Datasets:

ltuncay
/

chime-vocalsound

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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json dict	__key__ string	__url__ string
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 4096, "member": "vocalsound-199e815255795cadc0b302e5d8d2a3ba.wav", "pcm_bytes": 8192, "sample_rate": 48000, "sample_width": 2, "sha256": "b7890720acdc31d4218536b1a8cf414fcddcabdb7bcddb6e73b726d...	vocalsound-199e815255795cadc0b302e5d8d2a3ba	hf://datasets/ltuncay/chime-vocalsound@45abc8ce2559ee28c3c4b82c21ce55cb733f69a8/data/source_unassigned/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 12288, "member": "vocalsound-39c029dc197b6fd3ce59b4509eff5b7c.wav", "pcm_bytes": 24576, "sample_rate": 48000, "sample_width": 2, "sha256": "02e0ef243c47ecebe138b409054dc4da65b8be3de5e785c698545...	vocalsound-39c029dc197b6fd3ce59b4509eff5b7c	hf://datasets/ltuncay/chime-vocalsound@45abc8ce2559ee28c3c4b82c21ce55cb733f69a8/data/source_unassigned/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 8192, "member": "vocalsound-7a65b8e0e720a92c5917d74d561a0229.wav", "pcm_bytes": 16384, "sample_rate": 48000, "sample_width": 2, "sha256": "f86227311a2dc5ff2d9784b72e8e37803bc59714e2671b480c2432...	vocalsound-7a65b8e0e720a92c5917d74d561a0229	hf://datasets/ltuncay/chime-vocalsound@45abc8ce2559ee28c3c4b82c21ce55cb733f69a8/data/source_unassigned/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 8192, "member": "vocalsound-811d8de2915399dba847e4cd885012f8.wav", "pcm_bytes": 16384, "sample_rate": 48000, "sample_width": 2, "sha256": "28a9d9a470333e5a3dd4aceaf5d951152891a80582ceb8b7cf62fd...	vocalsound-811d8de2915399dba847e4cd885012f8	hf://datasets/ltuncay/chime-vocalsound@45abc8ce2559ee28c3c4b82c21ce55cb733f69a8/data/source_unassigned/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 4096, "member": "vocalsound-88397034377513e663218a02d63b70bb.wav", "pcm_bytes": 8192, "sample_rate": 48000, "sample_width": 2, "sha256": "75f04a7fc5036f21a9383a244028f56f9d161e18826530396edc2b1...	vocalsound-88397034377513e663218a02d63b70bb	hf://datasets/ltuncay/chime-vocalsound@45abc8ce2559ee28c3c4b82c21ce55cb733f69a8/data/source_unassigned/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 4096, "member": "vocalsound-8c6e81dcf29a3cf2e2884d018268a05a.wav", "pcm_bytes": 8192, "sample_rate": 44100, "sample_width": 2, "sha256": "871d3b50ebf88b282d93e1f83b8a674b13076d97d6461b9dc60b6aa...	vocalsound-8c6e81dcf29a3cf2e2884d018268a05a	hf://datasets/ltuncay/chime-vocalsound@45abc8ce2559ee28c3c4b82c21ce55cb733f69a8/data/source_unassigned/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 8192, "member": "vocalsound-9eb5d5970036f1de790747646868e13a.wav", "pcm_bytes": 16384, "sample_rate": 48000, "sample_width": 2, "sha256": "3715e7e9506abe42d86f093ff8726fc62055f23f7679836f0b0636...	vocalsound-9eb5d5970036f1de790747646868e13a	hf://datasets/ltuncay/chime-vocalsound@45abc8ce2559ee28c3c4b82c21ce55cb733f69a8/data/source_unassigned/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 8192, "member": "vocalsound-c0f9252652ed25657f8766f6295a416d.wav", "pcm_bytes": 16384, "sample_rate": 44100, "sample_width": 2, "sha256": "01b6fcb111ecb693670fcb8be3baa4e5b691b8db972aae746ef334...	vocalsound-c0f9252652ed25657f8766f6295a416d	hf://datasets/ltuncay/chime-vocalsound@45abc8ce2559ee28c3c4b82c21ce55cb733f69a8/data/source_unassigned/shard-000000.tar

CHIME · VocalSound

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: VocalSound.
Pinned source release: author native-rate archive observed 2026-10-28.
Selection: Keep all 20,985 nonempty native recordings: 20,977 examples retain the revised official train/validation/test splits; eight short recordings keep valid filename-derived class labels with source_unassigned split. Exclude exactly 39 empty header-only WAVs. Preserve actual rates 8/16/44.1/48/96/192 kHz, mono 16-bit; the upstream archive name does not imply a uniform sample rate.
Original license/access conditions: CC-BY-SA-4.0 Public authoritative HTTPS retrieval without registration or individually signed agreement. Upstream use and attribution conditions remain applicable. This acquisition recipe grants no additional publication rights.
Source notes: Observed snapshot SHA256 is not an author-published checksum. ZIP CRC passed for every entry. The original native-rate archive is retained; no 16 kHz downsampled release is substituted. AppleDouble files are ignored during preparation.

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 20,985 audio assets, 8,239,822,540 stored-audio bytes and 8,238,899,200 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

source_unassigned: 8 original audio assets.
test: 3,591 original audio assets.
train: 15,531 original audio assets.
validation: 1,855 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-vocalsound", 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: 3590 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)
`vocalsound-vocalization`	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.

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