Datasets:

ltuncay
/

chime-urban-sed

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 · URBAN-SED

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: URBAN-SED.
Pinned source release: 1324404 / v2.0.0.
Selection: Optional controlled-synthetic diagnostic: official native WAVs and exact JAMS event labels are openly deposited; preserve the published waveforms, because regeneration with newer Scaper is not byte-identical.
Original license/access conditions: CC-BY-4.0; preserve FREESOUNDCREDITS.txt and source licenses. Official open direct Zenodo deposit; no website form required.
Source notes: Acquire only pinned objects. Preserve native audio and raw source labels. Subset and quality rules are in the matching decision; scientific admission is pending. Header verification2026-10-29:10,000nativePCM16 scenes,8,820,515,278stored/8,820,075,278PCM bytes;9GBPCM guard.

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 10,000 audio assets, 8,820,515,278 stored-audio bytes and 8,820,075,278 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: 2,000 original audio assets.
train: 6,000 original audio assets.
validation: 2,000 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-urban-sed", 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: 2000 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)
`urban-sed-sed`	frame_multilabel	segment_f1	1	train	validation	test
`urban-sed-tagging`	multilabel	macro_ap	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.

Archived original provenance

Large provenance directories are preserved byte for byte in provenance/archives/*.tar.gz. The index records original logical paths, archive members, sizes and SHA256s. Record references such as raw_annotation_ref retain their original names. Read them with from chime_bench.datasets.provenance import read_provenance, then read_provenance(release_root, reference). No audio, labels or sample keys change.

Downloads last month: 1,086

Total file size:

