Hugging Face's logo

Datasets:
tencent
/
VCB-Bench

Modalities:
Audio
ArXiv:
Dataset card Data Studio Files
xet
Community
VCB-Bench
File size: 9,563 Bytes 
c0a083c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
 
<div align="center">
    <h1>
    VCB-Bench: An Evaluation Benchmark for Audio-Grounded Large Language Model Conversational Agents
    </h1>
    <a href="https://arxiv.org/abs/2510.11098"><img src="https://img.shields.io/badge/arXiv-2502.17810-B31B1B.svg" alt="arXiv"></a>
    <a href="https://github.com/Tencent/VCB-Bench"><img src="https://img.shields.io/badge/GitHub-Repo-181717.svg" alt="GitHub"></a>
    <a href="https://huggingface.co/datasets/tencent/VCB-Bench"><img src="https://img.shields.io/badge/Hugging%20Face-Data%20Page-yellow" alt="Hugging Face"></a>

</div>


## Introduction

<b>Voice Chat Bot Bench (VCB Bench)</b> is a high-quality Chinese benchmark built entirely on real human speech. It evaluates large audio language models (LALMs) along three complementary dimensions: 
<br>
(1) <b>Instruction following</b>: 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);<br>
(2) <b>Knowledge</b>: General Knowledge (GK), Mathematical Logic (ML), Discourse Comprehension (DC) and Story Continuation (SC).<br>
(3) <b>Robustness</b>: 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.<br>
(2) Each dataset in the SV, EV, and CV sections has a corresponding comparison dataset named "{data_name}_cmp", following the specified naming convention.<br>
(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:
<table>
  <thead>
    <tr>
      <th>Data Type</th>
      <th>Data Name</th>
      <th>Detail</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td class="category" rowspan="7">TIF</td>
      <td>continuation</td>
      <td>-</td>
    </tr>
    <tr>
      <td>creation</td>
      <td>-</td>
    </tr>
    <tr>
      <td>empathy</td>
      <td>-</td>
    </tr>
    <tr>
      <td>recommendation</td>
      <td>-</td>
    </tr>
    <tr>
      <td>rewriting</td>
      <td>-</td>
    </tr>
    <tr>
      <td>safety</td>
      <td>-</td>
    </tr>
    <tr>
      <td>simulation</td>
      <td>-</td>
    </tr>
    <tr>
      <td class="category" rowspan="7">TIF-En</td>
      <td>continuation_en</td>
      <td>-</td>
    </tr>
    <tr>
      <td>creation_en</td>
      <td>-</td>
    </tr>
    <tr>
      <td>empathy_en</td>
      <td>-</td>
    </tr>
    <tr>
      <td>recommendation_en</td>
      <td>-</td>
    </tr>
    <tr>
      <td>rewriting_en</td>
      <td>-</td>
    </tr>
    <tr>
      <td>safety_en</td>
      <td>-</td>
    </tr>
    <tr>
      <td>simulation_en</td>
      <td>-</td>
    </tr>
    <tr>
      <td class="category" rowspan="6">SIF</td>
      <td>emotional_control</td>
      <td>-</td>
    </tr>
    <tr>
      <td>language_control</td>
      <td>-</td>
    </tr>
    <tr>
      <td>non_verbal_vocalization</td>
      <td>-</td>
    </tr>
    <tr>
      <td>pacing_control</td>
      <td>-</td>
    </tr>
    <tr>
      <td>style_control</td>
      <td>-</td>
    </tr>
    <tr>
      <td>volume_control</td>
      <td>-</td>
    </tr>
    <tr>
      <td class="category" rowspan="6">SIF-En</td>
      <td>emotional_control_en</td>
      <td>-</td>
    </tr>
    <tr>
      <td>language_control_en</td>
      <td>-</td>
    </tr>
    <tr>
      <td>non_verbal_vocalization_en</td>
      <td>-</td>
    </tr>
    <tr>
      <td>pacing_control_en</td>
      <td>-</td>
    </tr>
    <tr>
      <td>style_control_en</td>
      <td>-</td>
    </tr>
    <tr>
      <td>volume_control_en</td>
      <td>-</td>
    </tr> 
    <tr>
      <td class="category" rowspan="3">MTD</td>
      <td>progression</td>
      <td>-</td>
    </tr>
    <tr>
      <td>backtracking</td>
      <td>-</td>
    </tr>
    <tr>
      <td>transition</td>
      <td>-</td>
    </tr> 
    <tr>
      <td class="category" rowspan="1">GK</td>
      <td>general_knowledge</td>
      <td>mathematics, geography, politics, chemistry, biology, law, physics, history, medicine, economics, sports, culture</td>
    </tr>
    <tr>
      <td class="category" rowspan="3">ML</td>
      <td>basic_math</td>
      <td>-</td>
    </tr>
    <tr>
      <td>math</td>
      <td>-</td>
    </tr>
    <tr>
      <td>logical_reasoning</td>
      <td>analysis, induction, analogy, logic</td>
    </tr>
    <tr>
      <td class="category" rowspan="1">DC</td>
      <td>discourse_comprehension</td>
      <td>inference, induction, analysis</td>
    </tr>
    <tr>
      <td class="category" rowspan="4">SV</td>
      <td>age</td>
      <td>child, elder</td>
    </tr>
    <tr>
      <td>accent</td>
      <td>tianjin, beijing, dongbei, sichuan</td>
    </tr>
    <tr>
      <td>volume</td>
      <td>down, up</td>
    </tr>
    <tr>
      <td>speed</td>
      <td>-</td>
    </tr>
    <tr>
      <td class="category" rowspan="3">EV</td>
      <td>non_vocal_noise</td>
      <td>echo, outdoors, far_field</td>
    </tr>
    <tr>
      <td>vocal_noise</td>
      <td>TV_playback, background_chat, vocal_music, voice_announcement</td>
    </tr>
    <tr>
      <td>unstable_signal</td>
      <td>-</td>
    </tr>
    <tr>
      <td class="category" rowspan="5">CV</td>
      <td>casual_talk</td>
      <td>-</td>
    </tr>
    <tr>
      <td>mispronunciation</td>
      <td>-</td>
    </tr>
    <tr>
      <td>grammatical_error</td>
      <td>-</td>
    </tr>
    <tr>
      <td>topic_shift</td>
      <td>-</td>
    </tr>
    <tr>
      <td>code_switching</td>
      <td>-</td>
    </tr>
  </tbody>
</table>

