Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers

Chengyi Wang; Sanyuan Chen; Yu Wu; Ziqiang Zhang; Long Zhou; Shujie Liu; Zhuo Chen; Yanqing Liu; Huaming Wang; Jinyu Li; Lei He; Sheng Zhao; Furu Wei

arXiv · 2301.02111

Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers

Chengyi Wang·Sanyuan Chen·Yu Wu·Ziqiang Zhang·Long Zhou·Shujie Liu·Zhuo Chen·Yanqing Liu·Huaming Wang·Jinyu Li·Lei He·Sheng Zhao·Furu Wei

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Abstract

We introduce a language modeling approach for text to speech synthesis (TTS). Specifically, we train a neural codec language model (called Vall-E) using discrete codes derived from an off-the-shelf neural audio codec model, and regard TTS as a conditional language modeling task rather than continuous signal regression as in previous work. During the pre-training stage, we scale up the TTS training data to 60K hours of English speech which is hundreds of times larger than existing systems. Vall-E emerges in-context learning capabilities and can be used to synthesize high-quality personalized speech with only a 3-second enrolled recording of an unseen speaker as an acoustic prompt. Experiment results show that Vall-E significantly outperforms the state-of-the-art zero-shot TTS system in terms of speech naturalness and speaker similarity. In addition, we find Vall-E could preserve the speaker's emotion and acoustic environment of the acoustic prompt in synthesis. See https://aka.ms/valle for demos of our work.

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Chengyi Wang, Sanyuan Chen, Yu Wu, Ziqiang Zhang, Long Zhou, Shujie Liu, Zhuo Chen, Yanqing Liu, Huaming Wang, Jinyu Li, Lei He, Sheng Zhao, Furu Wei. 2023-01-05. Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers. https://arxiv.org/abs/2301.02111

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