SPEECH & AUDIO

NLP

Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale

June 16, 2023

Abstract

Large-scale generative models such as GPT and DALL-E have revolutionized natural language processing and computer vision research. These models not only generate high fidelity text or image outputs, but are also generalists which can solve tasks not explicitly taught. In contrast, speech generative models are still primitive in terms of scale and task generalization. In this paper, we present Voicebox, the most versatile text-guided generative model for speech at scale. Voicebox is a non-autoregressive flow-matching model trained to infill speech, given audio context and text, trained on over 50K hours of speech that are neither filtered nor enhanced. Similar to GPT, Voicebox can perform many different tasks through in-context learning, but is more flexible as it can also condition on future context. Voicebox can be used for mono or cross-lingual zero-shot text-to-speech synthesis, noise removal, content editing, style conversion, and diverse sample generation. In particular, Voicebox outperforms the state-of-the-art zero-shot TTS model VALL-E on both intelligibility (5.9% vs 1.9% word error rates) and audio similarity (0.580 vs 0.681) while being up to 20 times faster. See voicebox.metademolab.com for a demo of the model

Download the Paper

AUTHORS

Written by

Matt Le

Apoorv Vyas

Bowen Shi

Brian Karrer

Leda Sari

Rashel Moritz

Mary Williamson

Vimal Manohar

Yossef (Yossi) Adi

Jay Mahadeokar

Wei-Ning Hsu

Publisher

Meta Research

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