Datasets:

ltuncay
/

chime-mmau

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 · MMAU

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: MMAU.
Pinned source release: ccd9696c0111ea7060827598f310558df0b71b0a.
Selection: Author ungated test-mini release supplies 1,000 public gold answers; the full hidden-answer test set is not a supervised benchmark.
Original license/access conditions: CC-BY-NC-4.0 (author pinned dataset card), with underlying source provenance retained. Public ungated author repository, immutable commit and per-object source checksum.
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.

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 1,000 audio assets, 2,279,613,004 stored-audio bytes and 2,279,565,958 decoded-PCM-equivalent bytes. Native sample rate, duration and channel count are retained. No loudness normalization, silence trimming, resampling or downmixing is applied.

Every audio payload uses WAV (including WAVEX/RF64). Existing WAV bytes are retained. Other source codecs are decoded once into sample-exact integer or floating-point WAV; floating-point WAV avoids quantizing MP3/Vorbis decoder output. Every converted frame is read back and its float64 sample digest compared with the original decoder. The source encoding/hash and decoder version remain in each converted record's container_standardization metadata. Sampling rate, frame count, channel count, annotations and sample keys are unchanged. 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. Container standardization preserves decoded samples exactly; source encoded bytes differ for converted assets.

Source splits

test: 1,000 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-mmau", 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: 1000 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)
`mmau-qa`	qa	exact_match	1	—	—	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:

