Datasets:

ltuncay
/

chime-compa

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 · CompA

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: CompA.
Pinned source release: ICLR2024 author original CompA-order and CompA-attribute ZIPs, SHA256 snapshot2026-10-28.
Selection: Both original author-linked public ZIPs downloaded anonymously and validated; stable direct download URLs checked. Native gold subset has400 order rows and197 attribute rows, with complete actual-file joins.
Original license/access conditions: No separate dataset license notice found in original ZIPs or repository README; public acquisition is available. Preserve the author citation and original source metadata; this acquisition recipe does not confer redistribution rights. Public author-linked Google Drive downloads; no account, access application or agreement form. Standard public virus-scan download interstitial followed; direct confirm=t URLs verified.
Source notes: Source hashes are locally observed from authoritative anonymous HTTPS downloads, not claimed to be publisher-supplied checksums. Reuse staged bootstrap objects. Both direct URLs returned application/octet-stream with exact Content-Length.

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 1,226 audio assets, 571,828,314 stored-audio bytes and 571,771,878 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: 1,226 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-compa", 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: 1294 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)
`compa-caption-choice`	qa	exact_match	1	—	—	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: 56

Total file size:

580 MB

json dict	__key__ string	__url__ string
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 47184, "member": "compa-001decd827c1a9bf0801dca8f71d7990.wav", "pcm_bytes": 188736, "sample_rate": 16000, "sample_width": 4, "sha256": "4fae423cb7ee6783b47e4e9520c91b66666b154a375c881b7653733a7...	compa-001decd827c1a9bf0801dca8f71d7990	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 81616, "member": "compa-006cb22b2b3811914995cb6cca7de23e.wav", "pcm_bytes": 326464, "sample_rate": 16000, "sample_width": 4, "sha256": "7e6e215f1adc8fb4b3b84e099e35c92cce36b43912991b68ca62a901f...	compa-006cb22b2b3811914995cb6cca7de23e	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 160000, "member": "compa-00eedbd615d8d852c5eb8ccd687c017f.wav", "pcm_bytes": 640000, "sample_rate": 16000, "sample_width": 4, "sha256": "2d40e6db07a3d3ea3080c9c9a05e06a2d03f872db23d7b6805ece160...	compa-00eedbd615d8d852c5eb8ccd687c017f	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 160000, "member": "compa-0125117b3239e2a0dad39a75c45b5220.wav", "pcm_bytes": 640000, "sample_rate": 16000, "sample_width": 4, "sha256": "2aeaba965ad9dd12758403dc7ab39994ccd5ac49934cf5042951f839...	compa-0125117b3239e2a0dad39a75c45b5220	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 111904, "member": "compa-0174848f6cee76e3bce0aafa57c87efe.wav", "pcm_bytes": 447616, "sample_rate": 16000, "sample_width": 4, "sha256": "e494294f42976591d8d8ab54e33fcf387796b981f9a8a1804286cac6...	compa-0174848f6cee76e3bce0aafa57c87efe	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 20800, "member": "compa-0176ff1480d1efa38ab926cc99b48fcf.wav", "pcm_bytes": 83200, "sample_rate": 16000, "sample_width": 4, "sha256": "54e3b748266d8e5ed2be8752fabb50c1e8882226378193d8f8f29d7c65...	compa-0176ff1480d1efa38ab926cc99b48fcf	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 80992, "member": "compa-019b426af4bd04a6d541c61d7f761935.wav", "pcm_bytes": 323968, "sample_rate": 16000, "sample_width": 4, "sha256": "65d66034dcfe96e9c99eb3962f3d812f0cbf95fdf0bc636d1f4699a4c...	compa-019b426af4bd04a6d541c61d7f761935	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 70016, "member": "compa-01b56264a068906edb490c10428c35db.wav", "pcm_bytes": 140032, "sample_rate": 16000, "sample_width": 2, "sha256": "927c156b87463541e641e588e494e0af5214e226ad179768a49ea9d78...	