English, German, Spanish, and Ukrainian
• Generates natural-sounding speech from text using lightweight TTS models
• Runs entirely on CPU with sub-real-time latency for VAD and TTS tasks
• Integrates with PyTorch via torch.hub for one-line model loading

Architecture Overview

The models use optimized neural network architectures (primarily based on convolutional and recurrent layers) compiled to TorchScript for portable, dependency-free inference. The VAD model operates on raw audio chunks and outputs speech probability scores. STT models use CTC-based decoding, while TTS models use a feed-forward synthesis approach for fast generation.

Self-Hosting & Configuration

• Install PyTorch (CPU version is sufficient) and load models via torch.hub
• No additional dependencies beyond PyTorch and torchaudio for most use cases
• Configure audio sampling rate (16kHz recommended) and chunk size for streaming VAD
• Export models to ONNX format for deployment in non-Python environments
• Use the provided utility functions for audio reading, resampling, and timestamp extraction

Key Features

• Silero VAD is widely adopted as the standard open-source voice activity detector
• Models are under 10 MB each, suitable for mobile and IoT deployment
• ONNX export enables use in C++, JavaScript, and other runtime environments
• Streaming-capable VAD processes audio in real-time with minimal buffering
• Permissive licensing allows commercial use without per-request fees

Comparison with Similar Tools

• Whisper — OpenAI's STT model is larger and GPU-optimized; Silero runs well on CPU
• Coqui TTS — full-featured TTS toolkit; Silero TTS is smaller and faster for basic synthesis
• WebRTC VAD — rule-based VAD; Silero VAD uses neural networks for higher accuracy
• Picovoice — commercial embedded voice AI; Silero is open source and free
• Vosk — offline STT toolkit; Silero offers a broader model family (VAD + STT + TTS)

FAQ

Q: Does Silero VAD work with streaming audio? A: Yes. The VAD model processes audio in small chunks (30-100ms) and returns speech probabilities in real-time.

Q: What languages are supported for STT? A: English, German, Spanish, and several other European languages. Check the repository for the current language list.

Q: Can I fine-tune the models? A: The pre-trained models are distributed as compiled TorchScript bundles. Fine-tuning requires access to the original training pipeline, which is not fully open source.

Q: How accurate is the VAD compared to Whisper? A: Silero VAD is a voice activity detector (speech vs. non-speech), while Whisper performs full transcription. They serve different purposes and are often used together.

Sources

• https://github.com/snakers4/silero-models
• https://github.com/snakers4/silero-vad