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Update README.md

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1. Add OTC paper link
2. Add OTC bibtex
3. Add pre-trained model link
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# Introduction # Introduction
This is a weakly supervised ASR recipe for the LibriSpeech (clean 100 hours) dataset. We train a This is a weakly supervised ASR recipe for the LibriSpeech (clean 100 hours) dataset. We train a
conformer model using [Bypass Temporal Classification](https://arxiv.org/pdf/2306.01031.pdf) (BTC)/Omni-temporal Classification (OTC) with transcripts with synthetic errors. In this README, we will describe conformer model using [Bypass Temporal Classification](https://arxiv.org/pdf/2306.01031.pdf) (BTC)/[Omni-temporal Classification](https://arxiv.org/pdf/2309.15796.pdf) (OTC) with transcripts with synthetic errors. In this README, we will describe
the task and the BTC/OTC training process. the task and the BTC/OTC training process.
Note that OTC is an extension of BTC and supports all BTC functions. Therefore, in the following, we only describe OTC. Note that OTC is an extension of BTC and supports all BTC functions. Therefore, in the following, we only describe OTC.
@ -203,6 +203,9 @@ export CUDA_VISIBLE_DEVICES="0"
</tr> </tr>
</table> </table>
## Pre-trained Model
Pre-trained model: <https://huggingface.co/dgao/icefall-otc-librispeech-conformer-ctc2>
## Citations ## Citations
``` ```
@inproceedings{gao2023bypass, @inproceedings{gao2023bypass,
@ -211,4 +214,11 @@ export CUDA_VISIBLE_DEVICES="0"
booktitle={INTERSPEECH}, booktitle={INTERSPEECH},
year={2023} year={2023}
} }
@inproceedings{gao2023learning,
title={Learning from Flawed Data: Weakly Supervised Automatic Speech Recognition},
author={Gao, Dongji and Xu, Hainan and Raj, Desh and Garcia, Leibny Paola and Povey, Daniel and Khudanpur, Sanjeev},
booktitle={IEEE ASRU},
year={2023}
}
``` ```