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- W3201257124 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." @default.
- W3201257124 created "2021-09-27" @default.
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- W3201257124 date "2021-09-14" @default.
- W3201257124 modified "2023-09-30" @default.
- W3201257124 title "fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit" @default.
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- W3201257124 doi "https://doi.org/10.48550/arxiv.2109.06912" @default.
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