Datasets:

ltuncay
/

chime-ami

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 · AMI

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: AMI.
Pinned source release: manual annotations 1.6.2 and author Mix-Headset sessions; observed 2026-10-28.
Selection: Exactly ES2002a (custom train), IS1000a (custom validation), TS3003a (custom test), complete author-provided Mix-Headset WAV sessions. Their 12 global speaker IDs are pairwise disjoint and frozen in ami.json. Preserve source-native 16 kHz audio and the original mixture view; no local downmix or utterance clipping. This custom three-scenario subset is not the standard full AMI benchmark split.
Original license/access conditions: CC-BY-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: The author download chooser supplied these exact HTTPS URLs. Manual annotation ZIP CRC and all selected word timestamps were validated against the actual native session durations. The full annotation package remains provenance; native audio is limited to the three specified sessions.

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 82,869 audio assets, 21,177,704,328 stored-audio bytes and 21,174,058,092 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: 12,377 original audio assets.
train: 56,609 original audio assets.
validation: 13,883 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-ami", 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: 12377 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)
`ami-activity`	frame_multilabel	segment_f1	1	train	validation	test
`ami-asr`	asr	wer	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: 81

Total file size:

21.5 GB

json dict	__key__ string	__url__ string
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 18912, "member": "ami-0004cb865402a0948235aabb04de2839.wav", "pcm_bytes": 37824, "sample_rate": 16000, "sample_width": 2, "sha256": "0ac6bfbd1817ea763e12d70db0c9423252950afb84fe45708fc01f799ae3...	ami-0004cb865402a0948235aabb04de2839	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 42336, "member": "ami-0005c3a81073037436ab796b7827b447.wav", "pcm_bytes": 84672, "sample_rate": 16000, "sample_width": 2, "sha256": "9012251bc12f889f95a1e074e52a7bb29b029a2ecd88ee8c5738761424d3...	ami-0005c3a81073037436ab796b7827b447	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 7072, "member": "ami-000696dfcd11d58280a6134e384bf6ea.wav", "pcm_bytes": 14144, "sample_rate": 16000, "sample_width": 2, "sha256": "3fd9199c0f51dbb897adc545fc862c4411eef0cfedfb675e08d53bf3e4d65...	ami-000696dfcd11d58280a6134e384bf6ea	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 500672, "member": "ami-000821ec770b23bd6405e5636f328a51.wav", "pcm_bytes": 1001344, "sample_rate": 16000, "sample_width": 2, "sha256": "0fabdd55104e70761484d69a714885e9676a509effc7744df3c8cc5cc...	ami-000821ec770b23bd6405e5636f328a51	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 49392, "member": "ami-0008f28e164fabbf520cc279a8cb3fb5.wav", "pcm_bytes": 98784, "sample_rate": 16000, "sample_width": 2, "sha256": "544b60bb3d3faaed844ae7cf442e7f36d0e2eaf506700c0c7c1b310fd7da...	ami-0008f28e164fabbf520cc279a8cb3fb5	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 90064, "member": "ami-00090051076a9d1bbd63ecf2f75908b3.wav", "pcm_bytes": 180128, "sample_rate": 16000, "sample_width": 2, "sha256": "5203fdcbb7f32c9233a1b9c41dbd696a234832981dbc3b80a2110f9cb46...	ami-00090051076a9d1bbd63ecf2f75908b3	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 20512, "member": "ami-00097599aabc47afb82587cc52a0f9ea.wav", "pcm_bytes": 41024, "sample_rate": 16000, "sample_width": 2, "sha256": "73c1fae189eecb194ee626da59143c8532f48fe7bfc4f6aea1ccf473dce7...	ami-00097599aabc47afb82587cc52a0f9ea	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 50448, "member": "ami-000af45f871902eb97b0e567d2363cf3.wav", "pcm_bytes": 100896, "sample_rate": 16000, "sample_width": 2, "sha256": "14ef2e4c9337c9f369d7f2a2553492e4dd9bcd207fdd946c968ee0e6c3b...	ami-000af45f871902eb97b0e567d2363cf3	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 16384, "member": "ami-000b811a9c05b44d5de7d077f74ebe9e.wav", "pcm_bytes": 32768, "sample_rate": 16000, "sample_width": 2, "sha256": "3935cc1fc8e23f71b5ed4b93f5a9c1817b38d5062871ca21b72e388006b6...	ami-000b811a9c05b44d5de7d077f74ebe9e	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 32192, "member": "ami-000d032bb635a93b30707b7eeb26e205.wav", "pcm_bytes": 64384, "sample_rate": 16000, "sample_width": 2, "sha256": "3a2c467bddaa550348fe133f8e670fcb795287bde9685be89bdd5bf3d135...	