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• W3118578889 abstract "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." @default.
• W3118578889 created "2021-01-18" @default.
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• W3118578889 date "2020-10-11" @default.
• W3118578889 modified "2023-09-25" @default.
• W3118578889 title "fairseq S2T: Fast Speech-to-Text Modeling with fairseq" @default.
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• W3118578889 doi "https://doi.org/10.48550/arxiv.2010.05171" @default.
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