Datasets:

ltuncay
/

chime-asvspoof-2015

Error code:   DatasetGenerationError
Exception:    TypeError
Message:      int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1539, in _prepare_split_single
                  for key, record in generator:
                                     ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 131, in _generate_examples
                  for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
                                              ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
                  for filename, f in tar_iterator:
                                     ^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
                  for x in self.generator(*self.args):
                           ~~~~~~~~~~~~~~^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1419, in _iter_from_urlpath
                  with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
                       ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 976, in xopen
                  file_obj = fs.open(paths[0], mode)
                File "<string>", line 3, in open
                File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
                  return self._mock_call(*args, **kwargs)
                         ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
                  return self._execute_mock_call(*args, **kwargs)
                         ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
                  result = effect(*args, **kwargs)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 794, in wrapped
                  tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
                                                               ~~~^^^^^^^^
              TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
              
              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/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1401, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1579, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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End of preview.

CHIME · ASVspoof 2015

This is a source-preserving prepared candidate, not a scientifically admitted CHIME benchmark release. It repackages the selected upstream release into split-specific WebDataset TAR shards with native audio and per-example JSON. A Parquet index permits metadata filtering without downloading audio shards.

Source and selected scope

Upstream: ASVspoof 2015.
Pinned source release: Edinburgh DataShare DOI 10.7488/ds/298.
Selection: Official training and development partitions only, original speaker and spoof-system labels; select complete speaker groups round-robin by partition/sex/spoof class under both 10 GB budgets. Do not include evaluation recordings merely because they are in the same parent archive.
Original license/access conditions: CC-BY-4.0 Public HTTPS download without registration or signed agreement. Preserve source attribution and license restrictions.
Source notes: The three wav_data.*.tar.gz objects are an explicitly allowed 24.072 GB transient parent input. Follow upstream ExtractionInstructions using a streamed multipart reader. Materialize only selected train/dev files, not full evaluation data; no second assembled archive.

Original authors, licensors and recording contributors retain their rights. This preparation was assembled for public noncommercial research access; this repository does not grant broader rights than the original sources. Preserve per-recording attribution and notices. provenance/ contains original annotations/docs/licenses, source provenance and the redistribution audit. FSD50K/Clotho audio has per-recording terms; Clotho captions retain the complete custom Tampere experimental/noncommercial license. The CHIME code license does not apply to these data.

Representation and preservation

There are 263,151 audio assets, 26,845,023,094 stored-audio bytes and 26,833,444,450 decoded-PCM-equivalent bytes. Native sample rate, duration and channel count are retained. No loudness normalization, silence trimming, resampling or downmixing is applied.

Each sample retains its native audio codec (WAV/FLAC/MP3/OGG or another supported native container). Each payload is accompanied by <key>.json in an ordinary TAR. The JSON contains split, stored audio metadata, provenance, source groups and source targets. manifest.jsonl also records shard/member offsets and checksums. metadata.parquet provides audio and shard columns, common source/group/license columns, and exact arbitrary annotations in metadata_json (decode with json.loads). It does not duplicate audio payloads. Keeping heterogeneous annotations as JSON preserves empty objects and differing target schemas without Arrow type coercion. release.json hashes the audio shards and record manifest, and publication.json inventories all published files including attribution assets.

CHiME-3 background recordings interleave six synchronized native mono PCM16 channels into one six-channel PCM16 WAV per session, with exact decoded-sample checks and an explicit channel map. The excluded CHiME speech corpus is not included. Other audio payloads are copied byte for byte.

Source splits

test: 193,404 original audio assets.
train: 16,375 original audio assets.
validation: 53,372 original audio assets.

These are original source partitions or explicitly unspecified/background partitions. ESC-50 retains five official folds; no fixed benchmark train/test assignment is invented. VocalSet's split is the supplied technique-classification singer protocol, not a universal task protocol. Background noise is not converted into artificial word or silence examples. Linked views/channels share provenance groups.

Scientific limitations

Scientific admission remains pending. Native file integrity and packaging checks do not establish label correctness, perceptual deduplication, contamination freedom or benchmark validity. Existing upstream annotation errata and ambiguity are retained/documented. The source audit found declared Freesound ID overlaps within some official folds/partitions and across ESC-50, FSD50K and Clotho; FSD50K train/validation also share uploaders. Use the supplied source IDs, group metadata and provenance/source-audit.json when defining an evaluation protocol. Do not claim these candidate splits are universally leakage-free. Scores from this repository alone are not official CHIME scores.

Loading

Pin an immutable Hub commit revision in experiments. A generic Hugging Face WebDataset loader can read the shard configurations:

from datasets import load_dataset
samples = load_dataset("ltuncay/chime-asvspoof-2015", revision="<commit-sha>", streaming=True)

For local source-native decoding and checksum verification, use CHIME's iter_release(path, split=..., verify_hashes=True). Its audio arrays have shape [frames, channels]; the evaluated model owns its preprocessing. Download only selected shards/index files when appropriate; benchmark execution must not silently fetch data.

