Datasets:
The dataset viewer is not available for this subset.
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/webdataset/webdataset.py", line 80, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 33, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 1380, in _iter_from_urlpath
yield from cls._iter_tar(f)
File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 1331, in _iter_tar
stream = tarfile.open(fileobj=f, mode="r|*")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/tarfile.py", line 1886, in open
t = cls(name, filemode, stream, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/tarfile.py", line 1762, in __init__
self.firstmember = self.next()
^^^^^^^^^^^
File "/usr/local/lib/python3.12/tarfile.py", line 2750, in next
raise ReadError(str(e)) from None
tarfile.ReadError: invalid header
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.
ChainBench-ADD
ChainBench-ADD is a delivery-aware benchmark for audio deepfake detection. It is designed to evaluate whether audio deepfake detectors remain reliable after realistic post-generation delivery effects such as compression, telephony degradation, replay-style simulation, and hybrid transformation chains.
Dataset Description
Dataset Summary
ChainBench-ADD focuses on post-generation delivery effects in audio deepfake detection. Rather than evaluating only clean bona fide and clean spoofed speech, it organizes samples into controlled delivery conditions so that robustness can be studied under more realistic downstream transformations.
The benchmark includes both bona fide and spoof speech, supports English and Mandarin Chinese, and provides metadata for structured evaluation across delivery conditions, operator chains, and protocol splits.
Dataset Composition
Labels
bona_fidespoof
Scale
- Total waveforms: 941,201
- Bona fide samples: 315,573
- Spoof samples: 625,628
- Speakers: 448
Splits
The benchmark uses speaker-disjoint train/dev/test splits.
- Train: 663,363 samples from 314 speakers
- Dev: 136,027 samples from 67 speakers
- Test: 141,811 samples from 67 speakers
Sources
ChainBench-ADD is built from upstream speech resources and speech-generation systems, including:
- Bona fide sources: Common Voice 24.0, AISHELL-3
- Spoof generation systems: Qwen3-TTS, CosyVoice3, Spark-TTS, F5-TTS, IndexTTS2, VoxCPM
Metadata
Metadata include release and benchmarking information such as:
- sample identifiers
- parent identifiers
- split labels
- ground-truth labels
- language
- speaker identifiers
- transcript
- provenance/source information
- generator family and generator model
- delivery family
- template identifiers
- variant identifiers
- ordered operator signatures
- structural annotations required to instantiate benchmark tasks
Intended Use
ChainBench-ADD is released for:
- research on audio deepfake detection
- robustness evaluation
- forensic analysis
- provenance analysis
- benchmarking
- academic reproduction and verification
This dataset is intended for defensive, scientific, and evaluative use.
Out-of-Scope / Prohibited Use
ChainBench-ADD must not be used to support or enable:
- impersonation or identity fraud
- harassment, social engineering, or deception
- unauthorized voice cloning
- misleading, deceptive, or fraudulent media generation
- biometric surveillance or other high-risk deployment
- training, adapting, or improving systems intended for deceptive speech generation
Access and Redistribution
By accessing, downloading, or using this repository, you agree to the terms in the LICENSE file.
Redistribution of this benchmark package, or any subset that includes third-party material, is permitted only to the extent allowed by all applicable upstream terms and must preserve:
- the
LICENSEfile - this dataset card
- all applicable upstream notices
- any required attribution and usage restrictions
Acknowledgements
ChainBench-ADD builds on upstream speech datasets and speech-generation systems. Users are responsible for consulting and complying with the original terms and attribution requirements of all upstream resources.
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