StyleTTS2 is an adversarial text-to-speech model that uses style diffusion and large speech language models to generate natural-sounding speech from text input. It combines adversarial training with large pre-trained speech models to improve speech quality and reduce artifacts, while employing a style diffusion process that extracts prosodic and timbral features from reference audio to guide speech generation.
The model supports multi-speaker voice synthesis by conditioning the diffusion process on speaker-specific embeddings derived from reference utterances, enabling voice cloning and adaptation to new speakers with minimal data. It offers style-controllable text-to-speech through diffusion-based sampling, and can generate speech directly from text without requiring a reference audio sample at inference time. The system uses a two-stage training pipeline that first trains on aligned data, then fine-tunes with unaligned data using style diffusion and adversarial loss.
Pre-trained model checkpoints are available for loading and running inference, with provided notebooks and importable scripts for generating speech from text. The model can be fine-tuned on a new speaker using a small amount of speech data, or trained from scratch on single or multiple voices using speaker labels and adjustable settings.