TY - GEN
T1 - FAIRSEQ S2
T2 - 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021
AU - Wang, Changhan
AU - Hsu, Wei Ning
AU - Adi, Yossi
AU - Polyak, Adam
AU - Lee, Ann
AU - Chen, Peng Jen
AU - Gu, Jiatao
AU - Pino, Juan
N1 - Publisher Copyright:
© 2021 Association for Computational Linguistics.
PY - 2021
Y1 - 2021
N2 - This paper presents FAIRSEQ S2, 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 S2 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 will be made available at https://github.com/pytorch/fairseq/tree/master/examples/speech_synthesis.
AB - This paper presents FAIRSEQ S2, 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 S2 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 will be made available at https://github.com/pytorch/fairseq/tree/master/examples/speech_synthesis.
UR - http://www.scopus.com/inward/record.url?scp=85126960412&partnerID=8YFLogxK
M3 - ???researchoutput.researchoutputtypes.contributiontobookanthology.conference???
AN - SCOPUS:85126960412
T3 - EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
SP - 143
EP - 152
BT - EMNLP 2021 - 2021 Conference on Empirical Methods in Natural Language Processing
PB - Association for Computational Linguistics (ACL)
Y2 - 7 November 2021 through 11 November 2021
ER -