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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 datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
wav audio | json dict | __key__ string | __url__ string |
|---|---|---|---|
{
"audio": {
"channel_order": "source_native",
"channels": 1,
"format": "WAV",
"frames": 42175,
"member": "crema-d-00069f73dcb91544c91b02d90c23b899.wav",
"pcm_bytes": 84350,
"sample_rate": 16000,
"sample_width": 2,
"sha256": "a5c8835e306d5ab000c8f8094d17ceb0e021baaa2aeb5396fe39f56c... | crema-d-00069f73dcb91544c91b02d90c23b899 | hf://datasets/ltuncay/chime-crema-d@465c0cacf0af7f3fe103f79265c1a2601785c73c/data/unspecified/shard-000000.tar | |
{
"audio": {
"channel_order": "source_native",
"channels": 1,
"format": "WAV",
"frames": 40574,
"member": "crema-d-00159696d44093072078f85b4167cff3.wav",
"pcm_bytes": 81148,
"sample_rate": 16000,
"sample_width": 2,
"sha256": "180308bfaae8d66ddd472798de8f72a42e63eeee223f5c90412fa863... | crema-d-00159696d44093072078f85b4167cff3 | hf://datasets/ltuncay/chime-crema-d@465c0cacf0af7f3fe103f79265c1a2601785c73c/data/unspecified/shard-000000.tar | |
{
"audio": {
"channel_order": "source_native",
"channels": 1,
"format": "WAV",
"frames": 26159,
"member": "crema-d-001a79d6c2c2757ce2997477db259f31.wav",
"pcm_bytes": 52318,
"sample_rate": 16000,
"sample_width": 2,
"sha256": "b3a38480d84b5cfbf721a4d064b3bcc9e53f484cee44bfc1ed19e099... | crema-d-001a79d6c2c2757ce2997477db259f31 | hf://datasets/ltuncay/chime-crema-d@465c0cacf0af7f3fe103f79265c1a2601785c73c/data/unspecified/shard-000000.tar | |
{
"audio": {
"channel_order": "source_native",
"channels": 1,
"format": "WAV",
"frames": 54988,
"member": "crema-d-001c3b87245c85311a72e9f573a12162.wav",
"pcm_bytes": 109976,
"sample_rate": 16000,
"sample_width": 2,
"sha256": "3202f492fb77d36bb86228047323f5fdcd3be1c513d0ae5c3a00e5e... | crema-d-001c3b87245c85311a72e9f573a12162 | hf://datasets/ltuncay/chime-crema-d@465c0cacf0af7f3fe103f79265c1a2601785c73c/data/unspecified/shard-000000.tar | |
{
"audio": {
"channel_order": "source_native",
"channels": 1,
"format": "WAV",
"frames": 41642,
"member": "crema-d-0028791a51b4f54053f4febfe000f283.wav",
"pcm_bytes": 83284,
"sample_rate": 16000,
"sample_width": 2,
"sha256": "3d76d83acd710ce6dba1e7122760d171e7faa600983ab4f573cd97f6... | crema-d-0028791a51b4f54053f4febfe000f283 | hf://datasets/ltuncay/chime-crema-d@465c0cacf0af7f3fe103f79265c1a2601785c73c/data/unspecified/shard-000000.tar | |
{
"audio": {
"channel_order": "source_native",
"channels": 1,
"format": "WAV",
"frames": 47514,
"member": "crema-d-0036415221e8b7ff2ed4e84bed102180.wav",
"pcm_bytes": 95028,
"sample_rate": 16000,
"sample_width": 2,
"sha256": "8050aed4396b14e0e3e095e666bf33db82d2e75730ff88fb06ad5add... | crema-d-0036415221e8b7ff2ed4e84bed102180 | hf://datasets/ltuncay/chime-crema-d@465c0cacf0af7f3fe103f79265c1a2601785c73c/data/unspecified/shard-000000.tar | |
{
"audio": {
"channel_order": "source_native",
"channels": 1,
"format": "WAV",
"frames": 41642,
"member": "crema-d-0043c184e373c4d89992c12b6656732e.wav",
"pcm_bytes": 83284,
"sample_rate": 16000,
"sample_width": 2,
"sha256": "cce890fa09fd6282ce937faaf1bdd758aff1303f94343ab22422b649... | crema-d-0043c184e373c4d89992c12b6656732e | hf://datasets/ltuncay/chime-crema-d@465c0cacf0af7f3fe103f79265c1a2601785c73c/data/unspecified/shard-000000.tar | |
{
"audio": {
"channel_order": "source_native",
"channels": 1,
"format": "WAV",
"frames": 41108,
"member": "crema-d-005506fe09587e8fee69f69f55595360.wav",
"pcm_bytes": 82216,
"sample_rate": 16000,
"sample_width": 2,
"sha256": "00c3c03007662a892f7a4fdcf32cc6b481f31811e9f878e4172a00fc... | crema-d-005506fe09587e8fee69f69f55595360 | hf://datasets/ltuncay/chime-crema-d@465c0cacf0af7f3fe103f79265c1a2601785c73c/data/unspecified/shard-000000.tar | |
{
"audio": {
"channel_order": "source_native",
"channels": 1,
"format": "WAV",
"frames": 35235,
"member": "crema-d-005d3530887931ede404c54b25183fb5.wav",
"pcm_bytes": 70470,
"sample_rate": 16000,
"sample_width": 2,
"sha256": "2f69d39a5581fb0fddbaac4b948922ad2e3e80543d57b38f18352fb7... | crema-d-005d3530887931ede404c54b25183fb5 | hf://datasets/ltuncay/chime-crema-d@465c0cacf0af7f3fe103f79265c1a2601785c73c/data/unspecified/shard-000000.tar | |
