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

> Silero Models is a collection of pre-trained enterprise-grade speech models for speech-to-text, text-to-speech, and voice activity detection. The models run efficiently on CPU with PyTorch, requiring no GPU for inference, and support multiple languages out of the box.

## Install

Save in your project root:

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

## Quick Use
```python
import torch
# Load Voice Activity Detection model
model, utils = torch.hub.load(
    repo_or_dir='snakers4/silero-vad',
    model='silero_vad',
    force_reload=True
)
(get_speech_timestamps, _, read_audio, _, _) = utils
wav = read_audio('audio.wav', sampling_rate=16000)
timestamps = get_speech_timestamps(wav, model)
```

## 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

---
Source: https://tokrepo.com/en/workflows/asset-1c626bb0
Author: AI Open Source