Datasets:

ltuncay
/

chime-fleurs

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 · FLEURS

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: FLEURS.
Pinned source release: 70bb2e84b976b7e960aa89f1c648e09c59f894dd.
Selection: Seven fixed languages, one per FLEURS geographic group: en_us, uk_ua, ar_eg, sw_ke, hi_in, id_id, cmn_hans_cn. Include native train/dev/test archives and TSV metadata; stratify selection by language and official split under both 10 GB budgets. Preserve native 16 kHz audio and official speaker-disjoint split assignments.
Original license/access conditions: CC-BY-4.0 Public HTTPS download without registration or signed agreement. Preserve source attribution and license restrictions.
Source notes: All selected parent archives total about 14.94 GB. They are transient source inputs, not a claim that all expanded recordings fit the prepared budget. No converted Parquet or resampling is used. The final example selection and byte totals must be materialized in a manifest before publication.

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 13,415 audio assets, 9,999,919,586 stored-audio bytes and 9,999,141,512 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: 4,362 original audio assets.
train: 6,830 original audio assets.
validation: 2,223 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-fleurs", 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: 4362 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)
`fleurs-language`	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: 52

Total file size:

10.1 GB

json dict	__key__ string	__url__ string
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 384960, "member": "fleurs-0028a03e432898f83c4a68d57cda463d.wav", "pcm_bytes": 1539840, "sample_rate": 16000, "sample_width": 4, "sha256": "1f1a6d5c526b94383ad46fa54be69404b037d2518790740da5bc9a...	fleurs-0028a03e432898f83c4a68d57cda463d	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 41280, "member": "fleurs-00345a9e6f40bbf16e9f4f74d5e872f2.wav", "pcm_bytes": 165120, "sample_rate": 16000, "sample_width": 4, "sha256": "3fc5d253cd10a0737e1b736e834eabf158e819615119c858d864e91b...	fleurs-00345a9e6f40bbf16e9f4f74d5e872f2	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 184320, "member": "fleurs-003aaab61831104f5b2e87a5ab2fbbba.wav", "pcm_bytes": 737280, "sample_rate": 16000, "sample_width": 4, "sha256": "2fa9f20cff0b34bb85a238e1a3ce8ba64ffae8447dc6ad74a0be374...	fleurs-003aaab61831104f5b2e87a5ab2fbbba	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 190080, "member": "fleurs-003c1372ff312a51a95f6019d5b4a94b.wav", "pcm_bytes": 760320, "sample_rate": 16000, "sample_width": 4, "sha256": "771f5aec277c5959f10ed42ada40d2a14b84066033fb395ff45e93a...	fleurs-003c1372ff312a51a95f6019d5b4a94b	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 178560, "member": "fleurs-004f2b830691198aefb9938b378bfc3e.wav", "pcm_bytes": 714240, "sample_rate": 16000, "sample_width": 4, "sha256": "676d808f7314a8865b46b69e6bae7fba5fd247356291c903b8be317...	fleurs-004f2b830691198aefb9938b378bfc3e	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 271680, "member": "fleurs-005129c01551ed39a3d7769c364f7ba0.wav", "pcm_bytes": 1086720, "sample_rate": 16000, "sample_width": 4, "sha256": "38043c4d1e6926d50447d8ab8a170a43950984793dbb8abb8653bc...	fleurs-005129c01551ed39a3d7769c364f7ba0	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 177600, "member": "fleurs-0052fcea42d34182ffcadd8b8673c4f3.wav", "pcm_bytes": 710400, "sample_rate": 16000, "sample_width": 4, "sha256": "48726bdab98e380e1ec424a4400fccc08d000bd47be680de214aa4d...	fleurs-0052fcea42d34182ffcadd8b8673c4f3	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 122880, "member": "fleurs-0053e72a932ee1e94f8b3278ef1be22c.wav", "pcm_bytes": 491520, "sample_rate": 16000, "sample_width": 4, "sha256": "b9880c953ceb727fa59ccb0653e01c7d1309c6db3f6f124da14af24...	fleurs-0053e72a932ee1e94f8b3278ef1be22c	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
