Create README.md

README.md

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+---
+language:
+- en
+- multilingual
+tags:
+- text-to-speech
+- speech-synthesis
+- pytorch
+- styletts2
+- 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