8.94 GB

json dict	__key__ string	__url__ string
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{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441000, "member": "urban-sed-00067d2d2456d87b73c12782a6cf684f.wav", "pcm_bytes": 882000, "sample_rate": 44100, "sample_width": 2, "sha256": "0e87cac8de7b29bdd4c8b035688a13fa20de2ccda4d0bae1b83b...	urban-sed-00067d2d2456d87b73c12782a6cf684f	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441000, "member": "urban-sed-0019cebf07a5d9bd0a27fa4e75e27174.wav", "pcm_bytes": 882000, "sample_rate": 44100, "sample_width": 2, "sha256": "1276e739f35308b9dfe07e672aea5fd080732b9fc5901830435e...	urban-sed-0019cebf07a5d9bd0a27fa4e75e27174	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441000, "member": "urban-sed-0025c376f8dbf3c95faa51cfe6d540dd.wav", "pcm_bytes": 882000, "sample_rate": 44100, "sample_width": 2, "sha256": "f0bff16da81ac54c9035dd4af53b88c817da268f62bbb6bc61c0...	urban-sed-0025c376f8dbf3c95faa51cfe6d540dd	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441000, "member": "urban-sed-0028aa5818358b353ba9c740361ba5ac.wav", "pcm_bytes": 882000, "sample_rate": 44100, "sample_width": 2, "sha256": "067a103a94ee797b019c81955683266870f123c1be4c03a0faef...	urban-sed-0028aa5818358b353ba9c740361ba5ac	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441001, "member": "urban-sed-002f6bc58b645962662815496fac98e4.wav", "pcm_bytes": 882002, "sample_rate": 44100, "sample_width": 2, "sha256": "4112dc961d58b5b49fac785ecb07e613750f16dcf5bd1354d884...	urban-sed-002f6bc58b645962662815496fac98e4	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441000, "member": "urban-sed-0053d0fbeab62621eaa259ec3ace8ae5.wav", "pcm_bytes": 882000, "sample_rate": 44100, "sample_width": 2, "sha256": "f737f807aa3f1a1dddbb6f534d3dff4d470e01fe671bb9dd23c6...	urban-sed-0053d0fbeab62621eaa259ec3ace8ae5	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441000, "member": "urban-sed-005a18155a08fca964e0f00e357eb39d.wav", "pcm_bytes": 882000, "sample_rate": 44100, "sample_width": 2, "sha256": "26e4799dca0ff295312101c47ef504a72956823db595e39753ad...	urban-sed-005a18155a08fca964e0f00e357eb39d	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
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{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441000, "member": "urban-sed-00714118140652f7677cc9b0bc4f337b.wav", "pcm_bytes": 882000, "sample_rate": 44100, "sample_width": 2, "sha256": "49a2ec11747bb953eaf401bc29e5d15c512b0abe476c0bf443df...	urban-sed-00714118140652f7677cc9b0bc4f337b	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441000, "member": "urban-sed-0071a4a3ee8b7b01596f93653b2bebf9.wav", "pcm_bytes": 882000, "sample_rate": 44100, "sample_width": 2, "sha256": "998f94c055e7e71db9c9a280a2ee8d8e64a3c6a82cff379d919a...	urban-sed-0071a4a3ee8b7b01596f93653b2bebf9	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441000, "member": "urban-sed-00767c0e8beb8178ff136dfa52b5f570.wav", "pcm_bytes": 882000, "sample_rate": 44100, "sample_width": 2, "sha256": "bb2fd5274fa3805736e3ac6c6db7ffe7c27b92af4bd3f9f0d518...	urban-sed-00767c0e8beb8178ff136dfa52b5f570	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441001, "member": "urban-sed-00883194eee68260d93bf687e09c7c82.wav", "pcm_bytes": 882002, "sample_rate": 44100, "sample_width": 2, "sha256": "9309d54b13ad3f2a4259a95d3bc1ae7142480f9c0802c9794923...	urban-sed-00883194eee68260d93bf687e09c7c82	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441000, "member": "urban-sed-0089bda08a6f8db1b9800d0adb154706.wav", "pcm_bytes": 882000, "sample_rate": 44100, "sample_width": 2, "sha256": "b0f5c18a6a4209ff712bbe185ccbb08f2f5a25d99943131f3b7c...	urban-sed-0089bda08a6f8db1b9800d0adb154706	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441001, "member": "urban-sed-008cec0dc4a09d78f4e36aad53106bda.wav", "pcm_bytes": 882002, "sample_rate": 44100, "sample_width": 2, "sha256": "934caa28898589cbfdb70324d09515958458bb8ae98042c15579...	urban-sed-008cec0dc4a09d78f4e36aad53106bda	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441000, "member": "urban-sed-009bb6f51c6a4dfe1d6fb78bde9d0ef0.wav", "pcm_bytes": 882000, "sample_rate": 44100, "sample_width": 2, "sha256": "d67f7e4449bb851b842bfee693441ffdefe18bae5f0d2b77c0a8...	urban-sed-009bb6f51c6a4dfe1d6fb78bde9d0ef0	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441000, "member": "urban-sed-00b3e90323b39de1c911600f301a446f.wav", "pcm_bytes": 882000, "sample_rate": 44100, "sample_width": 2, "sha256": "068f19e7359b7b3adfa3109696701d52cf18ccc0dbb1153678bb...	urban-sed-00b3e90323b39de1c911600f301a446f	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441000, "member": "urban-sed-00b8f2efb2c54bb917db4bc29348054b.wav", "pcm_bytes": 882000, "sample_rate": 44100, "sample_width": 2, "sha256": "dd0f063bb52659a100a7aa091d5cd49dc0fcaf85af5475b1f3a3...	urban-sed-00b8f2efb2c54bb917db4bc29348054b	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441000, "member": "urban-sed-00c7f80aacb137b12dafbec5a16c0cdd.wav", "pcm_bytes": 882000, "sample_rate": 44100, "sample_width": 2, "sha256": "6bde2bb2ca0b6faba28987593ac4609a2ec3bbe14497885754d0...	urban-sed-00c7f80aacb137b12dafbec5a16c0cdd	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441000, "member": "urban-sed-00c9ed5d177e44199927ba2a81bf116a.wav", "pcm_bytes": 882000, "sample_rate": 44100, "sample_width": 2, "sha256": "43a1d8c702d9af0d02cc2ab68a2d0dc4845d900b0c05af745c6e...	urban-sed-00c9ed5d177e44199927ba2a81bf116a	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441000, "member": "urban-sed-00cde91eb19b2377b2ec3eaf3035ac7f.wav", "pcm_bytes": 882000, "sample_rate": 44100, "sample_width": 2, "sha256": "f8691c0232e28ae14e52d2785b7b7a6a20e4e2ed6dc9b2e7f328...	urban-sed-00cde91eb19b2377b2ec3eaf3035ac7f	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 441000, "member": "urban-sed-00da72ad7bdee52b4bffcfeb4852ce6b.wav", "pcm_bytes": 882000, "sample_rate": 44100, "sample_width": 2, "sha256": "8e419b4b12c2492c50c0c3b11e4970147d50c2d03484523cd972...	urban-sed-00da72ad7bdee52b4bffcfeb4852ce6b	hf://datasets/ltuncay/chime-urban-sed@e7d514c5fa1fe08a5c079564bebf589968c08f48/data/train/shard-000002.tar
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