An end-to-end optimization framework jointly trains the speech tokenizer, LLM, FM model, and reward model for discrete-token TTS, reporting new SOTA WER of 0.78% and 1.56% on Seed-TTS-Eval with 0.6B LLM and 0.5B FM.
Scaling speech tokenizers with diffusion autoencoders
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End-to-End Training for Discrete Token LLM based TTS System
An end-to-end optimization framework jointly trains the speech tokenizer, LLM, FM model, and reward model for discrete-token TTS, reporting new SOTA WER of 0.78% and 1.56% on Seed-TTS-Eval with 0.6B LLM and 0.5B FM.