ESPnet: End-to-End Speech Processing Toolkit
Paper • 1804.00015 • Published
How to use espnet/Karthik_sinhala_asr_train_asr_transformer with ESPnet:
from espnet2.bin.asr_inference import Speech2Text
model = Speech2Text.from_pretrained(
"espnet/Karthik_sinhala_asr_train_asr_transformer"
)
speech, rate = soundfile.read("speech.wav")
text, *_ = model(speech)[0]espnet/Karthik_sinhala_asr_train_asr_transformer
This model was trained by Karthik using sinhala/asr1 recipe in espnet.
# coming soon
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson {Enrique Yalta Soplin} and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
or arXiv:
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Enrique Yalta Soplin and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}