Datasets:

ltuncay
/

chime-gtzan

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 · GTZAN

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: GTZAN.
Pinned source release: Native maintainer archive d2146561ecc7df707d9e6b8318885fe6a39668a2; Sturm reference partitions fedc781026cf1a20034830f8071c98d93a422016.
Selection: Acquire all1000original native WAV clips and fault/artist metadata. Keep standard uncorrected genre task outside primary score; historical fault-filtered930clip protocol is an explicitly named optional diagnostic.
Original license/access conditions: Recording copyright/license unspecified in maintainer card; public research acquisition only. Apache/GPL/MIT software labels do not license recordings. Anonymous public maintainer distribution with no corpus access agreement. Acquisition for research is separate from unresolved redistribution rights; no public rehosting in this task.
Source notes: Maintainer namespace is public,ungated and anonymously downloadable; original raw TAR.GZ has LFS SHA256, not a resampled Parquet representation. Bounded sampleWAV header verifies22050Hz monoPCM16. Source-wide decoding remains a preparation check. Public distribution is an acquisition endpoint, not proof of rights to publish a new mirror. No new rehosting is authorized or performed.

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 999 audio assets, 1,322,781,424 stored-audio bytes and 1,322,737,468 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

unspecified: 999 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-gtzan", 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: 290 records in partitions defined by CHIME (this release's native splits do not mark them), 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)
`gtzan-genre`	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: 71

Total file size:

