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

• URL: http://arxiv.org/abs/2109.06912v1
• Date: Tue, 14 Sep 2021 18:20:28 GMT
• Title: fairseq S^2: A Scalable and Integrable Speech Synthesis Toolkit
• Authors: Changhan Wang, Wei-Ning Hsu, Yossi Adi, Adam Polyak, Ann Lee, Peng-Jen Chen, Jiatao Gu, Juan Pino
• Abstract summary: fairseq S2 is 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.
• Score: 60.74922995613379
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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