Datasets:

ltuncay
/

chime-voxconverse

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 · VoxConverse

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: VoxConverse.
Pinned source release: audio 2021-07-21; annotations 24bf60be297701cd7e4ef18550c6d390c1b87365.
Selection: Official development and test native WAV archives with corrected v0.3 RTTM annotations from the pinned author commit. Preserve overlapping speech and all recording difficulties; group by original video and retain official split. Keep the full family if both native/PCM totals fit 10 GB; otherwise deterministic complete video groups under both ceilings.
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 landing page says audio is unavailable, but both exact author archive URLs returned public HTTPS 200 with ZIP content type and exact byte sizes on 2026-10-28. Dev central directory was read with strict bounded Range requests: 2,338,200,960 expanded bytes. Published author MD5s are retained; no password, registration or third-party mirror used.

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 448 audio assets, 7,353,582,640 stored-audio bytes and 7,353,547,696 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: 232 original audio assets.
validation: 216 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-voxconverse", 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: 232 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)
`voxconverse-activity`	frame_multilabel	segment_f1	1	train	validation	test
`voxconverse-count-qa`	qa	exact_match	1	—	validation	test
`voxconverse-speaker-change`	frame_multilabel	segment_f1	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: 89

Total file size:

7.36 GB

json dict	__key__ string	__url__ string
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 2855424, "member": "voxconverse-018ea62475197019c1315f638244a0e4.wav", "pcm_bytes": 5710848, "sample_rate": 16000, "sample_width": 2, "sha256": "d2400604a2ab78a2b9f9818a3db9ec917b341771c89c86e6...	voxconverse-018ea62475197019c1315f638244a0e4	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 5546880, "member": "voxconverse-05ef68bbc7b685e861c1e687b2dbf3ed.wav", "pcm_bytes": 11093760, "sample_rate": 16000, "sample_width": 2, "sha256": "e0d8ad29f45920a2f9fea0b50cc0c6735316adb6255116d...	voxconverse-05ef68bbc7b685e861c1e687b2dbf3ed	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 7505294, "member": "voxconverse-060ed8d77799d7fd8dbb69e3daa82880.wav", "pcm_bytes": 15010588, "sample_rate": 16000, "sample_width": 2, "sha256": "673754dc795c359aa097dddf4fdacee8bce3d9e35872241...	voxconverse-060ed8d77799d7fd8dbb69e3daa82880	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 3603456, "member": "voxconverse-0699ae94e8c1aab2b4f121d884abe0fd.wav", "pcm_bytes": 7206912, "sample_rate": 16000, "sample_width": 2, "sha256": "4b750648cda107baa6db183789646befd5c8e2f5b7077687...	voxconverse-0699ae94e8c1aab2b4f121d884abe0fd	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 447635, "member": "voxconverse-08d55234ad0880e02b3e6469308aba4e.wav", "pcm_bytes": 895270, "sample_rate": 16000, "sample_width": 2, "sha256": "f0fcf06daeb14c1e6c05165371f6f372687f80a79666402026...	voxconverse-08d55234ad0880e02b3e6469308aba4e	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 2395008, "member": "voxconverse-09793c334c424711d8f50c990037f2c1.wav", "pcm_bytes": 4790016, "sample_rate": 16000, "sample_width": 2, "sha256": "6d6edc8d909fb21883937c03db21dff3d16e7d8549c68aa0...	voxconverse-09793c334c424711d8f50c990037f2c1	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 7220245, "member": "voxconverse-0d2fc1148a5bbb61205415731c9af160.wav", "pcm_bytes": 14440490, "sample_rate": 16000, "sample_width": 2, "sha256": "fd3b7ba27685b0f98a41b16f42de144c1d0929ec560c6f5...	voxconverse-0d2fc1148a5bbb61205415731c9af160	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 2903808, "member": "voxconverse-0d5046caa09d68f222a60c235e719e35.wav", "pcm_bytes": 5807616, "sample_rate": 16000, "sample_width": 2, "sha256": "a20292170ccec787b902f74bc5ed2bd1d7235d89572b00fb...	voxconverse-0d5046caa09d68f222a60c235e719e35	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 1106338, "member": "voxconverse-0dd6d2e139a5fd6b27b7370b13b973ad.wav", "pcm_bytes": 2212676, "sample_rate": 16000, "sample_width": 2, "sha256": "32d3950fc245e8f3eb7d997349c9aca7d2ab9b6cfdcd0907...	