Datasets:

ltuncay
/

chime-vocalset

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 · VocalSet

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: VocalSet.
Pinned source release: 1.1.
Selection: Full original archive, one organization of each recording.
Original license/access conditions: CC-BY-4.0 Public download; retain attribution.
Source notes: The later 1.2 deposit adds three file organizations; its VocalSet11.zip has the same checksum. Avoid the redundant 1.2 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 3,613 audio assets, 2,792,209,216 stored-audio bytes and 2,792,050,244 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: 914 original audio assets.
train: 2,699 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-vocalset", 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: 914 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)
`vocalset-technique`	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: 102

Total file size:

2.81 GB

json dict	__key__ string	__url__ string
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 116445, "member": "vocalset-000e0436cb8c55e98faedc44c44db997.wav", "pcm_bytes": 232890, "sample_rate": 44100, "sample_width": 2, "sha256": "40d5a774ff71acead0ba577475b25aa01e3fea5e44a645a26f332...	vocalset-000e0436cb8c55e98faedc44c44db997	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 434852, "member": "vocalset-002236d837966a3a459297dd953cb74a.wav", "pcm_bytes": 869704, "sample_rate": 44100, "sample_width": 2, "sha256": "6f40ded8bd0c3b96920cee34377492ac885caaa8986f376422020...	vocalset-002236d837966a3a459297dd953cb74a	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 446035, "member": "vocalset-0023990c311311f9b85bf73dd56615df.wav", "pcm_bytes": 892070, "sample_rate": 44100, "sample_width": 2, "sha256": "3e377121523bd62e979be2b8ebcfef05f8b48ce2fbb5f8f887c38...	vocalset-0023990c311311f9b85bf73dd56615df	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 320816, "member": "vocalset-002ed0c548bad0c1cf60e856981c2180.wav", "pcm_bytes": 641632, "sample_rate": 44100, "sample_width": 2, "sha256": "fd7bd16a0c67c32aa526eb8e3e1e6e675dcdd3f28326d092d526c...	vocalset-002ed0c548bad0c1cf60e856981c2180	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 465289, "member": "vocalset-006243ee7ccce2601f064ebad1b031a6.wav", "pcm_bytes": 930578, "sample_rate": 44100, "sample_width": 2, "sha256": "d18b00ade180d77a348060b0a41c1e7a76f772183ea4076d05197...	vocalset-006243ee7ccce2601f064ebad1b031a6	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 389303, "member": "vocalset-00727c91b07d97a5e7edee284f2df857.wav", "pcm_bytes": 778606, "sample_rate": 44100, "sample_width": 2, "sha256": "66d96ed410af68011d0a914296e503f588cc1ccada8565b3b8efa...	vocalset-00727c91b07d97a5e7edee284f2df857	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 214806, "member": "vocalset-008e0602ee7fc8b309cb59c0320e3c6b.wav", "pcm_bytes": 429612, "sample_rate": 44100, "sample_width": 2, "sha256": "ced0cba3f081af012b737ee10913927cae8f404345c847a7ee76c...	vocalset-008e0602ee7fc8b309cb59c0320e3c6b	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 409291, "member": "vocalset-00906efac3b0959a4c6c0b66f2bb8e19.wav", "pcm_bytes": 818582, "sample_rate": 44100, "sample_width": 2, "sha256": "17f3973f38f1509bc105d1dfa90fdbaff0eeb961a7e4d47d6c15e...	vocalset-00906efac3b0959a4c6c0b66f2bb8e19	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 315531, "member": "vocalset-00b26e2693086ba932f2c0ff32581191.wav", "pcm_bytes": 631062, "sample_rate": 44100, "sample_width": 2, "sha256": "c147ced28b8613f0b14affeed06a2a10e6fe7c5fb40965d2a4cd4...	vocalset-00b26e2693086ba932f2c0ff32581191	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 428670, "member": "vocalset-00bb754413d151c6ebd4fa79614b7701.wav", "pcm_bytes": 857340, "sample_rate": 44100, "sample_width": 2, "sha256": "944babf26b8311f74894d26a97a85d0e53efe19c526dc131d6267...	vocalset-00bb754413d151c6ebd4fa79614b7701	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 276370, "member": "vocalset-00d5027507cdde68f800a475612d0746.wav", "pcm_bytes": 552740, "sample_rate": 44100, "sample_width": 2, "sha256": "78115397d7ff4d92a7078f509f4f2e04ce4dfb2c11195da214a41...	