README.md

---
dataset_info:
- config_name: role_playing
  features:
  - name: ID
    dtype: int64
  - name: text_0
    dtype: string
  - name: text_1
    dtype: string
  - name: audio_0
    dtype:
      audio:
        sampling_rate: 16000
  - name: audio_1
    dtype:
      audio:
        sampling_rate: 16000
  - name: source
    dtype: string
  - name: speaker1
    dtype: string
  - name: speaker2
    dtype: string
  splits:
  - name: test
    num_bytes: 182310504.0
    num_examples: 20
  download_size: 148908359
  dataset_size: 182310504.0
- config_name: voice_instruction_following
  features:
  - name: ID
    dtype: int64
  - name: text_1
    dtype: string
  - name: text_2
    dtype: string
  - name: audio_1
    dtype:
      audio:
        sampling_rate: 16000
  - name: audio_2
    dtype:
      audio:
        sampling_rate: 16000
  splits:
  - name: test
    num_bytes: 36665909.0
    num_examples: 20
  download_size: 35109899
  dataset_size: 36665909.0
configs:
- config_name: role_playing
  data_files:
  - split: test
    path: role_playing/test-*
- config_name: voice_instruction_following
  data_files:
  - split: test
    path: voice_instruction_following/test-*
---
# StyleSet

**WARNING**: This dataset contains some profane words.

**A spoken language benchmark for evaluating speaking-style-related speech generation**  
Released in our paper, [Audio-Aware Large Language Models as Judges for Speaking Styles](https://arxiv.org/abs/2506.05984)

This dataset is released by NTU Speech Lab under the MIT license.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/622326ae0129f2097d69a3e2/Q8Os1g5vfy22Y9myvSc7X.png)

---

## Tasks

1. **Voice Style Instruction Following**  
   - Reproduce a given sentence verbatim.  
   - Match specified prosodic styles (emotion, volume, pace, emphasis, pitch, non-verbal cues).

2. **Role Playing**  
   - Continue a two-turn dialogue prompt in character.  
   - Generate the next utterance with appropriate prosody and style.
   - The dataset is modified from IEMOCAP with the consent of the authors. Please refer to [IEMOCAP](https://sail.usc.edu/iemocap/) for details and the original data of IEMOCAP. We do not redistribute the data here.

---

## Evaluation

We use ALLM-as-a-judge for evaluation. Currently, we found that `gemini-2.5-pro-0506` reaches the best agreement with human evaluators.
The complete evaluation prompt and evaluation pipelines can be found in Table 3 to Table 5 in our paper.


## Citation

If you use StyleSet or find ALLM-as-a-judge useful, please cite our paper by
```
@misc{chiang2025audioawarelargelanguagemodels,
      title={Audio-Aware Large Language Models as Judges for Speaking Styles}, 
      author={Cheng-Han Chiang and Xiaofei Wang and Chung-Ching Lin and Kevin Lin and Linjie Li and Radu Kopetz and Yao Qian and Zhendong Wang and Zhengyuan Yang and Hung-yi Lee and Lijuan Wang},
      year={2025},
      eprint={2506.05984},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/2506.05984}, 
}
```