voxconverse-0dd6d2e139a5fd6b27b7370b13b973ad	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 3521307, "member": "voxconverse-0e5b3c4175372b31072ce6fd918e9d65.wav", "pcm_bytes": 7042614, "sample_rate": 16000, "sample_width": 2, "sha256": "ff8e997f0884fd2e6e91f3579c28fca090b9204ea7684e72...	voxconverse-0e5b3c4175372b31072ce6fd918e9d65	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 6503808, "member": "voxconverse-11f485c0226801b438e798813010507b.wav", "pcm_bytes": 13007616, "sample_rate": 16000, "sample_width": 2, "sha256": "ab206582765bdf086d7ec06af93190781e18596e8ee8c78...	voxconverse-11f485c0226801b438e798813010507b	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 2418730, "member": "voxconverse-13dd40cdb340449f71aaca6a2ac55688.wav", "pcm_bytes": 4837460, "sample_rate": 16000, "sample_width": 2, "sha256": "b0ad071ae6a336ea19deb2eccf5a56acc7853b7d2cf1fc88...	voxconverse-13dd40cdb340449f71aaca6a2ac55688	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 2524416, "member": "voxconverse-157392882da48a554f0bf821b4b7c072.wav", "pcm_bytes": 5048832, "sample_rate": 16000, "sample_width": 2, "sha256": "5fd982a964a4714e323772e067c26fba8413d0d4321a1003...	voxconverse-157392882da48a554f0bf821b4b7c072	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 1391232, "member": "voxconverse-15d5aba0d9887979a2c4753046251c40.wav", "pcm_bytes": 2782464, "sample_rate": 16000, "sample_width": 2, "sha256": "6e920278aacfcf34074c56bc0106b9f7d79fa1b8877e593c...	voxconverse-15d5aba0d9887979a2c4753046251c40	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 2238720, "member": "voxconverse-170b727e54b61612125048e889616c23.wav", "pcm_bytes": 4477440, "sample_rate": 16000, "sample_width": 2, "sha256": "28420d717e9eac1b96a8d503a93428cebc52dadea61e7806...	voxconverse-170b727e54b61612125048e889616c23	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 3710592, "member": "voxconverse-1889d1661fa78bba0421df7283603ea2.wav", "pcm_bytes": 7421184, "sample_rate": 16000, "sample_width": 2, "sha256": "5e361542a3e4f6744cab895b333127ce13d539689a7aa380...	voxconverse-1889d1661fa78bba0421df7283603ea2	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 17557212, "member": "voxconverse-18dda14316c03045151906b25d02fff1.wav", "pcm_bytes": 35114424, "sample_rate": 16000, "sample_width": 2, "sha256": "af9ba2a9b142e03fbfa4b8226ef0f7a910960cd41901f4...	voxconverse-18dda14316c03045151906b25d02fff1	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 712203, "member": "voxconverse-1932cf09a97d7423d776020c239455cf.wav", "pcm_bytes": 1424406, "sample_rate": 16000, "sample_width": 2, "sha256": "39bd270761f867a17d77d4ae7e1c52936f8162dacf69f94b6...	voxconverse-1932cf09a97d7423d776020c239455cf	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 8004480, "member": "voxconverse-1b55ca8022d3de77e7809e896f987c9d.wav", "pcm_bytes": 16008960, "sample_rate": 16000, "sample_width": 2, "sha256": "d4b3be4550a0570e3245937a8e3d2da24efbf585928ed2a...	voxconverse-1b55ca8022d3de77e7809e896f987c9d	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 5193143, "member": "voxconverse-1d3c853f03ae926b9ed4d57634747c8d.wav", "pcm_bytes": 10386286, "sample_rate": 16000, "sample_width": 2, "sha256": "552f6b704b160b05aad14e7f13b5ed4c04f9bef45c27696...	voxconverse-1d3c853f03ae926b9ed4d57634747c8d	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 2499840, "member": "voxconverse-1da8facb00971a6bb8e2f75fd777dcd1.wav", "pcm_bytes": 4999680, "sample_rate": 16000, "sample_width": 2, "sha256": "5b525efe5708f5378c7e23d7bb163ac6f28c8e59297cd0dc...	voxconverse-1da8facb00971a6bb8e2f75fd777dcd1	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 7030656, "member": "voxconverse-1efd60930ea59aa68825ee2fa74fdd3c.wav", "pcm_bytes": 14061312, "sample_rate": 16000, "sample_width": 2, "sha256": "676a01ac67517a5b3d04b5fea2832f342c9ab7e25793fb5...	voxconverse-1efd60930ea59aa68825ee2fa74fdd3c	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 15621120, "member": "voxconverse-1fffd801872975fa877c9635a923aa39.wav", "pcm_bytes": 31242240, "sample_rate": 16000, "sample_width": 2, "sha256": "dabb063ecd09cce9e35ebe4e98fd4c71eff4e377adfd78...	voxconverse-1fffd801872975fa877c9635a923aa39	hf://datasets/ltuncay/chime-voxconverse@6b70530b2e686429733da0a9b9167f8a0b564939/data/validation/shard-000006.tar
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