{
"audio": {
"channel_order": "source_native",
"channels": 1,
"format": "WAV",
"frames": 36837,
"member": "crema-d-006151614a1f7d717bcb1dec8e52317d.wav",
"pcm_bytes": 73674,
"sample_rate": 16000,
"sample_width": 2,
"sha256": "d1695f49b56a73a071ce4d0b65217a057d7147b8e02a112fb24f704d... | crema-d-006151614a1f7d717bcb1dec8e52317d | hf://datasets/ltuncay/chime-crema-d@465c0cacf0af7f3fe103f79265c1a2601785c73c/data/unspecified/shard-000000.tar | |
{
"audio": {
"channel_order": "source_native",
"channels": 1,
"format": "WAV",
"frames": 46980,
"member": "crema-d-0074de2209f5315ce64232994a9a4223.wav",
"pcm_bytes": 93960,
"sample_rate": 16000,
"sample_width": 2,
"sha256": "70f6478ec6e4bbe3dba35069ae7746f528b681eedb310abc37ffbe5a... | crema-d-0074de2209f5315ce64232994a9a4223 | hf://datasets/ltuncay/chime-crema-d@465c0cacf0af7f3fe103f79265c1a2601785c73c/data/unspecified/shard-000000.tar | |
{
"audio": {
"channel_order": "source_native",
"channels": 1,
"format": "WAV",
"frames": 28829,
"member": "crema-d-007713048952564cdc9eb808432075b9.wav",
"pcm_bytes": 57658,
"sample_rate": 16000,
"sample_width": 2,
"sha256": "ff4bdcee101111275d59d7e817314945101057b67da76cb031fa001f... | crema-d-007713048952564cdc9eb808432075b9 | hf://datasets/ltuncay/chime-crema-d@465c0cacf0af7f3fe103f79265c1a2601785c73c/data/unspecified/shard-000000.tar | |
{
"audio": {
"channel_order": "source_native",
"channels": 1,
"format": "WAV",
"frames": 49650,
"member": "crema-d-007e844772977821eacbbe75cb1d064f.wav",
"pcm_bytes": 99300,
"sample_rate": 16000,
"sample_width": 2,
"sha256": "aa4558d9d0427c1494779ac89639bb9f18d6e38c9ca19df307af67f5... | crema-d-007e844772977821eacbbe75cb1d064f | hf://datasets/ltuncay/chime-crema-d@465c0cacf0af7f3fe103f79265c1a2601785c73c/data/unspecified/shard-000000.tar | |
{
"audio": {
"channel_order": "source_native",
"channels": 1,
"format": "WAV",
"frames": 38972,
"member": "crema-d-009a893c69cc0cebaace4a195f0adf52.wav",
"pcm_bytes": 77944,
"sample_rate": 16000,
"sample_width": 2,
"sha256": "b78b05901923c0fb3c3e06c66988d6b9da6f97fc407cea3afb1a3543... | crema-d-009a893c69cc0cebaace4a195f0adf52 | hf://datasets/ltuncay/chime-crema-d@465c0cacf0af7f3fe103f79265c1a2601785c73c/data/unspecified/shard-000000.tar | |
{
"audio": {
"channel_order": "source_native",
"channels": 1,
"format": "WAV",
"frames": 43777,
"member": "crema-d-00a217e3d1b0d6baccccfdcda9f563f3.wav",
"pcm_bytes": 87554,
"sample_rate": 16000,
"sample_width": 2,
"sha256": "9314b9a2989b35647ba1a6fa606fd58d721736f11e7b0462c672bef4... | crema-d-00a217e3d1b0d6baccccfdcda9f563f3 | hf://datasets/ltuncay/chime-crema-d@465c0cacf0af7f3fe103f79265c1a2601785c73c/data/unspecified/shard-000000.tar |
CHIME · CREMA-D
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: CREMA-D.
- Pinned source release:
1658cd342dff90010aa843eaeebd53610a08b1dc. - Selection: All 7,442 author-native AudioWAV recordings and source annotation CSVs, preserving actor IDs, sentence, intended emotion/intensity and available listener ratings. Keep all 91 actors; missing targets are task-specific exclusions, not a reason to discard difficult or disputed emotion examples.
- Original license/access conditions: ODbL-1.0 database; DbCL-1.0 individual contents Public HTTPS download without registration or signed agreement. Preserve source attribution and license restrictions.
- Source notes: The authoritative GitHub archive contains Git LFS pointers only. All 7,442 original WAV sizes and SHA256s were resolved from pointer metadata at this immutable commit; total native audio is 605,899,936 bytes. Optional author tracking form is not a signed access agreement. Git metadata CSV hashes use Git blob SHA1, not raw SHA1.
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 7,442 audio assets, 605,899,936 stored-audio bytes and 605,572,488 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: 7,442 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-crema-d", 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: 2044 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) |
|---|---|---|---|---|---|---|
crema-d-emotion |
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.
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