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  # LAU eval dataset
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- This dataset was created while evaluating and comparing the models trained with Listen Attend Understand regularization and our [E2E-ST model](https://huggingface.co/RobotsMali/st-soloni-114m-tdt-ctc).
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  The audio is from jeli-asr test set; the regularization loss weight lambda in the paper is represented by the character "k" in the fields of this dataset, each field represent a model with a specific decoding strategy (CTC or TDT)
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- ---
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- ## Citation
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-
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- ```bibtex
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- @misc{diarra2025lau,
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- title={Listen, Attend, Understand: a Regularization Technique for stable E2E Speech Translation training on High Variance labels},
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- author={Diarra, Yacouba and Leventhal, Michael},
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- year={2025},
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- eprint={2512.XXXXX},
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- archivePrefix={arXiv},
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- primaryClass={cs.CL},
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- url={https://arxiv.org/abs/2512.XXXXX},
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- }
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-
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- ```
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-
 
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  # LAU eval dataset
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+ This dataset was created while evaluating and comparing the models trained with Listen Attend Understand regularization and our [E2E-ST model](https://huggingface.co/anonymousnowhere/st-soloni-114m-tdt-ctc).
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  The audio is from jeli-asr test set; the regularization loss weight lambda in the paper is represented by the character "k" in the fields of this dataset, each field represent a model with a specific decoding strategy (CTC or TDT)
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