Nelson Yalta
2020
ESPnet-ST: All-in-One Speech Translation Toolkit
Hirofumi Inaguma
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Shun Kiyono
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Kevin Duh
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Shigeki Karita
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Nelson Yalta
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Tomoki Hayashi
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Shinji Watanabe
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-to-end speech processing toolkit, ESPnet, which integrates or newly implements automatic speech recognition, machine translation, and text-to-speech functions for speech translation. We provide all-in-one recipes including data pre-processing, feature extraction, training, and decoding pipelines for a wide range of benchmark datasets. Our reproducible results can match or even outperform the current state-of-the-art performances; these pre-trained models are downloadable. The toolkit is publicly available at https://github.com/espnet/espnet.
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Co-authors
- Hirofumi Inaguma 1
- Shun Kiyono 1
- Kevin Duh 1
- Shigeki Karita 1
- Tomoki Hayashi 1
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