Provenance and citation

Cite the upstream dataset and recording authors as specified in the original documentation under provenance/. This preparation does not claim authorship of the source recordings or annotations. Exact upstream objects, checksums, release selection, preparation metadata and source URLs are recorded in release.json. Source Zenodo metadata/citations are retained when available. See the CHIME project implementation brief and scientific paper for the evolving evaluation protocol.

CHIME v1 benchmark usage

Do not train on CHIME evaluation data: 193404 records in the release split(s) test, listed one key per line in provenance/benchmark-v1/eval-keys.txt. Models submitted to CHIME must not see these recordings (or their source recordings) during pretraining or fine-tuning.

Task	Family	Metric	Folds	Train	Validation	Test (eval)
`asvspoof2015-spoof-detection`	classification	balanced_accuracy	1	train	validation	test

Exact per-record partition assignments (including records excluded from training because they share a source with evaluation data, and digitally silent assets) are in provenance/benchmark-v1/<task>.jsonl; the frozen task definitions are in provenance/benchmark-v1/usage.json. Splits never depend on the evaluation seed.

Downloads last month: 67

Total file size:

28.1 GB

json dict	__key__ string	__url__ string
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 82400, "member": "asvspoof-2015-00049d5d591c336cb1ad8e341fb450d9.wav", "pcm_bytes": 164800, "sample_rate": 16000, "sample_width": 2, "sha256": "9022eea1efb358c88040e6073a6645d0d1cea1059f4b346ed...	asvspoof-2015-00049d5d591c336cb1ad8e341fb450d9	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 38080, "member": "asvspoof-2015-00062655b8404c33c590d639a9327b30.wav", "pcm_bytes": 76160, "sample_rate": 16000, "sample_width": 2, "sha256": "e6631f029b6d1bf02f3f505086c3f02c691c190fb6ea5b13b4...	asvspoof-2015-00062655b8404c33c590d639a9327b30	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 90863, "member": "asvspoof-2015-000b54fdc1fdca136a2d56ddc004e4e0.wav", "pcm_bytes": 181726, "sample_rate": 16000, "sample_width": 2, "sha256": "d1a1b23188b0fbd01d859b443f3871c0342f70ad0383c2167...	asvspoof-2015-000b54fdc1fdca136a2d56ddc004e4e0	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 61920, "member": "asvspoof-2015-0017efd4fa15b9eb54dc0f0657ba7186.wav", "pcm_bytes": 123840, "sample_rate": 16000, "sample_width": 2, "sha256": "261d15da1bda2e2d377bc4b87d93dab4cd70cf7f6e1c0af65...	asvspoof-2015-0017efd4fa15b9eb54dc0f0657ba7186	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 76960, "member": "asvspoof-2015-00184677a8165981cb2c44a4d326bc72.wav", "pcm_bytes": 153920, "sample_rate": 16000, "sample_width": 2, "sha256": "25e6922c586ebfe4f1efe65027a2eb8b16fac033d2ecb29d5...	asvspoof-2015-00184677a8165981cb2c44a4d326bc72	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 47825, "member": "asvspoof-2015-0018e1b01616821df6c4f8ea31c7e7d6.wav", "pcm_bytes": 95650, "sample_rate": 16000, "sample_width": 2, "sha256": "cfd9ae03ab8b990e683a904d168184beffda81d1f64f651bba...	asvspoof-2015-0018e1b01616821df6c4f8ea31c7e7d6	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 67669, "member": "asvspoof-2015-001ad06dd125b5860889b8af7bd06690.wav", "pcm_bytes": 135338, "sample_rate": 16000, "sample_width": 2, "sha256": "e355ec2a19db75486f812351b94f22c85395b1638280a2024...	asvspoof-2015-001ad06dd125b5860889b8af7bd06690	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 31957, "member": "asvspoof-2015-001c0fceb84f223a7fe0fb5b7e6e79c9.wav", "pcm_bytes": 63914, "sample_rate": 16000, "sample_width": 2, "sha256": "573d16c15482d654e016040f90e085ddd6ebc0d493655d897b...	asvspoof-2015-001c0fceb84f223a7fe0fb5b7e6e79c9	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
