Datasets:

ltuncay
/

chime-torgo

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 · TORGO

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: TORGO.
Pinned source release: author archives last modified 2015; observed 2026-10-28.
Selection: Select F01, M01, FC01 and MC01: one female and one male speaker in each dysarthric/control stratum. Keep all their native microphone views and companion prompt/phonetic/articulatory metadata. Group all views and sessions by speaker. Do not filter severity, noise, pauses, difficult articulation or overlap. Exact invalid file IDs and task-specific missing labels are recorded separately.
Original license/access conditions: Author terms: academic non-profit use with citation; no individually signed access agreement 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 9.577 GB full parent archives are explicit transient inputs; only the four named speaker subtrees may be extracted for this recipe. Full extraction of every speaker is not covered by the reservation. Acquisition permission is not a blanket right to republish TORGO under a new license.

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 8,216 audio assets, 818,470,312 stored-audio bytes and 818,108,808 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: 8,216 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-torgo", 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: 2197 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)
`torgo-dysarthria`	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: 72

Total file size:

851 MB

json dict	__key__ string	__url__ string
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 33680, "member": "torgo-0009b7c298a65e6c9309153b8b8d3db4.wav", "pcm_bytes": 67360, "sample_rate": 16000, "sample_width": 2, "sha256": "55f98930d576a82153358e9d11aab866605747f0a23c6a98184cb54b1a...	torgo-0009b7c298a65e6c9309153b8b8d3db4	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 75680, "member": "torgo-00224d56b0981f14084f5cfa8c0bbaba.wav", "pcm_bytes": 151360, "sample_rate": 16000, "sample_width": 2, "sha256": "802327bb8a8d55242e428688a9e2331d3ec24730d53596e27d4fd6b2a...	torgo-00224d56b0981f14084f5cfa8c0bbaba	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 42320, "member": "torgo-003a47e0aa55fc0787237ae021fbf90a.wav", "pcm_bytes": 84640, "sample_rate": 16000, "sample_width": 2, "sha256": "6e65f720034618214f10dd6976fdcb1cba8495ba90f37d803c5af135d3...	torgo-003a47e0aa55fc0787237ae021fbf90a	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 99760, "member": "torgo-003e5db4e5fe6531d36f0f0600cb14ac.wav", "pcm_bytes": 199520, "sample_rate": 16000, "sample_width": 2, "sha256": "636fb35671be4eff57c5625120a54030685fc788026040640f3647e8f...	torgo-003e5db4e5fe6531d36f0f0600cb14ac	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 30111, "member": "torgo-003ec405fc66adb2303ec314e37e418f.wav", "pcm_bytes": 60222, "sample_rate": 16000, "sample_width": 2, "sha256": "7a8a9da4823c9a9367b8fc0534603bb4d04ec5db635890318b43923c28...	torgo-003ec405fc66adb2303ec314e37e418f	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 31040, "member": "torgo-004ab4e1eda4b9f6396355372a836da1.wav", "pcm_bytes": 62080, "sample_rate": 16000, "sample_width": 2, "sha256": "110c556d2d9eece48c846be4ef87313e9a830b7396625bb5b68d21ec86...	torgo-004ab4e1eda4b9f6396355372a836da1	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 35440, "member": "torgo-00559c76ab0dd75ed031f6ced5d29adc.wav", "pcm_bytes": 70880, "sample_rate": 16000, "sample_width": 2, "sha256": "6d142ed337b40edde0dc2bc929d264a8f3045aed323584c98fa89b2a1c...	torgo-00559c76ab0dd75ed031f6ced5d29adc	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 39680, "member": "torgo-005f05774849b14ca86ffe0c91112dc6.wav", "pcm_bytes": 79360, "sample_rate": 16000, "sample_width": 2, "sha256": "6f74e1d7218787f7f31e1da44b9e4822d04c1a1e48e528b7f859cbd7d3...	torgo-005f05774849b14ca86ffe0c91112dc6	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 45840, "member": "torgo-00672e9b0178c2d2484874ea03fcc2d2.wav", "pcm_bytes": 91680, "sample_rate": 16000, "sample_width": 2, "sha256": "425068af6ed8b4a819f8fcfbf5cdda9aed6c657a1c12555f90b432b720...	torgo-00672e9b0178c2d2484874ea03fcc2d2	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 135520, "member": "torgo-0068f0715ffd9cc3f7af84a8c4f7562a.wav", "pcm_bytes": 271040, "sample_rate": 16000, "sample_width": 2, "sha256": "fd9c7246aff386a9c4f3defaf038586dcce276e30736328ea826ec77...	