AudioLLMs/earnings22_test
@article{del2022earnings, title={Earnings-22: A practical benchmark for accents in the wild}, author={Del Rio, Miguel and Ha, Peter and McNamara, Quinten and Miller, Corey and Chandra, Shipra}, journal={arXiv preprint arXiv:2203.15591}, year={2022} } @article{wang2024audiobench, title={AudioBench: A Universal Benchmark for Audio Large Language Models}, author={Wang, Bin and Zou, Xunlong and Lin, Geyu and Sun, Shuo and Liu, Zhuohan and Zhang, Wenyu and Liu, Zhengyuan and Aw, AiTi… See the full description on the dataset page: https://huggingface.co/datasets/AudioLLMs/earnings22_test.
0441
1---2dataset_info:3 features:4 - name: context5 dtype: audio6 - name: instruction7 dtype: string8 - name: answer9 dtype: string10 splits:11 - name: test12 num_bytes: 13816377699.013 num_examples: 12514 download_size: 1330210697115 dataset_size: 13816377699.016configs:17- config_name: default18 data_files:19 - split: test20 path: data/test-*21---22 23```24@article{del2022earnings,25 title={Earnings-22: A practical benchmark for accents in the wild},26 author={Del Rio, Miguel and Ha, Peter and McNamara, Quinten and Miller, Corey and Chandra, Shipra},27 journal={arXiv preprint arXiv:2203.15591},28 year={2022}29}30```31 32```33@article{wang2024audiobench,34 title={AudioBench: A Universal Benchmark for Audio Large Language Models},35 author={Wang, Bin and Zou, Xunlong and Lin, Geyu and Sun, Shuo and Liu, Zhuohan and Zhang, Wenyu and Liu, Zhengyuan and Aw, AiTi and Chen, Nancy F},36 journal={NAACL},37 year={2025}38}39```40