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{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 51673, "member": "asvspoof-2015-00268253519609ad2f0373c842b8b8c6.wav", "pcm_bytes": 103346, "sample_rate": 16000, "sample_width": 2, "sha256": "032912ecf3229d2b6d66590f5342fbc348ad4c63689f3f3d6...	asvspoof-2015-00268253519609ad2f0373c842b8b8c6	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 24352, "member": "asvspoof-2015-0026efe5de23a3686c85ac4521c7ab61.wav", "pcm_bytes": 48704, "sample_rate": 16000, "sample_width": 2, "sha256": "123f9bd6d53b5311ea5f90562215c8b6ffccf20a16aacf1b3f...	asvspoof-2015-0026efe5de23a3686c85ac4521c7ab61	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 58560, "member": "asvspoof-2015-0028a2c43af582c31c84a6513f8695f8.wav", "pcm_bytes": 117120, "sample_rate": 16000, "sample_width": 2, "sha256": "7f6f6f525dc878e0eaa8bc9e436a3fcbd5f6ce021a7ee346b...	asvspoof-2015-0028a2c43af582c31c84a6513f8695f8	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 57940, "member": "asvspoof-2015-002c339830df063c17d14beff4a8698e.wav", "pcm_bytes": 115880, "sample_rate": 16000, "sample_width": 2, "sha256": "0f9561b168c37d5109a536d6a65bc011d4ff9a25123ff1519...	asvspoof-2015-002c339830df063c17d14beff4a8698e	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 51520, "member": "asvspoof-2015-0033f99551e95167313773bc81cb944b.wav", "pcm_bytes": 103040, "sample_rate": 16000, "sample_width": 2, "sha256": "238b13b6a12f7baa9afe974ff68645bca517dcc2e054f49a6...	asvspoof-2015-0033f99551e95167313773bc81cb944b	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 32265, "member": "asvspoof-2015-00347fa2d737b1a1419469f3deb3331c.wav", "pcm_bytes": 64530, "sample_rate": 16000, "sample_width": 2, "sha256": "5e982d4053841c9ce8cbcc5db8f72b6bc22d47c90dcc7e0dea...	asvspoof-2015-00347fa2d737b1a1419469f3deb3331c	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 59309, "member": "asvspoof-2015-003cef65a0401651c6ef4dfbd729c2ca.wav", "pcm_bytes": 118618, "sample_rate": 16000, "sample_width": 2, "sha256": "31d39f8aca8a7ad2645ce0d8737057abc5d3daba49b3f997b...	asvspoof-2015-003cef65a0401651c6ef4dfbd729c2ca	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 53273, "member": "asvspoof-2015-003f57c1b595646d50b4273d8f2ee84e.wav", "pcm_bytes": 106546, "sample_rate": 16000, "sample_width": 2, "sha256": "4fd371f779de2d05852591983c98a93ad8a9bf29ae001f60c...	asvspoof-2015-003f57c1b595646d50b4273d8f2ee84e	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 29321, "member": "asvspoof-2015-0041b71d7c78f7f82f1aadebe586085e.wav", "pcm_bytes": 58642, "sample_rate": 16000, "sample_width": 2, "sha256": "eb16fcb9b85b1dbecd374dc215158362c28b238c2a82eed221...	asvspoof-2015-0041b71d7c78f7f82f1aadebe586085e	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 35009, "member": "asvspoof-2015-0042f650968b806beba74799b4480a04.wav", "pcm_bytes": 70018, "sample_rate": 16000, "sample_width": 2, "sha256": "94dd62baf3e3df3e4e1064d7aab2ef746090b24d7d47000f80...	asvspoof-2015-0042f650968b806beba74799b4480a04	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 35239, "member": "asvspoof-2015-0047ef91231bf346de19a89cf9483427.wav", "pcm_bytes": 70478, "sample_rate": 16000, "sample_width": 2, "sha256": "5235d5e0bb547a48e91fa6b135d276c477f78513d9ea04c147...	asvspoof-2015-0047ef91231bf346de19a89cf9483427	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 29379, "member": "asvspoof-2015-004b28a1038f55198b0123697b32cc71.wav", "pcm_bytes": 58758, "sample_rate": 16000, "sample_width": 2, "sha256": "e74b7cd8f138f20f6899e4537615b6a9527d2e3351199634f9...	asvspoof-2015-004b28a1038f55198b0123697b32cc71	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 32242, "member": "asvspoof-2015-004fa8c004f1a63704f3099f57208d83.wav", "pcm_bytes": 64484, "sample_rate": 16000, "sample_width": 2, "sha256": "f604760917971c7f0d6f2689e570506cd69516d7f8a7a00cda...	asvspoof-2015-004fa8c004f1a63704f3099f57208d83	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 69440, "member": "asvspoof-2015-0051bb54e70909cac1389dd232a9150c.wav", "pcm_bytes": 138880, "sample_rate": 16000, "sample_width": 2, "sha256": "4f6dce3da9b8c289eae293bb8b87c5fdd764c5f75ed5fa27d...	asvspoof-2015-0051bb54e70909cac1389dd232a9150c	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 63611, "member": "asvspoof-2015-00567d2db6297e8aca1ddbce39a50cdd.wav", "pcm_bytes": 127222, "sample_rate": 16000, "sample_width": 2, "sha256": "b46129453d8c32280fb758a398241fcf133d92b4bc03467f6...	asvspoof-2015-00567d2db6297e8aca1ddbce39a50cdd	hf://datasets/ltuncay/chime-asvspoof-2015@be8b3fd74effdb9be17267e4499a322d80090fdb/data/train/shard-000021.tar
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