Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 246, in _split_generators
raise ValueError(
ValueError: `file_name`, `*_file_name`, `file_names` or `*_file_names` must be present as dictionary key in metadata files
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Multilingual Deepfake Speech Dataset (Indian Languages)
Overview
This dataset contains real and synthetic speech across four Indian languages:
- Telugu
- Tamil
- Malayalam
- Konkani
It is designed for deepfake speech detection and cross-language generalization research.
Composition
Real speech: OpenSLR datasets
Synthetic speech:
- MMS-TTS (neural TTS)
- SIGVC (signal-based transformations)
- RVC (voice conversion)
Total samples: ~15,000+ Average duration: ~5–7 seconds
Task
Binary classification:
- 0 → Real
- 1 → Fake
Dataset Structure
data/
mms_tts/
sigvc/
rvc/
metadata.csv
Metadata Format
file_path,label,language,method
Key Findings Enabled by This Dataset
- In-domain detection is near-perfect for neural models
- Cross-language generalization is significantly harder
- Malayalam shows consistent transfer difficulty
- Fake method generalization is weak
Limitations
- Dataset size is moderate compared to large benchmarks
- Fake methods limited to MMS-TTS, SIGVC, and RVC
- Cross-language imbalance exists across languages
Intended Use
- Deepfake detection research
- Cross-language generalization studies
- Model benchmarking
Disclaimer
This dataset is intended for research purposes only. The authors are not responsible for misuse.
License
This dataset is released under the CC-BY 4.0 License.
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