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---
dataset_info:
features:
- name: audio
dtype: audio
- name: duration
dtype: float64
- name: bam
dtype: string
- name: french
dtype: string
- name: asr-ctc
dtype: string
- name: asr-tdt
dtype: string
- name: asr-mt-ctc
dtype: string
- name: asr-mt-tdt
dtype: string
- name: st-ctc
dtype: string
- name: st-tdt
dtype: string
- name: lau-tdt-k1
dtype: string
- name: lau-ctc-k1
dtype: string
- name: lau-tdt-k5
dtype: string
- name: lau-ctc-k5
dtype: string
- name: lau-tdt-k0.2
dtype: string
- name: lau-ctc-k0.2
dtype: string
- name: lau-tdt-mse-k1
dtype: string
- name: lau-ctc-mse-k1
dtype: string
- name: cluster_label
dtype: string
splits:
- name: test
num_bytes: 121749438
num_examples: 1218
download_size: 117816418
dataset_size: 121749438
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# LAU eval dataset
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).
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)
---
## Citation
```bibtex
@misc{diarra2026listenattendunderstandregularization,
title={Listen, Attend, Understand: a Regularization Technique for Stable E2E Speech Translation Training on High Variance labels},
author={Yacouba Diarra and Michael Leventhal},
year={2026},
eprint={2601.01121},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2601.01121},
}
```
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