Datasets:

ltuncay
/

chime-voxlingua33

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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CHIME · VoxLingua33

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: VoxLingua33.
Pinned source release: 8dfcfa5b4aa77ac630c566a997c1af095088a36b.
Selection: The complete official VoxLingua107 development partition: 1,609 native 16 kHz WAV segments spanning exactly its 33 manually validated languages. This is explicitly the dev-only VoxLingua33 definition; no 107-language training parent is downloaded and no evaluation numbers are described as full-parent results. Preserve language labels and source-video grouping.
Original license/access conditions: CC-BY-4.0 Public HTTPS download without registration or signed agreement. Preserve source attribution and license restrictions.
Source notes: Author organization publishes native WAV+JSON WebDataset. The uncompressed dev TAR bounds total embedded native WAV and PCM bytes; enumerate its actual 33 language codes and sample IDs in the post-extraction selection manifest. The author states each dev language label was validated by at least two volunteers.

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 34,609 audio assets, 10,906,782,914 stored-audio bytes and 10,905,260,118 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: 1,609 original audio assets.
train: 33,000 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-voxlingua33", 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: 1609 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)
`voxlingua33-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: 1,103

Total file size:

11 GB

json dict	__key__ string	__url__ string
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 193920, "member": "voxlingua33-00021edf9e72f1126c174601273e5cbe.wav", "pcm_bytes": 387840, "sample_rate": 16000, "sample_width": 2, "sha256": "2f9284f9c62c3c613537b53af213a5879ba1faf7d997bf9b81...	voxlingua33-00021edf9e72f1126c174601273e5cbe	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 62080, "member": "voxlingua33-0003d1859883ec4b6b4f7ddb0a0e8a4c.wav", "pcm_bytes": 124160, "sample_rate": 16000, "sample_width": 2, "sha256": "17cd61846f5d4df98342a67a1d78a24a983c4105b31fe920ad1...	voxlingua33-0003d1859883ec4b6b4f7ddb0a0e8a4c	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 316640, "member": "voxlingua33-00048a9e313c8d487ba5e8940ef09c9c.wav", "pcm_bytes": 633280, "sample_rate": 16000, "sample_width": 2, "sha256": "0da6f225b59bdcc7f9f222369681f29b88cbca4f8dd9189f4e...	voxlingua33-00048a9e313c8d487ba5e8940ef09c9c	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 211520, "member": "voxlingua33-0006b481f6415e29feeb773e87e2203d.wav", "pcm_bytes": 423040, "sample_rate": 16000, "sample_width": 2, "sha256": "8f0cf92140c11d2ba663251e972c109c0c7934fdbdbcaadfb2...	voxlingua33-0006b481f6415e29feeb773e87e2203d	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 184320, "member": "voxlingua33-00091f18cd029be914cf05bcfaec738b.wav", "pcm_bytes": 368640, "sample_rate": 16000, "sample_width": 2, "sha256": "2d39b3dbafe7e3ac9a88d8936983057c9021d200f03f99462b...	voxlingua33-00091f18cd029be914cf05bcfaec738b	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 71360, "member": "voxlingua33-00095ab848278559990e6f381141438f.wav", "pcm_bytes": 142720, "sample_rate": 16000, "sample_width": 2, "sha256": "b2bd00a7f59b3abd31e9925196bdfa8ad2a6376838cf76b027b...	voxlingua33-00095ab848278559990e6f381141438f	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 233120, "member": "voxlingua33-0009d3e6d1d105122339a400e616af74.wav", "pcm_bytes": 466240, "sample_rate": 16000, "sample_width": 2, "sha256": "d87700e094aebfbd3d2e0a581cd891ffaf1e7bcbd9ba3e1ede...	voxlingua33-0009d3e6d1d105122339a400e616af74	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 135040, "member": "voxlingua33-000ef0891419f3c046a4d096ee1adbfa.wav", "pcm_bytes": 270080, "sample_rate": 16000, "sample_width": 2, "sha256": "42ae48f5d8bf59c63761aa738306ee5a30aa110587d22a30fc...	voxlingua33-000ef0891419f3c046a4d096ee1adbfa	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 174080, "member": "voxlingua33-0016e1ff51aac8b2d908c11b530b14fe.wav", "pcm_bytes": 348160, "sample_rate": 16000, "sample_width": 2, "sha256": "f17f47137131da6016bc4f891065b0bf6388377f4c695ffa98...	voxlingua33-0016e1ff51aac8b2d908c11b530b14fe	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 140800, "member": "voxlingua33-00173bbce6991aa5ae1ad07740f8f016.wav", "pcm_bytes": 281600, "sample_rate": 16000, "sample_width": 2, "sha256": "d74ebeb992cf135d4883ec0085a4f31b873582db8978d79640...	