vocalset-00d5027507cdde68f800a475612d0746	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 432087, "member": "vocalset-00f0a3ae42e46f8f36f193e099961f09.wav", "pcm_bytes": 864174, "sample_rate": 44100, "sample_width": 2, "sha256": "1d3a9cce323281cba88d4c453a4f9893b09967711f8e909e5998f...	vocalset-00f0a3ae42e46f8f36f193e099961f09	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 262753, "member": "vocalset-01149a087a4e4b46080831d4d7126fe8.wav", "pcm_bytes": 525506, "sample_rate": 44100, "sample_width": 2, "sha256": "25abd412c7208ae2e049ac99373581bc09e0803a291580f41b8eb...	vocalset-01149a087a4e4b46080831d4d7126fe8	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 99948, "member": "vocalset-013056531f6092fd318bb0a9496c1692.wav", "pcm_bytes": 199896, "sample_rate": 44100, "sample_width": 2, "sha256": "901171f3a3cbe5f10ed9c2ae75d6b8ca415756c4a094e14292a8b3...	vocalset-013056531f6092fd318bb0a9496c1692	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 72382, "member": "vocalset-017a72b72c35276ad15562cec1f90944.wav", "pcm_bytes": 144764, "sample_rate": 44100, "sample_width": 2, "sha256": "0d3c1e5865cb2371d261cda525ee2c580f80268efbdd208d1b778c...	vocalset-017a72b72c35276ad15562cec1f90944	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 464288, "member": "vocalset-019133d73bd8dbb9ecafc423b3ffadb8.wav", "pcm_bytes": 928576, "sample_rate": 44100, "sample_width": 2, "sha256": "c18627d88576898b6b3a4f54a57b87a51a466e0382fc3e58ba4cc...	vocalset-019133d73bd8dbb9ecafc423b3ffadb8	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 302505, "member": "vocalset-019b590e7f7001467e848b880bb0cf6b.wav", "pcm_bytes": 605010, "sample_rate": 44100, "sample_width": 2, "sha256": "6393ae63a46c10499e70b3ea78385af1e3e3548190e706e95baf3...	vocalset-019b590e7f7001467e848b880bb0cf6b	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 171969, "member": "vocalset-01b1b4b5d46494627a2d8bcaa09a745b.wav", "pcm_bytes": 343938, "sample_rate": 44100, "sample_width": 2, "sha256": "896df3a7e5fc5975e18018386e4ff9fc33222098aeadc5e4a26e6...	vocalset-01b1b4b5d46494627a2d8bcaa09a745b	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 144012, "member": "vocalset-01baad52a43d434695b9250371023a8e.wav", "pcm_bytes": 288024, "sample_rate": 44100, "sample_width": 2, "sha256": "a5299eb9e2b77a1b72f4bf2f16b24504dd3c56d52ccd57c4f644b...	vocalset-01baad52a43d434695b9250371023a8e	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 396135, "member": "vocalset-01c8ed62a3d5fdfe41d5e30852581a85.wav", "pcm_bytes": 792270, "sample_rate": 44100, "sample_width": 2, "sha256": "16ceadfdf63b47de06663c4822bd69710f4a3ec7ab329d04dc0c3...	vocalset-01c8ed62a3d5fdfe41d5e30852581a85	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 161431, "member": "vocalset-01cc6c9e893f02e69f49e5c53d23e09b.wav", "pcm_bytes": 322862, "sample_rate": 44100, "sample_width": 2, "sha256": "4013d2c8a6b652304ba0024cfa5ea18d8f7e87bb07d5d8588efc8...	vocalset-01cc6c9e893f02e69f49e5c53d23e09b	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 351898, "member": "vocalset-01e05bc79e45828ff691dd2a3d91b93b.wav", "pcm_bytes": 703796, "sample_rate": 44100, "sample_width": 2, "sha256": "489464432b4f9b45a6274f499902df2a34230770cc3bbb0a49097...	vocalset-01e05bc79e45828ff691dd2a3d91b93b	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 383897, "member": "vocalset-01f35ef2b2fbda628e07064354dd9bcd.wav", "pcm_bytes": 767794, "sample_rate": 44100, "sample_width": 2, "sha256": "909f1b6f9821d5c7e7116bef02b5bcdc53404adecc745763f8830...	vocalset-01f35ef2b2fbda628e07064354dd9bcd	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 437676, "member": "vocalset-020dc4143b8b50256283b9e1748c39a2.wav", "pcm_bytes": 875352, "sample_rate": 44100, "sample_width": 2, "sha256": "f6d8724c41cfe6fa06f030a79bea475c4337d103968003cdd75f4...	vocalset-020dc4143b8b50256283b9e1748c39a2	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 3363826, "member": "vocalset-0219f56abdb94d3dce8b9563c86c1358.wav", "pcm_bytes": 6727652, "sample_rate": 44100, "sample_width": 2, "sha256": "2a1a22d87f604690f9b1c4b3e8076cc85f534bf882799e26301...	vocalset-0219f56abdb94d3dce8b9563c86c1358	hf://datasets/ltuncay/chime-vocalset@61be935b042cfb88774ece8d405e6e78e03ec63d/data/train/shard-000001.tar
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