1.33 GB

json dict	__key__ string	__url__ string
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-0001878e7497e128684760adb1d432d7.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "47d5ce87ec3cc97a5a46aed407d45082fb41698ca0c13aaedf23991...	gtzan-0001878e7497e128684760adb1d432d7	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-0033b043cc396a50bbaf0baf2c11ab49.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "09ba4d9a8be6fba6c11fd5943051ca5dbd2f8eaf7eda294aedcd8a5...	gtzan-0033b043cc396a50bbaf0baf2c11ab49	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-00485a23b29351fe3cf1fedcb6f2c42f.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "c57a4f889a27e0c8f22379f758b63e80b3848994f3cecf3db83828f...	gtzan-00485a23b29351fe3cf1fedcb6f2c42f	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-00714b2493d288738c3384b1b0d80837.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "22b7cee72810275e909118b8f1e0d9445fc56846988a589d91186de...	gtzan-00714b2493d288738c3384b1b0d80837	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-00a3a23592c3559c7aa1b9adaff91b2d.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "66643f187227d2d90c88327ca8767713fc2a0a66b05c1679221560f...	gtzan-00a3a23592c3559c7aa1b9adaff91b2d	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-00b1a9974f77ac759e6b8f9aa8a8605c.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "d994c4b9c77971a5dccdc114fc7449ff1b04ccda424f91a4d147c6a...	gtzan-00b1a9974f77ac759e6b8f9aa8a8605c	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-00b8e4cc52abab83182f008915deee5d.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "1ad5886871ba35bb8808e2b1409e0aedd4803dc459dc84bbc691e4a...	gtzan-00b8e4cc52abab83182f008915deee5d	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-018a67fdd071e1dfd076664cc05d607e.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "e1900f7ed5b537609293ee402e01226c3badf2922954bfe50dae741...	gtzan-018a67fdd071e1dfd076664cc05d607e	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661504, "member": "gtzan-01cc84ca03cfdd46428912fb58e7e6a0.wav", "pcm_bytes": 1323008, "sample_rate": 22050, "sample_width": 2, "sha256": "297b1836ebf2891f474aea90daa2f1762569c5e0f755b7b8da6f81a...	gtzan-01cc84ca03cfdd46428912fb58e7e6a0	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-024b2a8a73b09603d02bdc5906403859.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "0c1e5a2a195dae1533378dcae3e638c1b9969b6833fcaa9d921e869...	gtzan-024b2a8a73b09603d02bdc5906403859	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-026ccc5e49dfe0638cd0165fb0e2934e.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "0f2335aafc996383a4f409a1a0400a3442de68cfba8133715b88a89...	gtzan-026ccc5e49dfe0638cd0165fb0e2934e	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661504, "member": "gtzan-02c6da3f9f807bb25b220438758672bd.wav", "pcm_bytes": 1323008, "sample_rate": 22050, "sample_width": 2, "sha256": "a45cc4005c96e8c00b684f852b5b9cc52be95867cf5ae36a1797c2e...	gtzan-02c6da3f9f807bb25b220438758672bd	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661504, "member": "gtzan-02e01e844f588e8d9b09deafac912608.wav", "pcm_bytes": 1323008, "sample_rate": 22050, "sample_width": 2, "sha256": "989718a9bb6d4a9e6d8bddae9b7c56fad99aa44430a976f37e66051...	gtzan-02e01e844f588e8d9b09deafac912608	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-02f8059920df4718a722d1731f07037f.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "247509a491c459694eef7c159c312f59dbd22712f850c04e18f366e...	gtzan-02f8059920df4718a722d1731f07037f	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-02fe54f5fd491316a7269f60b0c544f3.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "8ece32b272d07ecf176400464dc3cb3f4aa0c9be308b2d6bc9e43f9...	gtzan-02fe54f5fd491316a7269f60b0c544f3	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661504, "member": "gtzan-03424203aec85178fe91b3fa5d00840d.wav", "pcm_bytes": 1323008, "sample_rate": 22050, "sample_width": 2, "sha256": "0f534671f25a614dbf91183c7d0bf8f0bd0812db16ea18fc42ff251...	gtzan-03424203aec85178fe91b3fa5d00840d	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-0359135eb90d9c99a5f768dc588d1328.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "65a251cb9318387c805db382e58ecc3e1da7f11b01f106dad417333...	gtzan-0359135eb90d9c99a5f768dc588d1328	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661504, "member": "gtzan-036128e83ee8c9078d4799a3d84d714d.wav", "pcm_bytes": 1323008, "sample_rate": 22050, "sample_width": 2, "sha256": "444cd511ad2c054a39788387bfc071d3430459d9d637e257dc93e0c...	gtzan-036128e83ee8c9078d4799a3d84d714d	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-03abd5c6ac2f0792ca73136743265e58.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "ffe3d0c0219848518f0262ec41d0cf641f8b01957ef1942da44569e...	gtzan-03abd5c6ac2f0792ca73136743265e58	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-03d187bac312f8741e339de30bd2d6db.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "c0805a1328ba013a76470aff6b74c36cba386a19901b0825f01306e...	gtzan-03d187bac312f8741e339de30bd2d6db	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-03d283cc51c78ba095a89a0b3e3bef73.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "8f756ee23b553baf28f317cc8a1ad11041b5fb029ba738fbf3f2395...	gtzan-03d283cc51c78ba095a89a0b3e3bef73	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-03f45f72aee02e54d360101cf4027cc8.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "c3bb2d29248f9ec4c8fc8b1c8102de6b6ac3c89731ed2af72c3515a...	gtzan-03f45f72aee02e54d360101cf4027cc8	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661504, "member": "gtzan-0434d11368c08a450988370aede03c5d.wav", "pcm_bytes": 1323008, "sample_rate": 22050, "sample_width": 2, "sha256": "fb5e74f166215f91d2b802c024c89e7018baf8272ed79e198800fce...	gtzan-0434d11368c08a450988370aede03c5d	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 661794, "member": "gtzan-04bb4f25b2214544ad5e4d04c09a6b0f.wav", "pcm_bytes": 1323588, "sample_rate": 22050, "sample_width": 2, "sha256": "55dfc6e206889257608c95382146210f3397a7f178188b5bc084beb...	gtzan-04bb4f25b2214544ad5e4d04c09a6b0f	hf://datasets/ltuncay/chime-gtzan@e09504619dfd7d9416f8ecb93b58514b4478f3e4/data/unspecified/shard-000000.tar
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