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{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 165120, "member": "fleurs-0082acd5562dd184462af4c629c49502.wav", "pcm_bytes": 660480, "sample_rate": 16000, "sample_width": 4, "sha256": "0257c970eb43b790c8bfab5817bcf9781b7cf7814466d694f01490a...	fleurs-0082acd5562dd184462af4c629c49502	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 179520, "member": "fleurs-00879fd3258f9fbcaebcf1cf952ae66b.wav", "pcm_bytes": 718080, "sample_rate": 16000, "sample_width": 4, "sha256": "ea0f0d4772e48555c2a657a4962f544fea68b843308a86a33027ea6...	fleurs-00879fd3258f9fbcaebcf1cf952ae66b	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 202560, "member": "fleurs-008b17f6e0fdcf16aaa611512d7b19a5.wav", "pcm_bytes": 810240, "sample_rate": 16000, "sample_width": 4, "sha256": "9836a9d658a507d633048b8e64703a6b3366f5ca21d0d0446b39b8b...	fleurs-008b17f6e0fdcf16aaa611512d7b19a5	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 349440, "member": "fleurs-0099dd84792cd93b0f2b1ed87753818d.wav", "pcm_bytes": 1397760, "sample_rate": 16000, "sample_width": 4, "sha256": "8066f24d7bfff37ca3389f45f29d6e29d34128fad30aac525f9743...	fleurs-0099dd84792cd93b0f2b1ed87753818d	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 190080, "member": "fleurs-00a15606ad122aeec2d3ad6a723c879b.wav", "pcm_bytes": 760320, "sample_rate": 16000, "sample_width": 4, "sha256": "3e151a9195610223f092508fd3439e127c11116190a13f21ae53d15...	fleurs-00a15606ad122aeec2d3ad6a723c879b	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 178560, "member": "fleurs-00a5fc7daa520552413d2107c985ccb2.wav", "pcm_bytes": 714240, "sample_rate": 16000, "sample_width": 4, "sha256": "88ee0ca1aac365b20a30d9845034d5f22efd6b7b40fde1f7ae9a141...	fleurs-00a5fc7daa520552413d2107c985ccb2	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 149760, "member": "fleurs-00b2397956550ddd309cbf7b89ab5e14.wav", "pcm_bytes": 599040, "sample_rate": 16000, "sample_width": 4, "sha256": "9fa1395e40445d6d4c220e19257319ef787aa248cddb6c31d19037f...	fleurs-00b2397956550ddd309cbf7b89ab5e14	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220800, "member": "fleurs-00b927f24ecf58213662c539b907e626.wav", "pcm_bytes": 883200, "sample_rate": 16000, "sample_width": 4, "sha256": "6525c3b961d5325526b486300639a968d4fb6518a32f6f24c7e8e14...	fleurs-00b927f24ecf58213662c539b907e626	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 112320, "member": "fleurs-00cf365c978a2aa207364c185b655867.wav", "pcm_bytes": 449280, "sample_rate": 16000, "sample_width": 4, "sha256": "9185cd80b3eae4e6bc6a270aa2e39ba76d1dc3db111df234e9dce45...	fleurs-00cf365c978a2aa207364c185b655867	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 118080, "member": "fleurs-00d906c41abe1477820ec62fb7dad5e4.wav", "pcm_bytes": 472320, "sample_rate": 16000, "sample_width": 4, "sha256": "a528a895eebe32ee249a06f94d818b0834da93f80bda017070f6cf4...	fleurs-00d906c41abe1477820ec62fb7dad5e4	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 113280, "member": "fleurs-00e0fb7ee53b64d08ab681390244d556.wav", "pcm_bytes": 453120, "sample_rate": 16000, "sample_width": 4, "sha256": "f560655f34e560ddf134556805b327daf2e2f7dd3d4a01e75e7a757...	fleurs-00e0fb7ee53b64d08ab681390244d556	hf://datasets/ltuncay/chime-fleurs@51357d37024623e7b3faa46182e967e56f48732a/data/train/shard-000004.tar
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