---
language:
- en
- multilingual
tags:
- text-to-speech
- speech-synthesis
- pytorch
- styletts2
- speaches
- neural-tts
- voice-cloning
pipeline_tag: text-to-speech
library_name: pytorch
license: mit
datasets:
- LibriTTS
metrics:
- naturalness
- similarity
widget:
- text: "Hello, this is a sample of StyleTTS2 speech synthesis."
  example_title: "English Sample"
- text: "StyleTTS2 can synthesize high-quality speech with style control."
  example_title: "Style Control Sample"
---

# StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training

StyleTTS 2 is a text-to-speech model that leverages style diffusion and adversarial training with large speech language models (SLMs) to achieve human-level text-to-speech synthesis. This model builds upon the original StyleTTS with significant improvements in naturalness and similarity.

## Model Description

- **Model Type**: Neural Text-to-Speech (TTS)
- **Language(s)**: English (primary), with support for 18+ languages
- **License**: MIT
- **Paper**: [StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training](https://arxiv.org/abs/2306.07691)
- **Sample Rate**: 24,000 Hz
- **Architecture**: Style diffusion with adversarial training

## Features

- **High-Quality Synthesis**: Achieves human-level naturalness in speech synthesis
- **Style Control**: Advanced style transfer and voice cloning capabilities
- **Multi-Language Support**: Primary English model with support for 18+ additional languages
- **Voice Cloning**: Can clone voices from reference audio samples
- **Diffusion-Based**: Uses diffusion models for high-quality audio generation

## Usage

This model is designed for text-to-speech synthesis with the following capabilities:

1. **Multi-Voice Synthesis**: Generate speech using preset voice styles
2. **Voice Cloning**: Clone voices from reference audio samples
3. **Style Control**: Fine-tune synthesis parameters for different styles
4. **Multi-Language**: Support for various languages with English-accented pronunciation

### Parameters

- `alpha` (0.0-1.0): Style blending factor (default: 0.3)
- `beta` (0.0-1.0): Style mixing factor (default: 0.7)
- `diffusion_steps` (3-20): Number of diffusion steps for quality (default: 5)
- `embedding_scale` (1.0-10.0): Embedding scale factor (default: 1.0)

## Training Data

- **Primary Dataset**: LibriTTS
- **Languages**: English (primary) + 18 additional languages
- **Training Approach**: Style diffusion with adversarial training using large speech language models

## Performance

StyleTTS 2 achieves human-level performance in:
- **Naturalness**: Comparable to human speech in listening tests
- **Similarity**: High fidelity voice cloning and style transfer
- **Quality**: Superior audio quality compared to previous TTS models

## Limitations

- **Compute Requirements**: Requires significant computational resources for inference
- **English-First**: Optimized for English, other languages may have accented pronunciation
- **Context Dependency**: Performance varies with input text length and complexity

## Citation

```bibtex
@article{li2024styletts2,
  title={StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models},
  author={Li, Yinghao Aaron and Han, Cong and Mesgarani, Nima},
  journal={arXiv preprint arXiv:2306.07691},
  year={2024}
}
```

## Links

- Paper: [https://arxiv.org/abs/2306.07691](https://arxiv.org/abs/2306.07691)
- Samples: [https://styletts2.github.io/](https://styletts2.github.io/)
- Code: [https://github.com/yl4579/StyleTTS2](https://github.com/yl4579/StyleTTS2)
- License: MIT License