Datasets:

ltuncay
/

chime-song-describer

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 · Song Describer

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: Song Describer.
Pinned source release: 1.0.0.
Selection: All706native distributed2-minuteMP3 previews and1106original captions retained; valid-caption view uses746captions/547tracks according to actualCSV.
Original license/access conditions: Captions CC-BY-SA-4.0; individual audio CC licenses in audio_licenses.txt Public anonymous download; no access request or click-through agreement. Retain original per-recording terms and attribution.
Source notes: ZIP directory confirms706MP3files and3,340,819,044nativeaudio bytes. Upstream README says546validtracks but CSV has547; prefer actualCSV and record discrepancy. Full source metadata duration describes fullsong, while archive is .2min.mp3 previews; no further cropping.

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 706 audio assets, 29,523,292,496 stored-audio bytes and 29,523,230,368 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: 706 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-song-describer", 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: 706 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)
`song-describer-caption-choice`	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: 66

Total file size:

29.5 GB

json dict	__key__ string	__url__ string
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{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-006c5a9569312a462ff69b4452084165.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "0ad90f0c0cf4a9f269dd9cc1f862bdbabeb1106bda33...	song-describer-006c5a9569312a462ff69b4452084165	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-0118fdcf30ca136876904c5873c7cb43.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "723eaf3395d1443f28b7fdaf841354bae108b032b7a1...	song-describer-0118fdcf30ca136876904c5873c7cb43	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-0164a9367d86015dca0160967728b34e.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "d6e5d0de504e49bc987465644fcb9a44bef14012e209...	song-describer-0164a9367d86015dca0160967728b34e	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-017107160eff6ed18bc10e48c037dd87.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "8852876fc19451be52a5aa9ff7d1a501ecf2fede04a0...	song-describer-017107160eff6ed18bc10e48c037dd87	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-0216a071cf6252ae3c62e0d0d97fe5d8.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "c6cc30f9cd45417c6e7a28436a7229ef47074942fbfb...	song-describer-0216a071cf6252ae3c62e0d0d97fe5d8	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-02468aecad41a8d79eca85ef3588e457.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "bb2c37dc0ed549fec4f9f10c24247cc6cabb39378357...	song-describer-02468aecad41a8d79eca85ef3588e457	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-031ff4f16a47ba925820c830c6479498.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "342d97ce4afddcf68d9445b01717d14e2dd692cf777d...	song-describer-031ff4f16a47ba925820c830c6479498	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-0373062963d4c514c4f1ad8849a8499a.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "9b87a580dedf52ffca05880fc5fa7f1bfefc344ba030...	song-describer-0373062963d4c514c4f1ad8849a8499a	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-03eb16dd3eb0679534c42de6efe79f51.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "7a36d49801a63656f7ac4a4c0db06e11e6c743e58341...	song-describer-03eb16dd3eb0679534c42de6efe79f51	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5759471, "member": "song-describer-041dbd168974d50da3c4f2469953955e.wav", "pcm_bytes": 46075768, "sample_rate": 48000, "sample_width": 4, "sha256": "3823e501a20e3ccb43057b18a679d25747abb34087fa...	song-describer-041dbd168974d50da3c4f2469953955e	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-0477f5819c7bcd6e27d1e4011b41595b.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "393a22dedb5ee1c498e09db961fa5df778b591c488e7...	song-describer-0477f5819c7bcd6e27d1e4011b41595b	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-0553af57e7ea5ba5dbd4b42d6e028add.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "01fb4f70730c0d2737d771ccf771cfb697b886c8787c...	song-describer-0553af57e7ea5ba5dbd4b42d6e028add	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-05ba7774fa400b799f05dc0b5ac96beb.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "c23948d1d8b82c1aae8d6a2b993921388b13c568162b...	song-describer-05ba7774fa400b799f05dc0b5ac96beb	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-074cd776761af58578bba64e1b0290f5.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "b6c00016f27149b64665a0759aed503d793289d48849...	song-describer-074cd776761af58578bba64e1b0290f5	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-0792311a72f8b09ff22223ea531cb510.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "36beaaa53b898da787dbcbc49d27073dfcbf7d8c4513...	song-describer-0792311a72f8b09ff22223ea531cb510	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-0830f5a9b19bfa886be3716e01328876.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "6cd1de6c7422346679ab1438acb69c1d4715f34d7d25...	song-describer-0830f5a9b19bfa886be3716e01328876	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-08760a859040ef32cfbdb4b7926771c6.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "13d17a6bfec05b105f68d68268119d1408d712886458...	song-describer-08760a859040ef32cfbdb4b7926771c6	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-0936179d9dbf3903845283b5e8714a25.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "35643c37730129b8f65b6d9bf61e37f63739a1110cf2...	song-describer-0936179d9dbf3903845283b5e8714a25	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-09c0b4315dbcb2047092f9159724a009.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "80bb139187a2f31e55c5e32a039785b5b3e45640eb15...	song-describer-09c0b4315dbcb2047092f9159724a009	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-09eec3b283c4a9f1ea01212428a65a6b.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "abedec24c18c40868fee2d73e4c66dba5974c9a09197...	song-describer-09eec3b283c4a9f1ea01212428a65a6b	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5759471, "member": "song-describer-09efdf0c53247481223e8a1b0dc3e59f.wav", "pcm_bytes": 46075768, "sample_rate": 48000, "sample_width": 4, "sha256": "344e2d27082a59ebce6f37f971d159a1da3427417dae...	song-describer-09efdf0c53247481223e8a1b0dc3e59f	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-0a01e112ef6e4c17d3d638deef563746.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "a6e6ff940fe546f2f1039c60354d1cd25f8169963d70...	song-describer-0a01e112ef6e4c17d3d638deef563746	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000000.tar
{ "audio": { "channel_order": "source_native", "channels": 2, "format": "WAV", "frames": 5291759, "member": "song-describer-0a268d1f502508c7bf3b9211e6d76964.wav", "pcm_bytes": 42334072, "sample_rate": 44100, "sample_width": 4, "sha256": "7203214caf6d1820f4750cf5cccad0bf8c713c653f8f...	song-describer-0a268d1f502508c7bf3b9211e6d76964	hf://datasets/ltuncay/chime-song-describer@be9be1f343911876e7d1f935d0d6a33cf011c9c7/data/test/shard-000001.tar
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