Datasets:

ltuncay
/

chime-esc-50

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 · ESC-50

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: ESC-50.
Pinned source release: 33c8ce9eb2cf0b1c2f8bcf322eb349b6be34dbb6.
Selection: All 2,000 native WAVs and official five-fold/source metadata, README and LICENSE.
Original license/access conditions: CC-BY-NC-3.0 (ESC-50); individual source information in LICENSE. Public download; noncommercial use and attribution. Preserve source-group folds.
Source notes: Git blob SHA-1 authenticates each pinned file against the release tree; also record local SHA-256 after download.

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 2,000 audio assets, 882,088,000 stored-audio bytes and 882,000,000 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

fold1: 400 original audio assets.
fold2: 400 original audio assets.
fold3: 400 original audio assets.
fold4: 400 original audio assets.
fold5: 400 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-esc-50", 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) fold1, fold2, fold3, fold4, fold5, 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)
`esc50-classification`	classification	balanced_accuracy	5	fold1, fold2, fold3, fold4, fold5	fold1, fold2, fold3, fold4, fold5	fold1, fold2, fold3, fold4, fold5

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: 83

Total file size:

893 MB

json dict	__key__ string	__url__ string
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-0023c0e25801826911ac0ed25a2f8299.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "40acbd05cffe4f1a0a435a4ac31e2d1a747f3ecaa43a04ffaf93ea4...	esc-50-0023c0e25801826911ac0ed25a2f8299	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-00a1945444955a867d44872179b05ff9.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "81312e10f426ae6312c29a97fd4227ef36bcae323c520ddff4b8e89...	esc-50-00a1945444955a867d44872179b05ff9	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-00a86b6e763243132a6653b1e705f7c2.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "1b4210c50a01cabab07065a112463fad4f82de60385ef9aaf6316af...	esc-50-00a86b6e763243132a6653b1e705f7c2	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-00ea58cf9bd92736089fa3ae26b80ed9.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "4e0e2251d9819f49e8d9ee6c4106f9e4de95995e336be88c517269d...	esc-50-00ea58cf9bd92736089fa3ae26b80ed9	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-015f2a3bc5c314fb641829439af623f8.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "1b7ae0e5dbef4b0778aa3f90e31ffb5e5bf479720b459f023f08771...	esc-50-015f2a3bc5c314fb641829439af623f8	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-021e396b2bbefe915a381e6fccc47366.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "f8958a100e0b005c1421eac9b114d4dff6b0d7d58caf24ba933a2c6...	esc-50-021e396b2bbefe915a381e6fccc47366	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-0251d178b025c6d8e2834116969c6f02.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "897a289834ff66b503326ca50ffb5771e97a4524f04fa5facca5278...	esc-50-0251d178b025c6d8e2834116969c6f02	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-028b585e84e0d1cd685a5ecb278d20b7.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "c5c88b84b54d976693bf3c55575d065986a00e46362f6b111536fcf...	esc-50-028b585e84e0d1cd685a5ecb278d20b7	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-0322a7621399b7d5707dcaeef6997a9d.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "4c1b3bc531fb57cbe6d23a111d35878579070fafad0a1508b954cf9...	esc-50-0322a7621399b7d5707dcaeef6997a9d	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-0363842780e73f78c4b5af528ff65b11.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "167d606a6707a59115243e50b344c5cf97f5865f3ec70585feb7480...	esc-50-0363842780e73f78c4b5af528ff65b11	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-037a00b7a8da5b4f7229d084409c903e.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "f92e52f9ab78d700dc10bcbc36020082613e669db245961b3205e83...	esc-50-037a00b7a8da5b4f7229d084409c903e	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-03b94c538b737dda246cafed33292b1f.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "f28b0f8158b6a5adfa4bca5df5c71963c839dd22815440c8b3e220e...	esc-50-03b94c538b737dda246cafed33292b1f	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-0423917e1acfae070f8bf3bec8314aef.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "ca32a4cd05246a83b4ede5ecd48d05de0fd026169002f786f28de7e...	esc-50-0423917e1acfae070f8bf3bec8314aef	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-04776229c7a775498c9fce686e8a5749.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "3cbcd59a0db21f0ed8c252c74ea1bc193117d2c13ed99821885c46c...	esc-50-04776229c7a775498c9fce686e8a5749	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-04f0cea30a829849c731d578339a11ae.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "8d26e40e0e382b0d2d7f6dafcb132cfe703ee3f7f32780e878fdb5a...	esc-50-04f0cea30a829849c731d578339a11ae	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-05214ba0ab94a00dc44bbbe502eccf31.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "853c5ceac82891c53d4c8f918ee74e91fa56187c5c5c0442020440f...	esc-50-05214ba0ab94a00dc44bbbe502eccf31	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-05b7b7d3b84b092444d80ef725c1ec5b.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "b0ce84a3c012d144a1f97394cab2830512078d660e809eecb8abc4f...	esc-50-05b7b7d3b84b092444d80ef725c1ec5b	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-05c5c0c02d4c8e532386e0163a07e9d1.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "6fda26eb779f5cd7edd40280d8073c43b399e80d2e2e23939529ec0...	esc-50-05c5c0c02d4c8e532386e0163a07e9d1	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-060922928df13a138a676a8fba8445e7.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "2abe50ef7597a38f17d49076a3800690355a8f70ab2952298dd0277...	esc-50-060922928df13a138a676a8fba8445e7	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-061a54061bac8411f9dcc06488e0f5ea.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "417d56b2057b5e2b4e73eeffbef13a333e788fce95e9622ddad47e1...	esc-50-061a54061bac8411f9dcc06488e0f5ea	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-061e621537b9211c615a744477bc99d2.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "f3597d925f71cc6a85f03cd08edeed6657b5433d70c229484d8f6cc...	esc-50-061e621537b9211c615a744477bc99d2	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-063d6ec63973d2718f8d2142c0771662.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "6e225fdade2a26e8477eab8f3d9058537cd919de78fd1a2dcb029dc...	esc-50-063d6ec63973d2718f8d2142c0771662	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-06ae1bcb63dce76105b796d6447f8d67.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "68c54aaccb36a91281ca689aaa8d4d888e7a53c571f4c00995a9f69...	esc-50-06ae1bcb63dce76105b796d6447f8d67	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 220500, "member": "esc-50-07b1ff8590adf7fa8a72ceb7693c6459.wav", "pcm_bytes": 441000, "sample_rate": 44100, "sample_width": 2, "sha256": "43814c96e62a5c2fbf14b84828cc929e381e53c0add7c1e8211d212...	esc-50-07b1ff8590adf7fa8a72ceb7693c6459	hf://datasets/ltuncay/chime-esc-50@5a54228df8a5b61684c19b631a08600b7c4cbefa/data/fold1/shard-000000.tar
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