Datasets:

MattyB95
/

VoxCelebSpoof

Error code:   StreamingRowsError
Exception:    HfHubHTTPError
Message:      502 Server Error: Bad Gateway for url: https://huggingface.co/api/datasets/MattyB95/VoxCelebSpoof/paths-info/03154c7acc6e29b5ef01aaecfa09045f4f702259
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 322, in compute
                  compute_first_rows_from_parquet_response(
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 88, in compute_first_rows_from_parquet_response
                  rows_index = indexer.get_rows_index(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 444, in get_rows_index
                  return RowsIndex(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 347, in __init__
                  self.parquet_index = self._init_parquet_index(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 364, in _init_parquet_index
                  response = get_previous_step_or_raise(
                File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 566, in get_previous_step_or_raise
                  raise CachedArtifactError(
              libcommon.simple_cache.CachedArtifactError: The previous step failed.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py", line 286, in hf_raise_for_status
                  response.raise_for_status()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/requests/models.py", line 1021, in raise_for_status
                  raise HTTPError(http_error_msg, response=self)
              requests.exceptions.HTTPError: 502 Server Error: Bad Gateway for url: https://huggingface.co/api/datasets/MattyB95/VoxCelebSpoof/paths-info/03154c7acc6e29b5ef01aaecfa09045f4f702259
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 126, in get_rows_or_raise
                  return get_rows(
                File "/src/services/worker/src/worker/utils.py", line 64, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 87, in get_rows
                  ds = load_dataset(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 2567, in load_dataset
                  return builder_instance.as_streaming_dataset(split=split)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1382, in as_streaming_dataset
                  splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 119, in _split_generators
                  analyze(archives, downloaded_dirs, split_name)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 93, in analyze
                  for downloaded_dir_file in dl_manager.iter_files(downloaded_dir):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 869, in __iter__
                  yield from self.generator(*self.args, **self.kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 947, in _iter_from_urlpaths
                  if xisfile(urlpath, download_config=download_config):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 262, in xisfile
                  fs, *_ = fsspec.get_fs_token_paths(path, storage_options=storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 622, in get_fs_token_paths
                  fs = filesystem(protocol, **inkwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 290, in filesystem
                  return cls(**storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 79, in __call__
                  obj = super().__call__(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 56, in __init__
                  self.fo = fo.__enter__()  # the whole instance is a context
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 100, in __enter__
                  f = self.fs.open(self.path, mode=mode)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 1307, in open
                  f = self._open(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 223, in _open
                  return HfFileSystemFile(self, path, mode=mode, revision=revision, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 596, in __init__
                  super().__init__(fs, path, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 1663, in __init__
                  self.size = self.details["size"]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 1676, in details
                  self._details = self.fs.info(self.path)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_file_system.py", line 513, in info
                  paths_info = self._api.get_paths_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
                  return fn(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_api.py", line 2971, in get_paths_info
                  hf_raise_for_status(response)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py", line 333, in hf_raise_for_status
                  raise HfHubHTTPError(str(e), response=response) from e
              huggingface_hub.utils._errors.HfHubHTTPError: 502 Server Error: Bad Gateway for url: https://huggingface.co/api/datasets/MattyB95/VoxCelebSpoof/paths-info/03154c7acc6e29b5ef01aaecfa09045f4f702259

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VoxCelebSpoof

VoxCelebSpoof is a dataset related to detecting spoofing attacks on automatic speaker verification systems. This dataset is part of a broader effort to improve the security of voice biometric systems against various types of spoofing attacks, such as replay attacks, voice synthesis, and voice conversion.

Dataset Details

Dataset Description

The VoxCelebSpoof dataset includes a range of audio samples from different types of synthesis spoofs. The goal of the dataset is to develop systems that can accurately distinguish between genuine and spoofed audio samples.

Key features and objectives of VoxCelebSpoof include:

Data Diversity: The dataset is derived from VoxCeleb, a large-scale speaker identification dataset containing celebrity interviews. Due to this, the spoofing detection models trained on VoxCelebSpoof are exposed to various accents, languages, and acoustic environments.
Synthetic Varieties: The spoofs include a variety of synthetic (TTS) attacks, such as high-quality synthetic speech, using AI-based voice cloning, and challenging systems to recognise and defend against a range of synthetic vulnerabilities.
Benchmarking: VoxCelebSpoof can serve as a benchmark for comparing the performance of different spoofing detection systems under standardised conditions.
Research and Development: The dataset encourages the research community to innovate in anti-spoofing for voice biometric systems, promoting advancements in techniques like feature extraction, classification algorithms, and deep learning.
Curated by: Matthew Boakes
Funded by: Bill & Melinda Gates Foundation
Shared by: Alan Turing Institute
Language(s) (NLP): English
License: MIT

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🦜 Synthetic Voice Detection • 15 items • Updated Aug 15 • 1