⚡ A repository for evaluating AudioLLMs in various tasks 🚀 ⚡
⚡ AudioBench: A Universal Benchmark for Audio Large Language Models 🚀 ⚡
🌟 Come to View Our Live Leaderboard on Huggingface Space 🌟
🏠 AudioBench Leaderboard | 🤗 Huggingface Datasets | 🤗 AudioLLM Paper Collection
werwerwerwerwerwerwerwerwerwerbleubleubleubleubleubleullama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgeaccaccaccaccllama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, meteor, gpt4o_judgellama3_70b_judge, meteor, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgewerwerwerwerwerwerllama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgewerwerllama3_70b_judge, string_match, gpt4o_judgewerwerwerwerHow to evaluate with the supported datasets? That's as simple as it can be. Replace the DATASET and METRIC name.
DATASET=librispeech_test_clean
METRIC=wer
Two simple steps:
As long as the model can do inference, you can load them and inference to get the responses. To evaluate on new models, please refer to adding_new_model.
Installation with pip:
pip install -r requirements.txt
For model-as-judge evaluation, we serve the judgement model as a service via vllm on port 5000.
The example is hosting a Llama-3-70B-Instruct model and running the cascade Whisper + Llama-3 model.
# Step 1:
# Server the judgement model using VLLM framework (my example is using int4 quantized version)
# This requires with 1 * 80GB GPU
bash vllm_model_judge_llama_3_70b.sh
# Step 2:
# We perform model inference and obtain the evaluation results with the second GPU
GPU=2
BATCH_SIZE=1
OVERWRITE=True
NUMBER_OF_SAMPLES=-1 # indicate all test samples if number_of_samples=-1
MODEL_NAME=Qwen2-Audio-7B-Instruct
DATASET=cn_college_listen_mcq_test
METRICS=llama3_70b_judge
bash eval.sh $DATASET $MODEL_NAME $GPU $BATCH_SIZE $OVERWRITE $METRICS $NUMBER_OF_SAMPLES
If you find our work useful, please consider citing our paper!
@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 and Chen, Nancy F},
journal={NAACL},
year={2025}
}
Email: bwang28c@gmail.com
Python
97.8%
Shell
2.2%
⚡ A repository for evaluating AudioLLMs in various tasks 🚀 ⚡
⚡ AudioBench: A Universal Benchmark for Audio Large Language Models 🚀 ⚡
🌟 Come to View Our Live Leaderboard on Huggingface Space 🌟
🏠 AudioBench Leaderboard | 🤗 Huggingface Datasets | 🤗 AudioLLM Paper Collection
werwerwerwerwerwerwerwerwerwerbleubleubleubleubleubleullama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgeaccaccaccaccllama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, meteor, gpt4o_judgellama3_70b_judge, meteor, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgewerwerwerwerwerwerllama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgellama3_70b_judge, gpt4o_judgewerwerllama3_70b_judge, string_match, gpt4o_judgewerwerwerwerHow to evaluate with the supported datasets? That's as simple as it can be. Replace the DATASET and METRIC name.
DATASET=librispeech_test_clean
METRIC=wer
Two simple steps:
As long as the model can do inference, you can load them and inference to get the responses. To evaluate on new models, please refer to adding_new_model.
Installation with pip:
pip install -r requirements.txt
For model-as-judge evaluation, we serve the judgement model as a service via vllm on port 5000.
The example is hosting a Llama-3-70B-Instruct model and running the cascade Whisper + Llama-3 model.
# Step 1:
# Server the judgement model using VLLM framework (my example is using int4 quantized version)
# This requires with 1 * 80GB GPU
bash vllm_model_judge_llama_3_70b.sh
# Step 2:
# We perform model inference and obtain the evaluation results with the second GPU
GPU=2
BATCH_SIZE=1
OVERWRITE=True
NUMBER_OF_SAMPLES=-1 # indicate all test samples if number_of_samples=-1
MODEL_NAME=Qwen2-Audio-7B-Instruct
DATASET=cn_college_listen_mcq_test
METRICS=llama3_70b_judge
bash eval.sh $DATASET $MODEL_NAME $GPU $BATCH_SIZE $OVERWRITE $METRICS $NUMBER_OF_SAMPLES
If you find our work useful, please consider citing our paper!
@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 and Chen, Nancy F},
journal={NAACL},
year={2025}
}
Email: bwang28c@gmail.com
Python
97.8%
Shell
2.2%