metadata
configs:
- config_name: AISHELL1_dev
data_files:
- split: test
path: AISHELL1_dev/metadata.parquet
- config_name: Background_noise
data_files:
- split: test
path: Background_noise/metadata.parquet
- config_name: Bilingual_uedin
data_files:
- split: test
path: Bilingual_uedin/metadata.parquet
- config_name: CommonVoiceFR_dev
data_files:
- split: test
path: CommonVoiceFR_dev/metadata.parquet
- config_name: Libritts
data_files:
- split: test
path: Libritts/metadata.parquet
- config_name: Long_context
data_files:
- split: test
path: Long_context/metadata.parquet
- config_name: Multispeaker_libri
data_files:
- split: test
path: Multispeaker_libri/metadata.parquet
- config_name: VCTK
data_files:
- split: test
path: VCTK/metadata.parquet
- config_name: robotcall
data_files:
- split: test
path: robotcall/metadata.parquet
- config_name: vctk_text_robust
data_files:
- split: test
path: vctk_text_robust/metadata.parquet
RVCBench
RVCBench is a benchmark dataset for studying robustness in voice cloning and related audio generation pipelines.
Each subset is exposed as its own Hugging Face dataset configuration. Most subsets contain:
metadata.parquetaudios/
The canonical metadata stores one row per benchmark pair with columns such as:
speaker_idprompt_file_nameprompt_textprompt_languagetarget_file_nametarget_texttarget_languagepair_iddataset_namesplit
When available, training-oriented annotations are also preserved:
prompt_phonemes,prompt_tone,prompt_word2phtarget_phonemes,target_tone,target_word2ph
Some subsets include additional task-specific metadata, for example spam_type in robotcall.
Available Configs
AISHELL1_devBackground_noiseBilingual_uedinCommonVoiceFR_devLibrittsLong_contextMultispeaker_libriVCTKrobotcallvctk_text_robust
Notes
- The
compressiondirectory is intentionally excluded from this dataset release. - This repository is organized for direct browsing in the Hugging Face dataset viewer.
Paper
This dataset is introduced in: RVCBench: Benchmarking the Robustness of Voice Cloning Across Modern Audio Generation Models