Error code: ConfigNamesError Exception: DataFilesNotFoundError Message: No (supported) data files found in xincan/Llama-VITS_data Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 73, in compute_config_names_response config_names = get_dataset_config_names( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1904, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1885, in dataset_module_factory return HubDatasetModuleFactoryWithoutScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1270, in get_module module_name, default_builder_kwargs = infer_module_for_data_files( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 597, in infer_module_for_data_files raise DataFilesNotFoundError("No (supported) data files found" + (f" in {path}" if path else "")) datasets.exceptions.DataFilesNotFoundError: No (supported) data files found in xincan/Llama-VITS_data

Dataset Card for Llama-VITS_data

The dataset repository contains data related with our work "Llama-VITS: Enhancing TTS Synthesis with Semantic Awareness", encapsulating:

Filtered dataset EmoV_DB_bea_sem
Filelists with semantic embeddings
Model checkpoints
Human evaluation templates

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), a database of emotional speech containing 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-minute 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".

@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}
}