compa-01b56264a068906edb490c10428c35db	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 33088, "member": "compa-01c50686797c6cf7418c6e36ad2ae079.wav", "pcm_bytes": 132352, "sample_rate": 16000, "sample_width": 4, "sha256": "06a0643d448b749a9148bc8a79431536246fbe2074b23df411bb8871d...	compa-01c50686797c6cf7418c6e36ad2ae079	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 160000, "member": "compa-01dafbffb0dc971f1a5737112f6ec7a6.wav", "pcm_bytes": 320000, "sample_rate": 16000, "sample_width": 2, "sha256": "48a96aa8e17733892c196055cece1c125ba2373a07f9d473df2ec1ba...	compa-01dafbffb0dc971f1a5737112f6ec7a6	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 95648, "member": "compa-029b41e8ac0bac42870a82cf815f3610.wav", "pcm_bytes": 191296, "sample_rate": 16000, "sample_width": 2, "sha256": "d976cb0a37a5f53a99d5d051875242a1fadc3cbe5c6a4c3ae75f840b5...	compa-029b41e8ac0bac42870a82cf815f3610	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 160000, "member": "compa-02bb9cefe139730265d95a46c6c6f720.wav", "pcm_bytes": 640000, "sample_rate": 16000, "sample_width": 4, "sha256": "ce5d32c0b8f760f584c67ee0280f3a629db5841a0f2799cbc8aaa717...	compa-02bb9cefe139730265d95a46c6c6f720	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 409502, "member": "compa-02e26180db14d10b020c856c35bd9764.wav", "pcm_bytes": 819004, "sample_rate": 32000, "sample_width": 2, "sha256": "84ab9a5886795a7ecf0e71640c6fb974e8c8604a4f23bbaaed4067d6...	compa-02e26180db14d10b020c856c35bd9764	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 26272, "member": "compa-02e2cd99371ad9402c8828bccd76e53e.wav", "pcm_bytes": 105088, "sample_rate": 16000, "sample_width": 4, "sha256": "5d88c2a39e594e57b1e2193304512edbedb369730d89699ea574f3f8d...	compa-02e2cd99371ad9402c8828bccd76e53e	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 160000, "member": "compa-02f55d8645e75947e041e508f20aec97.wav", "pcm_bytes": 640000, "sample_rate": 16000, "sample_width": 4, "sha256": "0c6edd5f01ec3f1c604364b4bec4dd001d84c2b15510fa6cd81f8683...	compa-02f55d8645e75947e041e508f20aec97	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 86416, "member": "compa-033a10805e1d297365412a32a0e41fe4.wav", "pcm_bytes": 345664, "sample_rate": 16000, "sample_width": 4, "sha256": "70abf5f61a946030eb3f6195bab0bdf24e45b502eff26e8b25360ba1c...	compa-033a10805e1d297365412a32a0e41fe4	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 160000, "member": "compa-036fe697d53dbf2922368b65e0e52675.wav", "pcm_bytes": 640000, "sample_rate": 16000, "sample_width": 4, "sha256": "80cb5391014350c3fe32cfbbd63eff663cd3af1e6ef29755e41ac575...	compa-036fe697d53dbf2922368b65e0e52675	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 162846, "member": "compa-03b39b828cf9f182726cadec6dea2764.wav", "pcm_bytes": 325692, "sample_rate": 32000, "sample_width": 2, "sha256": "facd9648d431478013d12d9c2b6daf2eaa178dc2b6684f3b04cb276e...	compa-03b39b828cf9f182726cadec6dea2764	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 75344, "member": "compa-03bbc925de105ae7e05852b317d758d3.wav", "pcm_bytes": 301376, "sample_rate": 16000, "sample_width": 4, "sha256": "2a41c233efe1ea2ad0d8c3766d4544b3cb792656462c9c6de92a79a66...	compa-03bbc925de105ae7e05852b317d758d3	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 320000, "member": "compa-03bdab4f9902190605c5f26955eb4ea1.wav", "pcm_bytes": 640000, "sample_rate": 32000, "sample_width": 2, "sha256": "f5e8348c1e0f19ce38a4c5e0d92514b6b0494fae4ec7b4138ba60c2f...	compa-03bdab4f9902190605c5f26955eb4ea1	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 160000, "member": "compa-03c4932e3a05185edeb21c2c94d0e85d.wav", "pcm_bytes": 640000, "sample_rate": 16000, "sample_width": 4, "sha256": "4447b9fc691528c860dd0bb75b1084288985b7db8d145e5e4232dad4...	compa-03c4932e3a05185edeb21c2c94d0e85d	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 87184, "member": "compa-0432ac58e490a35a9247dab4d35f43bb.wav", "pcm_bytes": 348736, "sample_rate": 16000, "sample_width": 4, "sha256": "e47ff896111afdcd6192c54569b07b2bce59f032421f0c4276cf09647...	compa-0432ac58e490a35a9247dab4d35f43bb	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 43712, "member": "compa-043a42b26dc5b69228c5b7e8a1677925.wav", "pcm_bytes": 174848, "sample_rate": 16000, "sample_width": 4, "sha256": "e484acf0353a8352a1bc53f330d5d96c2202c193301be7a76f38e3a4d...	compa-043a42b26dc5b69228c5b7e8a1677925	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 146768, "member": "compa-04694e1710cf866a6e9d97ad9e8c9214.wav", "pcm_bytes": 293536, "sample_rate": 16000, "sample_width": 2, "sha256": "9b5630a88eef4f6460316b48da1d2e6363a429c01358ba0756b4e508...	compa-04694e1710cf866a6e9d97ad9e8c9214	hf://datasets/ltuncay/chime-compa@88dc95acc904aa708e6b3380f1f21d2a9c9ee8cb/data/test/shard-000000.tar
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