VCB-Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents

arXiv GitHub Hugging Face
## Introduction Voice Chat Bot Bench (VCB Bench) is a high-quality Chinese benchmark built entirely on real human speech. It evaluates large audio language models (LALMs) along three complementary dimensions:
(1) Instruction following: Text Instruction Following (TIF), Speech Instruction Following (SIF), English Text Instruction Following (TIF-En), English Speech Instruction Following (SIF-En) and Multi-turn Dialog (MTD);
(2) Knowledge: General Knowledge (GK), Mathematical Logic (ML), Discourse Comprehension (DC) and Story Continuation (SC).
(3) Robustness: Speaker Variations (SV), Environmental Variations (EV), and Content Variations (CV). ## Getting Started ### Installation: ```bash git clone https://github.com/Tencent/VCB-Bench.git cd VCB-Bench pip install -r requirements.txt ``` Note: To evaluate Qwen3-omni, please replace it with the environment it requires. ### Download Dataset: Download the dataset from [Hugging Face](https://huggingface.co/datasets/tencent/VCB-Bench) and place the 'vcb_bench' into 'data/downloaded_datasets'. ### Evaluation: This code is adapted from [Kimi-Audio-Evalkit](https://github.com/MoonshotAI/Kimi-Audio-Evalkit), where you can find more details about the evaluation commands. (1) Inference + Evaluation: ``` python run_audio.py --model {model_name} --data {data_name} ``` For example: ``` CUDA_VISIBLE_DEVICES=1 python run_audio.py --model Qwen2.5-Omni-7B --data general_knowledge ``` (2) Only Inference: ``` python run_audio.py --model {model_name} --data {data_name} --skip-eval ``` For example: ``` CUDA_VISIBLE_DEVICES=4,5,6,7 python run_audio.py --model StepAudio --data continuation_en creation_en empathy_en recommendation_en rewriting_en safety_en simulation_en emotional_control_en language_control_en non_verbal_vocalization_en pacing_control_en style_control_en volume_control_en --skip-eval ``` (3) Only Evaluation: ``` python run_audio.py --model {model_name} --data {data_name} --reeval ``` For example: ``` CUDA_VISIBLE_DEVICES=2 nohup python run_audio.py --model Mimo-Audio --data continuation creation empathy --reeval ``` (4) Inference + ASR + Evaluation: ``` python run_audio.py --model {model_name} --data {data_name} --wasr ``` For example: ``` CUDA_VISIBLE_DEVICES=3 python run_audio.py --model StepAudio2 --data rewriting safety simulation continuation_en --wasr ``` ### Format Result: ``` python sumup_eval.py --model {model_name} ``` ``` python sumup_eval.py --model {model_name} --export_excel --output_file my_results.xlsx ``` ## Supported Datasets and Models (1) Locate the dataset you need to evaluate from the Data Name column in the Datasets table, and populate the {data_name} parameter in the evaluation command accordingly.
(2) Each dataset in the SV, EV, and CV sections has a corresponding comparison dataset named "{data_name}_cmp", following the specified naming convention.
(3) Identify the model you intend to evaluate from the Model Name column in the Models table, and insert the appropriate {model_name} into the evaluation command. ### Datasets:
Data Type Data Name Detail
TIF continuation -
creation -
empathy -
recommendation -
rewriting -
safety -
simulation -
TIF-En continuation_en -
creation_en -
empathy_en -
recommendation_en -
rewriting_en -
safety_en -
simulation_en -
SIF emotional_control -
language_control -
non_verbal_vocalization -
pacing_control -
style_control -
volume_control -
SIF-En emotional_control_en -
language_control_en -
non_verbal_vocalization_en -
pacing_control_en -
style_control_en -
volume_control_en -
MTD progression -
backtracking -
transition -
GK general_knowledge mathematics, geography, politics, chemistry, biology, law, physics, history, medicine, economics, sports, culture
ML basic_math -
math -
logical_reasoning analysis, induction, analogy, logic
DC discourse_comprehension inference, induction, analysis
SV age child, elder
accent tianjin, beijing, dongbei, sichuan
volume down, up
speed -
EV non_vocal_noise echo, outdoors, far_field
vocal_noise TV_playback, background_chat, vocal_music, voice_announcement
unstable_signal -
CV casual_talk -
mispronunciation -
grammatical_error -
topic_shift -
code_switching -
### Models:
Model Type Model Name
Chat Model Qwen2-Audio-7B-Instruct
Qwen2.5-Omni-7B
Baichuan-Audio-Chat
GLM4-Voice
Kimi-Audio
Mimo-Audio
StepAudio
StepAudio2
GPT4O-Audio
Qwen3-Omni-Instruct
Pretrain Model Qwen2-Audio-7B
Baichuan-Audio
Kimi-Audio-Base
StepAudio2-Base
## Acknowledge We borrow some code from [Kimi-Audio-Evalkit](https://github.com/MoonshotAI/Kimi-Audio-Evalkit), [GLM-4-Voice](https://github.com/zai-org/GLM-4-Voice), [Baichuan-Audio](https://github.com/baichuan-inc/Baichuan-Audio), [Kimi-Audio](https://github.com/MoonshotAI/Kimi-Audio), [Mimo-Audio](https://github.com/XiaomiMiMo/MiMo-Audio), [Step-Audio2](https://github.com/stepfun-ai/Step-Audio2), and [StepAudio](https://github.com/stepfun-ai/Step-Audio). ## Citation ``` @misc{hu2025vcbbenchevaluationbenchmark, title={VCB Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents}, author={Jiliang Hu and Wenfu Wang and Zuchao Li and Chenxing Li and Yiyang Zhao and Hanzhao Li and Liqiang Zhang and Meng Yu and Dong Yu}, year={2025}, eprint={2510.11098}, archivePrefix={arXiv}, primaryClass={cs.SD}, url={https://arxiv.org/abs/2510.11098}, } ```