between synthesized and training speech is big compared to that of the models trained on curated
speech [Wang et al., 2021], which suggests that previous models terribly underfit in-the-wild data.
This paper presents Voicebox, the most versatile text-conditioned speech generative model at scale.
Voicebox is trained on a text-guided speech infilling task, where the goal is to generate masked speech
given its surrounding audio and text transcript. This can be considered as a guided in-context learning
problem, where audio style is inferred from the audio context and textual content is specified through
transcript. Voicebox does not require any audio style labels (e.g., speaker, emotion, and noise), which
differentiates Voicebox from the majority of prior work where such labels are used extensively. Prior
work uses labels to make the mapping between input (text and audio style) and output (speech) more
Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale