README.md

---
license: mit
dataset_info:
  features:
  - name: version
    dtype: string
  - name: data
    list:
    - name: a
      dtype: int64
    - name: b
      dtype: float64
    - name: c
      dtype: string
    - name: d
      dtype: bool
  splits:
  - name: train
    num_bytes: 58
    num_examples: 1
  download_size: 2749
  dataset_size: 58
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- text-to-speech
language:
- en
---
# Dataset Card for Dataset Name

The dataset repository includes the filtered dataset `EmoV_DB_bea_sem`, the filelists with semantic embeddings, and the model checkpoints that used in our work "Llama-VITS: Enhancing TTS Synthesis with Semantic Awareness".  

## Dataset Details

- **Paper:** Llama-VITS: Enhancing TTS Synthesis with Semantic Awareness
- **Curated by:** Xincan Feng, Akifumi Yoshimoto
- **Funded by:** CyberAgent Inc
- **Repository:** https://github.com/xincanfeng/vitsGPT
- **Demo:** https://xincanfeng.github.io/Llama-VITS_demo/

## Dataset Creation

We fileterd `EmoV_DB_bea_sem` dataset from EmoV_DB (Adigwe et al., 2018), which is a database of emotional speech that contains data for male and female actors in English and French. EmoV_DB covers 5 emotion classes, amused, angry, disgusted, neutral, and sleepy. To factor out the effect of different speakers, we filtered the original EmoV_DB dataset into the speech of a specific female English speaker, bea. Then we use Llama2 to predict the emotion label of the transcript chosen from the above 5 emotion classes, and select the audio samples which has the same predicted emotion. 
The filtered dataset contains 22.8-min records for training. We named the filtered dataset `EmoV_DB_bea_sem` and investigated how the semantic embeddings from Llama2 behave in naturalness and expressiveness on it. Please refer to our paper for more information. 

## Citation

If our work is useful to you, please cite our paper: "Llama-VITS: Enhancing TTS Synthesis with Semantic Awareness".
```sh
@misc{feng2024llamavits,
      title={Llama-VITS: Enhancing TTS Synthesis with Semantic Awareness}, 
      author={Xincan Feng and Akifumi Yoshimoto},
      year={2024},
      eprint={2404.06714},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```