Popov et al., 2021, Huang et al., 2022, Tan et al., 2022, Casanova et al., 2021]. Previous works
consider highly curated datasets such as VCTK [Yamagishi et al., 2019], which contains only clean
audio recorded in studio from about 100 speakers with little speaking style and text variation. Such
models struggle to synthesize speech with rich variation in emotion, voice, background noise, acoustic
condition, and have not been tested on the abilities to generalize to tasks not explicitly trained on.
There had been a few attempts of using in-the-wild data such as CommonVoice [Ardila et al., 2019],
Librispeech [Panayotov et al., 2015], and LibriTTS [Zen et al., 2019] for training text-to-speech
(TTS) models. It led to huge quality degradation compared to training on curated datasets [Hsu et al.,
2019, Wang et al., 2021]. In particular, while in-the-wild data are generally of lower quality, the gap
Voicebox-Text-GuidedMultilingual UniversalSpeechGenerationatScale