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VALL-E: Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers [research paper abstract]

Posted By: NaturalWisdom
Date: Saturday, 14-Jan-2023 23:20:03
www.rumormill.news/215313

In Response To: LISTENERS and VIEWERS BEWARE: MICROSOFT AI Can IMITATE (Fake) ANY VOICE Based on ONLY 3 SECONDS of ACTUAL RECORDING [2 min tweeted video] (NaturalWisdom)

I've tested text-to-speech synthesizer voices in the past and this new development is downright spooky and ominous.
-NW

VALL-E
Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
https://valle-demo.github.io/

[Paper]

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

Microsoft

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.

This page is for research demonstration purposes only.

SOURCE




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