voxlingua33-00173bbce6991aa5ae1ad07740f8f016	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 213440, "member": "voxlingua33-00178621f6646ccd0f21c1983e3c85b8.wav", "pcm_bytes": 426880, "sample_rate": 16000, "sample_width": 2, "sha256": "7ad96b699cf2f4f49dcc1b7d2bf9da28b67e2e3934263062f9...	voxlingua33-00178621f6646ccd0f21c1983e3c85b8	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 156480, "member": "voxlingua33-00227df4afb61257069cabf2a299ee5d.wav", "pcm_bytes": 312960, "sample_rate": 16000, "sample_width": 2, "sha256": "99b4f436c7bec1f47c317b21c062c1747d9b8c11f526d616bb...	voxlingua33-00227df4afb61257069cabf2a299ee5d	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 80160, "member": "voxlingua33-0023f8324beb2e57a8d43c1a18f4a162.wav", "pcm_bytes": 160320, "sample_rate": 16000, "sample_width": 2, "sha256": "e1b70beb0e56479ad752d204caf8d8b67e17fa2bc39396fa3bb...	voxlingua33-0023f8324beb2e57a8d43c1a18f4a162	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 138560, "member": "voxlingua33-002461cb2121d71cf4776688dc01740f.wav", "pcm_bytes": 277120, "sample_rate": 16000, "sample_width": 2, "sha256": "0ab1131e813e651a7e61c0e5266350bf37a8b10277f6be92c0...	voxlingua33-002461cb2121d71cf4776688dc01740f	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 243680, "member": "voxlingua33-002490362a475333edde06a0e1d6e265.wav", "pcm_bytes": 487360, "sample_rate": 16000, "sample_width": 2, "sha256": "4a37399287f532effae683964e7a4b761fea5378e3c6c6c211...	voxlingua33-002490362a475333edde06a0e1d6e265	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 101120, "member": "voxlingua33-0024c08e168c5cd9ac28e6d4ad3d8d99.wav", "pcm_bytes": 202240, "sample_rate": 16000, "sample_width": 2, "sha256": "6b73cd15156cf9a27b2115283cc728eada95505094d6ee5364...	voxlingua33-0024c08e168c5cd9ac28e6d4ad3d8d99	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 144640, "member": "voxlingua33-00289f3662462ac061b2fc6b1dc8b8f6.wav", "pcm_bytes": 289280, "sample_rate": 16000, "sample_width": 2, "sha256": "77e10a257019573706eaa8df661f0118ac9e35c8a6d836a921...	voxlingua33-00289f3662462ac061b2fc6b1dc8b8f6	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 260800, "member": "voxlingua33-002979c25b538d44ea0bd9f13c4c641d.wav", "pcm_bytes": 521600, "sample_rate": 16000, "sample_width": 2, "sha256": "0a5488b4e4e8b7d5ffc17647c897e4d77652d56ee7965d414f...	voxlingua33-002979c25b538d44ea0bd9f13c4c641d	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 81920, "member": "voxlingua33-002af77785e0facfcbc11b1ca049928e.wav", "pcm_bytes": 163840, "sample_rate": 16000, "sample_width": 2, "sha256": "a463c0f632a26a70d17cb528e055088565a3582ca0fdda078d1...	voxlingua33-002af77785e0facfcbc11b1ca049928e	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 232640, "member": "voxlingua33-002dcb46c26e2e6489d45bd27594107f.wav", "pcm_bytes": 465280, "sample_rate": 16000, "sample_width": 2, "sha256": "95d02bdc83baa477894db89c2bbe006d3072e7385654490727...	voxlingua33-002dcb46c26e2e6489d45bd27594107f	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 75360, "member": "voxlingua33-002e5941246fbb99d86e9e0d49d52fbf.wav", "pcm_bytes": 150720, "sample_rate": 16000, "sample_width": 2, "sha256": "e346c817c0adfc7775abb9b5e2a6a47117f216b6dd18d1afd79...	voxlingua33-002e5941246fbb99d86e9e0d49d52fbf	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 160960, "member": "voxlingua33-00303cb8f322c17b2847fd548a1176dd.wav", "pcm_bytes": 321920, "sample_rate": 16000, "sample_width": 2, "sha256": "e2c57be7f24279db6ed2fb9b47f6ec6d521af2da85dacac8ca...	voxlingua33-00303cb8f322c17b2847fd548a1176dd	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 77760, "member": "voxlingua33-00312263cbb743a3209b93284886378a.wav", "pcm_bytes": 155520, "sample_rate": 16000, "sample_width": 2, "sha256": "ef472b0d014a968e49dca4a707c1a642af9fca4b65495c29d99...	voxlingua33-00312263cbb743a3209b93284886378a	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 118080, "member": "voxlingua33-003931b9fe7370eba3659f9b98666e01.wav", "pcm_bytes": 236160, "sample_rate": 16000, "sample_width": 2, "sha256": "91f7a66839633eacbc079432c2acc9ab3ff5db0d110291dbaf...	voxlingua33-003931b9fe7370eba3659f9b98666e01	hf://datasets/ltuncay/chime-voxlingua33@1cde330effd322a2908b6f5218a2b749b642aeba/data/train/shard-000001.tar
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