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--- |
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license: mit |
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dataset_info: |
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features: |
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- name: version |
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dtype: string |
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- name: data |
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list: |
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- name: a |
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dtype: int64 |
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- name: b |
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dtype: float64 |
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- name: c |
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dtype: string |
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- name: d |
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dtype: bool |
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splits: |
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- name: train |
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num_bytes: 58 |
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num_examples: 1 |
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download_size: 2749 |
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dataset_size: 58 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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task_categories: |
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- text-to-speech |
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language: |
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- en |
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--- |
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# Dataset Card for Llama-VITS_data |
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The dataset repository contains data related with our work "Llama-VITS: Enhancing TTS Synthesis with Semantic Awareness", encapsulating: |
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- Filtered dataset `EmoV_DB_bea_sem` |
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- Filelists with semantic embeddings |
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- Model checkpoints |
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- Human evaluation templates |
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## Dataset Details |
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- **Paper:** Llama-VITS: Enhancing TTS Synthesis with Semantic Awareness |
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- **Curated by:** Xincan Feng, Akifumi Yoshimoto |
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- **Funded by:** CyberAgent Inc |
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- **Repository:** https://github.com/xincanfeng/vitsGPT |
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- **Demo:** https://xincanfeng.github.io/Llama-VITS_demo/ |
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## Dataset Creation |
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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. |
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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. |
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## Citation |
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If our work is useful to you, please cite our paper: "Llama-VITS: Enhancing TTS Synthesis with Semantic Awareness". |
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```sh |
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@misc{feng2024llamavits, |
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title={Llama-VITS: Enhancing TTS Synthesis with Semantic Awareness}, |
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author={Xincan Feng and Akifumi Yoshimoto}, |
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year={2024}, |
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eprint={2404.06714}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |