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STYLETTS2
Human-level TTS via style diffusion and speech language models
MIT
ABOUT
Many TTS systems sound robotic or need a reference recording to pick a speaking style. StyleTTS 2 models style as a latent variable with diffusion, then trains against large speech language model discriminators so output can match human recordings on standard English benchmarks and adapt to new speakers without a matched studio voice.
INTEGRATION GUIDE
1. Generate highly natural English speech for narration, assistants, and product demos
2. Adapt a multi-speaker StyleTTS 2 checkpoint to a new voice with limited data
3. Research style-controlled TTS without requiring a reference waveform at inference
4. Benchmark human-level TTS against commercial and open neural vocoder stacks
TAGS
text-to-speechttsstyle-diffusionvoice-synthesiszero-shotspeech-synthesisopen-source