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

• URL: http://arxiv.org/abs/2301.02111v1
• Date: Thu, 5 Jan 2023 15:37:15 GMT
• Title: Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
• Authors: 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
• Abstract summary: We introduce a language modeling approach for text to speech synthesis (TTS) Specifically, we train a neural language model (called Vall-E) using discrete codes derived from an off-the-shelf neural audio model. Vall-E emerges in-context learning capabilities and can be used to synthesize high-quality personalized speech.
• Score: 92.55131711064935
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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