### Models:

<table>
  <thead>
    <tr>
      <th>Model Type</th>
      <th>Model Name</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td class="model-type" rowspan="10">Chat Model</td>
      <td>Qwen2-Audio-7B-Instruct</td>
    </tr>
    <tr>
      <td>Qwen2.5-Omni-7B</td>
    </tr>
    <tr>
      <td>Baichuan-Audio-Chat</td>
    </tr>
    <tr>
      <td>GLM4-Voice</td>
    </tr>
    <tr>
      <td>Kimi-Audio</td>
    </tr>
    <tr>
      <td>Mimo-Audio</td>
    </tr>
    <tr>
      <td>StepAudio</td>
    </tr>
    <tr>
      <td>StepAudio2</td>
    </tr>
    <tr>
      <td>GPT4O-Audio</td>
    </tr>
    <tr>
      <td>Qwen3-Omni-Instruct</td>
    </tr>
    <tr>
      <td class="model-type" rowspan="4">Pretrain Model</td>
      <td>Qwen2-Audio-7B</td>
    </tr>
    <tr>
      <td>Baichuan-Audio</td>
    </tr>
    <tr>
      <td>Kimi-Audio-Base</td>
    </tr>
    <tr>
      <td>StepAudio2-Base</td>
    </tr>
  </tbody>
</table>

## 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}, 
}
```