Update README.md

README.md

@@ -25,4 +25,38 @@ configs:
   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}
+}
+```