DLLM-TTS: Block Discrete Diffusion Language Model for Text-to-Speech Synthesis

Abstract:Current text-to-speech systems face a trade-off: autoregres- sive codec language models produce highly intelligible speech but require large-scale models and training data and decode tokens sequentially, while non-autoregressive approaches im- prove speed at the cost of linguistic accuracy. We present DLLM-TTS, a framework that formulates TTS as conditional block discrete diffusion over X-Codec2 neural audio codec to- kens. The model decomposes sequences into blocks and applies masked diffusion within each block while processing blocks se- quentially, learning both local acoustic coherence and global text-speech alignment. During inference, parallel token pre- diction within blocks enables efficient generation with a real- time factor (RTF) of 0.15. A 0.6B-parameter model trained on 20K hours achieves competitive performance on the Seed- TTS-eval benchmark, demonstrating that block discrete diffu- sion language models enable practical and data-efficient speech synthesis with parallel generation.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.00011 [cs.CL]

(or arXiv:2608.00011v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.00011 Focus to learn more arXiv-issued DOI via DataCite

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