fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit

Changhan Wang; Wei-Ning Hsu; Yossi Adi; Adam Polyak; Ann Lee; Peng-Jen Chen; Jiatao Gu; Juan Pino

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arXiv · 2109.06912

fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit

Changhan Wang·Wei-Ning Hsu·Yossi Adi·Adam Polyak·Ann Lee·Peng-Jen Chen·Jiatao Gu·Juan Pino

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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.

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BibTeX RIS

Changhan Wang, Wei-Ning Hsu, Yossi Adi, Adam Polyak, Ann Lee, Peng-Jen Chen, Jiatao Gu, Juan Pino. 2021-09-14. fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit. https://arxiv.org/abs/2109.06912

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