{"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/fairseq-s-2-a-scalable-and-integrable-speech","title":"fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit","arxiv_id":"2109.06912","date":"2021-09-14","proceeding":null,"authors":["Changhan Wang","Wei-Ning Hsu","Yossi Adi","Adam Polyak","Ann Lee","Peng-Jen Chen","Jiatao Gu","Juan Pino"],"abstract":"This paper presents fairseq S^2, a fairseq extension for speech synthesis. We implement a number of autoregressive (AR) and non-AR text-to-speech models, and their multi-speaker variants. To enable training speech synthesis models with less curated data, a number of preprocessing tools are built and their importance is shown empirically. To facilitate faster iteration of development and analysis, a suite of automatic metrics is included. Apart from the features added specifically for this extension, fairseq S^2 also benefits from the scalability offered by fairseq and can be easily integrated with other state-of-the-art systems provided in this framework. The code, documentation, and pre-trained models are available at https://github.com/pytorch/fairseq/tree/master/examples/speech_synthesis.","url_abs":"https://arxiv.org/abs/2109.06912v1","url_pdf":"https://arxiv.org/pdf/2109.06912v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"fairseq-s-2-a-scalable-and-integrable-speech","repo_url":"https://github.com/pytorch/fairseq","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"fairseq-s-2-a-scalable-and-integrable-speech","repo_url":"https://github.com/2023-MindSpore-4/Code-5/tree/main/IntegralNeuralNetworks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"fairseq-s-2-a-scalable-and-integrable-speech","repo_url":"https://github.com/Mind23-2/MindCode-101/tree/main/IntegralNeuralNetworks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"fairseq-s-2-a-scalable-and-integrable-speech","repo_url":"https://github.com/Mind23-2/MindCode-3/tree/main/IntegralNeuralNetworks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"speech-synthesis","task_name":"Speech Synthesis"},{"task_slug":"text-to-speech","task_name":"Text to Speech"},{"task_slug":"text-to-speech-1","task_name":"text-to-speech"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2109.06912","atlas_url":"https://app.syntology.ai/?focus=2109.06912","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}