2.29 GB

json dict	__key__ string	__url__ string
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 112001, "member": "mmau-000c433f6f0bab40188d67f1c1ae6920.wav", "pcm_bytes": 224002, "sample_rate": 16000, "sample_width": 2, "sha256": "b319d0404f5adda08b37f6b83d7769982d68d4f11d3b325bbdf394008...	mmau-000c433f6f0bab40188d67f1c1ae6920	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 191232, "member": "mmau-009c2de582c0596a36ef53a1a1191b72.wav", "pcm_bytes": 382464, "sample_rate": 24000, "sample_width": 2, "sha256": "2d0dc30ff318da8b0c4b3abcf849c9428b83d6c0c1e920eba5d7eb5f1...	mmau-009c2de582c0596a36ef53a1a1191b72	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 320000, "member": "mmau-00a0e451badf30ce613b09eeaf306c81.wav", "pcm_bytes": 640000, "sample_rate": 32000, "sample_width": 2, "sha256": "9b3784dd3b9c07465f5b88eb69dab086d6fd3c86034ce6286aac25840...	mmau-00a0e451badf30ce613b09eeaf306c81	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 112641, "member": "mmau-00ff151b08847a653a36e87cadeaf24b.wav", "pcm_bytes": 225282, "sample_rate": 16000, "sample_width": 2, "sha256": "26135dd52c26009a757538cf15e915f4122de0d24bcb8a4a356417220...	mmau-00ff151b08847a653a36e87cadeaf24b	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 160000, "member": "mmau-013d3244f780ebc01fbafb328666ce59.wav", "pcm_bytes": 320000, "sample_rate": 16000, "sample_width": 2, "sha256": "215dbe0702956b9cd1db6946e740ba28ba761d7f5f1aa16727847d718...	mmau-013d3244f780ebc01fbafb328666ce59	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 6, "format": "WAV", "frames": 1400924, "member": "mmau-01d0e9bdd4eb537ef4b8176ad9c04e28.wav", "pcm_bytes": 16811088, "sample_rate": 48000, "sample_width": 2, "sha256": "416680119b68a75d462d718c0c916200552ec63a82a579d7ff3539...	mmau-01d0e9bdd4eb537ef4b8176ad9c04e28	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 320000, "member": "mmau-02256ffdd4b096c987f21c52518c1873.wav", "pcm_bytes": 640000, "sample_rate": 32000, "sample_width": 2, "sha256": "0a5be86c3dda4106f56fc7a26410136e73f30405c55ab4dd439aa482f...	mmau-02256ffdd4b096c987f21c52518c1873	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 1192535, "member": "mmau-023739f72c2a2ccaaa33ba9200c462cd.wav", "pcm_bytes": 2385070, "sample_rate": 44100, "sample_width": 2, "sha256": "0cb70571359b36a52e5b0aa77e40e68737b4c4e9726a529a03c6398...	mmau-023739f72c2a2ccaaa33ba9200c462cd	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 160000, "member": "mmau-026c6fac7dc9333b611d95d10a4f5ceb.wav", "pcm_bytes": 320000, "sample_rate": 16000, "sample_width": 2, "sha256": "0764e73976df5b48c9e97593ed8ddb35ee9491371525827ab508f87eb...	mmau-026c6fac7dc9333b611d95d10a4f5ceb	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 160000, "member": "mmau-02c307147324d8df7e943974a420c99d.wav", "pcm_bytes": 320000, "sample_rate": 16000, "sample_width": 2, "sha256": "1fe7c380ab2418059f0b0097ad5f9d45b62750f70abed273945509124...	mmau-02c307147324d8df7e943974a420c99d	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 292272, "member": "mmau-0374c600614e6e303a352719d32d0d92.wav", "pcm_bytes": 584544, "sample_rate": 32000, "sample_width": 2, "sha256": "c4c7329036d125078831af8ed71fa0107dbb210ae6537df11ae88ecd9...	mmau-0374c600614e6e303a352719d32d0d92	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 315952, "member": "mmau-03b21298c6ef915edc521d1556570e7a.wav", "pcm_bytes": 631904, "sample_rate": 32000, "sample_width": 2, "sha256": "1871a56c1454cf2c71ace103cfe7f83b075f743ac9a8ddf11fe9aed31...	mmau-03b21298c6ef915edc521d1556570e7a	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 6, "format": "WAV", "frames": 1354472, "member": "mmau-03cb0c734ee61ba94051709042165cd6.wav", "pcm_bytes": 16253664, "sample_rate": 48000, "sample_width": 2, "sha256": "90a025b801e34f575239e4fee268b68840045f1c5b500ae1cdc74a...	mmau-03cb0c734ee61ba94051709042165cd6	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 320020, "member": "mmau-04ab87c6698d41fb3b99b6e525450132.wav", "pcm_bytes": 640040, "sample_rate": 32000, "sample_width": 2, "sha256": "7ac20907065d2094d4da57e06f5aa79a11a1117ff23955044e3120807...	mmau-04ab87c6698d41fb3b99b6e525450132	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 426400, "member": "mmau-050073d02cd0e79a51ad56793967b7f0.wav", "pcm_bytes": 1705600, "sample_rate": 16000, "sample_width": 2, "sha256": "87407264c590eed41be93185203dbbcafc688047513e16e7fa891970...	mmau-050073d02cd0e79a51ad56793967b7f0	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 297271, "member": "mmau-05048a4db85e95f42ece6b60c5328c00.wav", "pcm_bytes": 594542, "sample_rate": 32000, "sample_width": 2, "sha256": "30a3cffd83d124f0782e5537d8601fc36820c2284a7569aa17ca705bf...	mmau-05048a4db85e95f42ece6b60c5328c00	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 1157120, "member": "mmau-0575b0b028cb5a7eaec0f84b00ed60e9.wav", "pcm_bytes": 4628480, "sample_rate": 44100, "sample_width": 2, "sha256": "6d5bdc997adf18bfb896ac732cb83e574bda29872c9e0af1c1e697a...	mmau-0575b0b028cb5a7eaec0f84b00ed60e9	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 320000, "member": "mmau-0579bb2aa7f054b889a410f7934a24b6.wav", "pcm_bytes": 640000, "sample_rate": 32000, "sample_width": 2, "sha256": "d009244fdfb84431a94cd9bc2ac8d6116ba731623b89fb3724b2444c8...	mmau-0579bb2aa7f054b889a410f7934a24b6	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 160000, "member": "mmau-05e84461e88f7d647101344f76c17030.wav", "pcm_bytes": 320000, "sample_rate": 16000, "sample_width": 2, "sha256": "c12539607e235125526aef365e551a45b8b5212f27f9988b6f9617a20...	mmau-05e84461e88f7d647101344f76c17030	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 160000, "member": "mmau-065867abd74183d7f48a2291d132acf1.wav", "pcm_bytes": 640000, "sample_rate": 16000, "sample_width": 4, "sha256": "7b99ec22c84386c73e40923142c981c7a1e1bd3b554f3790f6f1ec8ef...	mmau-065867abd74183d7f48a2291d132acf1	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 160000, "member": "mmau-0694a5b6c9d1774e484b86deebd53a06.wav", "pcm_bytes": 320000, "sample_rate": 16000, "sample_width": 2, "sha256": "d275201b88fcd0e0e03edd0ed87cc028edfa33e3d858e09c7c679c333...	mmau-0694a5b6c9d1774e484b86deebd53a06	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 1324800, "member": "mmau-069c8538ab99db954c369acb5411526e.wav", "pcm_bytes": 10598400, "sample_rate": 44100, "sample_width": 4, "sha256": "7b1baf83cac9b654c3e3ae7511272ba4b4ec24f6c614c169b73188...	mmau-069c8538ab99db954c369acb5411526e	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 440928, "member": "mmau-06a679ddb845df34f445a21646882eba.wav", "pcm_bytes": 1763712, "sample_rate": 16000, "sample_width": 2, "sha256": "eae63c957c58b7bb5f5be17f388356b05328eb8d139421cc4da45a10...	mmau-06a679ddb845df34f445a21646882eba	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 235521, "member": "mmau-06b7b9ba11f105bedc00498ec505023d.wav", "pcm_bytes": 471042, "sample_rate": 16000, "sample_width": 2, "sha256": "6ab7d388f8b29a374a92b9c2642cf079f9cbf8af34a6284da571de1fd...	mmau-06b7b9ba11f105bedc00498ec505023d	hf://datasets/ltuncay/chime-mmau@44eea572bbfb6f6be6ea432259e96b7f1a6f3a56/data/test/shard-000000.tar
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