ami-000d032bb635a93b30707b7eeb26e205	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 129984, "member": "ami-000d533a7ceffd50dcd1613cc708940e.wav", "pcm_bytes": 259968, "sample_rate": 16000, "sample_width": 2, "sha256": "c6b1cf5f7635007f12c86ef8e0c505373d7772d29b3f10fcb876015bf4...	ami-000d533a7ceffd50dcd1613cc708940e	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 119296, "member": "ami-000e03fa9695ba7511fc161a58448e16.wav", "pcm_bytes": 238592, "sample_rate": 16000, "sample_width": 2, "sha256": "dfbae283a7b9096e341207bb225dfcca2b204ea671e5b77ac3c2f3104e...	ami-000e03fa9695ba7511fc161a58448e16	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 21744, "member": "ami-000ef08e400d29afa1472313e2274bc2.wav", "pcm_bytes": 43488, "sample_rate": 16000, "sample_width": 2, "sha256": "2ad275bd2c6bf5d01c4e45680dd44f0f1ec0e41364c78271bec5e3ecce6a...	ami-000ef08e400d29afa1472313e2274bc2	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 20512, "member": "ami-00141d80704c90903609b698c9ec5a97.wav", "pcm_bytes": 41024, "sample_rate": 16000, "sample_width": 2, "sha256": "7b03c2ae3d3f0bbbc44374b5566f241f31dac0da9f6fb1dbd08af32e8640...	ami-00141d80704c90903609b698c9ec5a97	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 19616, "member": "ami-0014ad664c73786c215f07c58e87c318.wav", "pcm_bytes": 39232, "sample_rate": 16000, "sample_width": 2, "sha256": "26a92a4c2f0bbd6ea086ffee73c05a1e77a7385dba76a34133a6ef68cf63...	ami-0014ad664c73786c215f07c58e87c318	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 34816, "member": "ami-0015601cba2a8ed36b168b325913996c.wav", "pcm_bytes": 69632, "sample_rate": 16000, "sample_width": 2, "sha256": "90db97c96c412a67e1283318873e5a5705b1c0a8a5093b109901a9ebf615...	ami-0015601cba2a8ed36b168b325913996c	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 30736, "member": "ami-0016a788a1afd5bfb5377362445e11b7.wav", "pcm_bytes": 61472, "sample_rate": 16000, "sample_width": 2, "sha256": "db3da6abb26b0e5ad8a1ac67b7b85d5b9966beb7c129778568f5d433aeb1...	ami-0016a788a1afd5bfb5377362445e11b7	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 69120, "member": "ami-00193b29c00ebf4fe4dfc1166019f6b1.wav", "pcm_bytes": 138240, "sample_rate": 16000, "sample_width": 2, "sha256": "b3ac0a11f4f644bb85819d247facee8ed561d98f696fe710f7975ae0850...	ami-00193b29c00ebf4fe4dfc1166019f6b1	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 19712, "member": "ami-0019d77916dca3a744ec4e44ccae419a.wav", "pcm_bytes": 39424, "sample_rate": 16000, "sample_width": 2, "sha256": "bf5f205426208bcbc2c01330332e0e80bc827c0e41315fe6b68b8f0416ea...	ami-0019d77916dca3a744ec4e44ccae419a	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 61344, "member": "ami-001b205c465aae3ea11afddaa047c222.wav", "pcm_bytes": 122688, "sample_rate": 16000, "sample_width": 2, "sha256": "78f45e25167f30325f6f4f4530acea68de108c6595670aa3efa001f626f...	ami-001b205c465aae3ea11afddaa047c222	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 297168, "member": "ami-001b8544929867c7b4abafba755d973e.wav", "pcm_bytes": 594336, "sample_rate": 16000, "sample_width": 2, "sha256": "a2dd910db730103895a31ab0566547fcc3c3551b9b94c2631f1ec13c4e...	ami-001b8544929867c7b4abafba755d973e	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 74496, "member": "ami-001c9446bc36432f133202606ea54bd8.wav", "pcm_bytes": 148992, "sample_rate": 16000, "sample_width": 2, "sha256": "f7a751e715bcb9af764dbbe543253d1df71a8bf3df294fb354cfd95a600...	ami-001c9446bc36432f133202606ea54bd8	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 57520, "member": "ami-001fe468f500ef066ee15431fa30123d.wav", "pcm_bytes": 115040, "sample_rate": 16000, "sample_width": 2, "sha256": "153b3399a58deb2ad9125f5273e170d7c99e8dff42147e95121051ed282...	ami-001fe468f500ef066ee15431fa30123d	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 121088, "member": "ami-00230c44cf1a4271c230d2f1b4582a2f.wav", "pcm_bytes": 242176, "sample_rate": 16000, "sample_width": 2, "sha256": "9d78c05deaef228dcf39cd4d77d2f1170441253c94f9ab7c07bc19ac6b...	ami-00230c44cf1a4271c230d2f1b4582a2f	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 15104, "member": "ami-002499af6735829ae24918fa0329dc5f.wav", "pcm_bytes": 30208, "sample_rate": 16000, "sample_width": 2, "sha256": "7ba346795e145f6a47f07ce07748764e088e14051b31d91ed806ecf2876d...	ami-002499af6735829ae24918fa0329dc5f	hf://datasets/ltuncay/chime-ami@7f613d121d5266466b3709a2cd027d8c908b5f9d/data/train/shard-000004.tar
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