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fairseq S2T: Fast Speech-to-Text Modeling with fairseq

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arxiv 2010.05171 v2 pith:NYGUT64W submitted 2020-10-11 cs.CL eess.AS

Changhan Wang , Yun Tang , Xutai Ma , Anne Wu , Sravya Popuri , Dmytro Okhonko , Juan Pino This is my paper · ORCID
classification cs.CLeess.AS
keywords fairseqmodelsspeech-to-textend-to-endexampleslearningmodelingspeech
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We introduce fairseq S2T, a fairseq extension for speech-to-text (S2T) modeling tasks such as end-to-end speech recognition and speech-to-text translation. It follows fairseq's careful design for scalability and extensibility. We provide end-to-end workflows from data pre-processing, model training to offline (online) inference. We implement state-of-the-art RNN-based, Transformer-based as well as Conformer-based models and open-source detailed training recipes. Fairseq's machine translation models and language models can be seamlessly integrated into S2T workflows for multi-task learning or transfer learning. Fairseq S2T documentation and examples are available at https://github.com/pytorch/fairseq/tree/master/examples/speech_to_text.

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Cited by 4 Pith papers

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