{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/speak-read-and-prompt-high-fidelity-text-to","title":"Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervision","arxiv_id":"2302.03540","date":"2023-02-07","proceeding":null,"authors":["Eugene Kharitonov","Damien Vincent","Zalán Borsos","Raphaël Marinier","Sertan Girgin","Olivier Pietquin","Matt Sharifi","Marco Tagliasacchi","Neil Zeghidour"],"abstract":"We introduce SPEAR-TTS, a multi-speaker text-to-speech (TTS) system that can be trained with minimal supervision. By combining two types of discrete speech representations, we cast TTS as a composition of two sequence-to-sequence tasks: from text to high-level semantic tokens (akin to \"reading\") and from semantic tokens to low-level acoustic tokens (\"speaking\"). Decoupling these two tasks enables training of the \"speaking\" module using abundant audio-only data, and unlocks the highly efficient combination of pretraining and backtranslation to reduce the need for parallel data when training the \"reading\" component. To control the speaker identity, we adopt example prompting, which allows SPEAR-TTS to generalize to unseen speakers using only a short sample of 3 seconds, without any explicit speaker representation or speaker-id labels. 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