The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
~~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
cls = get_filesystem_class(protocol)
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: memory
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/split_names.py", line 71, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
callcentre-noise48k
A call-centre-weighted background-noise corpus at 48 kHz mono, for noise augmentation in speech-enhancement and speech-restoration training.
20,946 chunks · ≈28.7 h · 9.92 GB · 48 kHz mono WAV (PCM_16)
This is a derived corpus: five public noise/event datasets, decoded to 48 kHz mono, cut into ≤10 s chunks, and re-labelled into one flat category scheme chosen for telephony-domain relevance. It contains no speech content — see Deliberate omissions.
Files
| file | size |
|---|---|
archive/callcentre-noise48k.tar.part-aa |
4.29 GB |
archive/callcentre-noise48k.tar.part-ab |
4.29 GB |
archive/callcentre-noise48k.tar.part-ac |
1.33 GB |
noise_filelist.tsv |
1.1 MB |
The tar is split only to stay under per-file limits — the three parts are a plain byte split of one uncompressed tar, so they must be concatenated in order:
huggingface-cli download Scicom-intl/callcentre-noise48k --repo-type dataset --local-dir .
mkdir -p /data/noise48k
cat archive/callcentre-noise48k.tar.part-a? | tar xf - -C /data/noise48k
# the TSV stores the absolute paths from the machine that built it — repoint them
sed -i 's#/root/noise48k#/data/noise48k#g' /data/noise48k/noise_filelist.tsv
noise_filelist.tsv is also included inside the tar, so the extracted tree is
self-contained. The Dataset Viewer is disabled: this is a tar of WAVs, not parquet.
noise_filelist.tsv
Tab-separated, no header, 20,946 rows, three columns:
/root/noise48k/musan/noise-free-sound-0010_000.wav office musan
path category source
Consumers pick a chunk by weighting category for the acoustic scene they want to
simulate; source is kept so a subset can be filtered by upstream licence (see
Licensing — this matters).
Composition
Chunks per category × source:
| category | musan | demand | esc50 | fsd50k | audioset | total |
|---|---|---|---|---|---|---|
street |
– | 2880 | – | 1564 | 600 | 5044 |
office |
2515 | 1440 | 120 | 36 | 129 | 4240 |
ambient |
– | – | 1520 | 1589 | 267 | 3376 |
cafe |
– | 1440 | 40 | 263 | 19 | 1762 |
car |
– | 1440 | 160 | – | – | 1600 |
domestic |
– | 1440 | – | – | – | 1440 |
hold_music |
996 | – | – | 99 | 105 | 1200 |
babble |
– | – | – | 855 | 52 | 907 |
keyboard |
– | – | 80 | 690 | 98 | 868 |
phone |
– | – | – | 191 | 79 | 270 |
hvac |
– | – | 80 | 103 | 56 | 239 |
| total | 3511 | 8640 | 2000 | 5390 | 1405 | 20946 |
office and street dominate by design — they are the two scenes a call-centre
caller is most often in. MUSAN's music partition was downsampled to 996 chunks
so hold-music would not swamp the corpus (it is by far the largest raw partition).
FSD50K and AudioSet were class-filtered by label keyword down to
call-centre-relevant events; unmatched labels were dropped rather than binned as
"other", which is why their category spreads are narrow.
Processing
Every chunk went through the same path:
- decode → 48 kHz mono float32 (
soxrresampling) - cut into fixed 10 s frames, discarding any tail shorter than 2 s
- drop near-silent frames (peak < 1e-4)
- peak-limit to 0.9 only if the frame clipped (peak > 1) — otherwise the original level is preserved, so relative loudness across a source survives and consumers can set their own SNR
- write WAV PCM_16
Chunks are therefore variable length between 2 s and 10 s, averaging ≈4.9 s —
not a uniform 10 s. Compute duration from the files, not from rows × 10 s
(that would overstate the corpus at 58 h). The ≈28.7 h figure above is derived
from the payload size: 9.91 GB at 48 kHz mono PCM_16 is 96 kB/s.
Deliberate omissions
- No speech, and no multi-speaker babble beds. The
babblecategory here is crowd/chatter ambience (from FSD50K and AudioSet crowd labels) — a room tone, not intelligible talkers. If you need overlapping-speaker interference, mix it yourself from a speech corpus. - No RIRs. These chunks are dry recordings; reverberation is not baked in.
- Nothing scraped. No YouTube downloading was performed; the AudioSet portion came from an existing Hugging Face audio mirror.
Licensing
Read this before any commercial use. This corpus is a derivative of five
upstream datasets whose licences differ, and it is not uniformly permissive.
The repo licence is therefore other; the effective terms are the union of:
| source | chunks | upstream licence | commercial use |
|---|---|---|---|
| MUSAN (OpenSLR 17) | 3511 | CC BY 4.0 | yes, with attribution |
| DEMAND (Zenodo) | 8640 | CC BY-SA 3.0 | yes, share-alike |
| ESC-50 | 2000 | CC BY-NC 3.0 | no — non-commercial only |
| FSD50K (Zenodo) | 5390 | CC BY 4.0 overall, but per-clip CC0 / CC BY / CC BY-NC / Sampling+ | mixed — check per clip |
| AudioSet (via HF mirror) | 1405 | labels CC BY 4.0; the audio originates from YouTube and is not covered by that licence | unclear — treat as restricted |
Practical consequences:
- As distributed, treat the whole corpus as research / non-commercial.
- For a commercially clean subset, filter
noise_filelist.tsvon thesourcecolumn tomusananddemandonly (12,151 of 20,946 chunks) and observe CC BY-SA 3.0's share-alike obligation for the DEMAND portion. - The
sourcecolumn exists precisely so this filtering is a one-liner:
awk -F'\t' '$3=="musan" || $3=="demand"' noise_filelist.tsv > noise_commercial.tsv
Please cite the upstream datasets, not just this repo — MUSAN, DEMAND, ESC-50, FSD50K and AudioSet each have a paper.
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