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ESPnet-ST: All-in-One Speech Translation Toolkit

About

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.

Hirofumi Inaguma, Shun Kiyono, Kevin Duh, Shigeki Karita, Nelson Enrique Yalta Soplin, Tomoki Hayashi, Shinji Watanabe• 2020

Related benchmarks

TaskDatasetResultRank
Speech-to-speech translationFisher Spanish-English (test)--
55
Speech TranslationMuST-C EN-DE (test-COMMON)
BLEU22.9
41
Simultaneous Speech TranslationCallHome Spanish-English Es-En (test)
BLEU19.4
18
Speech TranslationMuST-C EN-FR COMMON (test)
BLEU32.8
17
Speech-to-text TranslationMuST-C En-X (tst-COM)
BLEU (German)23.6
16
Speech Translationlibri-trans (test)
Detokenized BLEU (case-sensitive)17
14
Speech TranslationMuST-C EN-ES (tst-COMMON)
BLEU28
14
Speech TranslationMuST-C COMMON (tst)
WER (de)22.9
13
Speech TranslationMuST-C en-nl (tst-COMMON)
BLEU Score27.4
6
Speech RecognitionMuST-C COMMON (test)
WER12
5
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Other info

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