Datasets:

ltuncay
/

chime-nsynth

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 · NSynth

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: NSynth.
Pinned source release: 2017-04-10 JSON/WAV (duplicate-note correction).
Selection: Full original JSON/WAV release: all 305,979 notes, instruments, pitches and velocities; preserve official train/valid/test.
Original license/access conditions: CC-BY-4.0 Public anonymous download; no access request or click-through agreement. Retain original per-recording terms and attribution.
Source notes: GCS JSON API provides immutable generation-pinned URLs, exact sizes and MD5. Original custom HTTPS host has a certificate mismatch; canonical Google Storage URL works. Use WAV/JSON archives, not TFRecords or converted HF audio.

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 305,979 audio assets, 39,178,775,076 stored-audio bytes and 39,165,312,000 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

test: 4,096 original audio assets.
train: 289,205 original audio assets.
valid: 12,678 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-nsynth", 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: 4096 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)
`nsynth-family`	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: 83

Total file size:

41.1 GB

json dict	__key__ string	__url__ string
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-00027ef13e40e77d71cd749521fcba71.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "cd97335df8ceb9df34fd4fc741800850478a2b7e4a3fadd52e7dfa60...	nsynth-00027ef13e40e77d71cd749521fcba71	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-003b7bc1e2af385a97cd4e63250984c0.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "483168567d6792cee09e5749cd19fdaf16935e483546656a321d24fe...	nsynth-003b7bc1e2af385a97cd4e63250984c0	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-00475bf09e6244d80a52c4fa736a9047.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "29499eb39b807025a07e96645768e5ccc0e02981cd2dc3e454127948...	nsynth-00475bf09e6244d80a52c4fa736a9047	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-0055efd4f20bae8236b0233ababa1581.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "f2b3c2df06a12d4865aaf1f84e29d4ac61955801405d4af15c6c02d3...	nsynth-0055efd4f20bae8236b0233ababa1581	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-0062776dab00fa06f0955bb3daa1400e.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "50c94257ea4d642e136f15081b2be46cf4fa0be00879acce93d32ec9...	nsynth-0062776dab00fa06f0955bb3daa1400e	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-006ad2e7fec8f7f40bdc9c7e8f309cee.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "fc3dd80aacc6a8ceb75bc752109a4cf7ae8f9082ebd8d280b87277ba...	nsynth-006ad2e7fec8f7f40bdc9c7e8f309cee	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-0070bc9214f24d1747e2a076d91d32c4.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "53b1183916d655144ca26371961876dda70fb690c872bf0581aaa555...	nsynth-0070bc9214f24d1747e2a076d91d32c4	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-00721f70b175dcbcc0ca81c0ae5e65df.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "71c86ba8fe35aa078d27600d80d31450062c2e07c07f80e612ddb37a...	nsynth-00721f70b175dcbcc0ca81c0ae5e65df	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-00779c118cd376377438a16e30503efc.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "ee84d4d58d76bd715e1d7d73b2e459a443760f0a77ca60e1f1bf69fc...	nsynth-00779c118cd376377438a16e30503efc	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-0083b80c5097ecf502ac600b730cc737.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "8a5c3381f24e92b7f78616ebf08e95b0390b948a0deff4b664cea808...	nsynth-0083b80c5097ecf502ac600b730cc737	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-00874fcb64db45996c67fbc846e041ff.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "ae9a5b9f1a4abfb17679a301f19630e0c495d14ddfa64f5f1fba707a...	nsynth-00874fcb64db45996c67fbc846e041ff	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-008daeba15de9a4636d8a472733075d1.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "b56af0569fe5e9b055d55a320e9908445b3c8c1ce21940cdc107ed1c...	nsynth-008daeba15de9a4636d8a472733075d1	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-009218262e8d3f98a2832a687ccc9ebe.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "06a0915ca13dad45b2afa97c0f522abaed116f0043e8f70fdac2bebc...	nsynth-009218262e8d3f98a2832a687ccc9ebe	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-0095d16f093b2ebdf809b83369ca149c.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "ee619959c4bc323072045f1846175dddfb1926c5a63f18f8d0387efa...	nsynth-0095d16f093b2ebdf809b83369ca149c	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-00a375853b76aa8a20507d329d49d653.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "492100d703db8c07c413a0fff218197f5334eeb9f3c200a06b59e59a...	nsynth-00a375853b76aa8a20507d329d49d653	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-00a40460ca53d3d38eb22d2add3abc5a.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "7b345bdafd03171f7d4e43e17d1a6c0599a9d1056993fbaf6be86d69...	nsynth-00a40460ca53d3d38eb22d2add3abc5a	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-00a6aaeb41b82acbdfe8bfa968450b0b.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "0a1b6d59f8eb40a3f4e8cac5fd38892929cc2d1632b91a0eb2dee886...	nsynth-00a6aaeb41b82acbdfe8bfa968450b0b	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-00bdb1795fa10e48a83ddbc435779791.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "b3601113c38ce363ca9d060d10154e26ed4b63a6bfac97ca2fa7e13d...	nsynth-00bdb1795fa10e48a83ddbc435779791	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-00e0c2a6296a277f18dc9f03279a7aa7.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "58dc80ffa91c693221ac84a91a0a9aea3f4b9a2e0735b6d33dddaecb...	nsynth-00e0c2a6296a277f18dc9f03279a7aa7	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-00f0ba2fd0dba8ef0ad07db29ebe50eb.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "c352a2f805550c62f38c09805935ef089d6ffb661605e661c7aa6b8d...	nsynth-00f0ba2fd0dba8ef0ad07db29ebe50eb	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-00f8a105e051b27d9f05f9a6fa6551d0.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "b3f487206ea0e3133ca8dfb6b66a07218e99b1bf415c578aec2f5e63...	nsynth-00f8a105e051b27d9f05f9a6fa6551d0	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-0103f97599869b39ae1aefd3032a4089.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "681fe4b6397ddfcb6dbb4506fc645783a59783859a45ad967c26123d...	nsynth-0103f97599869b39ae1aefd3032a4089	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-010d2ba31ceacb2ca318332a36a25ede.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "9e46853ee787b4b5d163ec3013bccfeef13a7468502defb73e2dd36c...	nsynth-010d2ba31ceacb2ca318332a36a25ede	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-01320e9871c93de6b81844a4e9bf750c.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "de3d341947341a6cbcde83c99326d1015e550c98cb18ab60cfac33fd...	nsynth-01320e9871c93de6b81844a4e9bf750c	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64000, "member": "nsynth-0139330a5134435ce9aaa3a1ae33c47d.wav", "pcm_bytes": 128000, "sample_rate": 16000, "sample_width": 2, "sha256": "d40b809306a0d090df08c9b111c4fd4017cc4b0bf7603619e442c538...	nsynth-0139330a5134435ce9aaa3a1ae33c47d	hf://datasets/ltuncay/chime-nsynth@dc21c337f7572d7d9ab175da61c11bfc048d369b/data/test/shard-000000.tar
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