torgo-0068f0715ffd9cc3f7af84a8c4f7562a	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 29520, "member": "torgo-006b6c5caec266aa5b79b5cbe8ebe2e8.wav", "pcm_bytes": 59040, "sample_rate": 16000, "sample_width": 2, "sha256": "f064bf58cab6e7b6c8a61db9f2900b04ad296d45d938cd2e4d320884e1...	torgo-006b6c5caec266aa5b79b5cbe8ebe2e8	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 31440, "member": "torgo-00809323e23bda7843328afd560ac0f8.wav", "pcm_bytes": 62880, "sample_rate": 16000, "sample_width": 2, "sha256": "82a262af9d01202b44fddda2a5d1387c457bca581f845c28d2eb999345...	torgo-00809323e23bda7843328afd560ac0f8	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 64880, "member": "torgo-009894b5fe77957b3650e994bd20b4ed.wav", "pcm_bytes": 129760, "sample_rate": 16000, "sample_width": 2, "sha256": "9587cee6c623dedb50875f0bf952d8caabd859cb09c22916be344d016...	torgo-009894b5fe77957b3650e994bd20b4ed	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 28240, "member": "torgo-009f3d5bd0674715295049d4fe1a4859.wav", "pcm_bytes": 56480, "sample_rate": 16000, "sample_width": 2, "sha256": "00ca5f40dcc30dba4ee30b2f7dd30a630f06c45038a83fe5856135e435...	torgo-009f3d5bd0674715295049d4fe1a4859	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 27840, "member": "torgo-00a52f6b2206dbe18b22b96e657ad737.wav", "pcm_bytes": 55680, "sample_rate": 16000, "sample_width": 2, "sha256": "f278076e0039c96af1da2f9b7ea2a73f1ce0dbb6873e0351afe413fc84...	torgo-00a52f6b2206dbe18b22b96e657ad737	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 29040, "member": "torgo-00a80bb4b27b6da21859b4b5b49eedb2.wav", "pcm_bytes": 58080, "sample_rate": 16000, "sample_width": 2, "sha256": "cdda1dcb81266913333509de98e0db8671b317772b18031ccd1a660133...	torgo-00a80bb4b27b6da21859b4b5b49eedb2	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 125600, "member": "torgo-00ad720a3bd7eab2dda6021b1a32d4fd.wav", "pcm_bytes": 251200, "sample_rate": 16000, "sample_width": 2, "sha256": "e61a5ede90a8c8401a09717bce6bbeecf01145303a1d5f5994d6cb7e...	torgo-00ad720a3bd7eab2dda6021b1a32d4fd	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 26902, "member": "torgo-00b1845b36b7b998b1f05a733e98fb37.wav", "pcm_bytes": 53804, "sample_rate": 16000, "sample_width": 2, "sha256": "f0409f8b8bc82a675083c1ce05ad930ff900f8c98dc92ff420bc87ee72...	torgo-00b1845b36b7b998b1f05a733e98fb37	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 60319, "member": "torgo-00b9c4342c2c26a7ae7ebec5b634e023.wav", "pcm_bytes": 120638, "sample_rate": 16000, "sample_width": 2, "sha256": "12ba0371e3262933351481de620adf16da7c4a4c2bd14f0764f3b0314...	torgo-00b9c4342c2c26a7ae7ebec5b634e023	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 111330, "member": "torgo-00ba2f8e12fa445ec205d34be3a0796d.wav", "pcm_bytes": 222660, "sample_rate": 16000, "sample_width": 2, "sha256": "a126e5af8f75bb10f6f87d7ace2c4efae28b5bef27e5c58bcbfbfb9a...	torgo-00ba2f8e12fa445ec205d34be3a0796d	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 55920, "member": "torgo-00bfa2059d607e6271f8b29ecd3376ff.wav", "pcm_bytes": 111840, "sample_rate": 16000, "sample_width": 2, "sha256": "c68c023e801c34b2d7b8bd0bc3de0266d20e0721a72509da70d7e13ae...	torgo-00bfa2059d607e6271f8b29ecd3376ff	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 29360, "member": "torgo-00c1b41b8b3f15fe1da23458200371f4.wav", "pcm_bytes": 58720, "sample_rate": 16000, "sample_width": 2, "sha256": "e3c778b2e4e8b882d056b599d516c045c36a4306f424aa71fed1758dda...	torgo-00c1b41b8b3f15fe1da23458200371f4	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 28720, "member": "torgo-00ccb72e0b311cc1df0a042d191ffd87.wav", "pcm_bytes": 57440, "sample_rate": 16000, "sample_width": 2, "sha256": "c3e6a9a47ca2ff4e655f0e777297b9e54565f23b4a26a68c7499e7e04f...	torgo-00ccb72e0b311cc1df0a042d191ffd87	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 1, "format": "WAV", "frames": 40081, "member": "torgo-00d6dcf8ea19ad7c7d74406606e5d58e.wav", "pcm_bytes": 80162, "sample_rate": 16000, "sample_width": 2, "sha256": "c1cddb4399d2cd9229d5f1a7ca50caf46041d67d8a6730adba18191b50...	torgo-00d6dcf8ea19ad7c7d74406606e5d58e	hf://datasets/ltuncay/chime-torgo@cb070bc207fc37ae7540724e8bebc709dc93a1c3/data/unspecified/shard-000000.tar
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