StyleTTS 2 is a text-to-speech (TTS) framework that employs style diffusion and adversarial training to generate speech with human-level naturalness. Developed as an evolution of the original StyleTTS, the model treats speech style as a latent random variable sampled from a diffusion model. This approach enables the production of expressive and diverse prosody without requiring explicit style labels or reference audio for style generation.\n\nThe architecture utilizes a large pre-trained speech language model (SLM), such as WavLM, which serves as a discriminator during end-to-end adversarial training. By leveraging SLM representations and a novel differentiable duration modeling approach, StyleTTS 2 effectively captures the complex distribution of natural human speech. The system is also capable of zero-shot speaker adaptation, allowing it to clone voices using a short reference audio clip.\n\nIn comparative evaluations, StyleTTS 2 has demonstrated performance that matches or exceeds human-level naturalness on benchmarks such as LJSpeech and VCTK. It is designed to be efficient across both single-speaker and multi-speaker configurations, focusing on high-fidelity expressive voice synthesis and cross-domain adaptation.