Datasets:

huper29
/

huper-clean100-proxyphones

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 80, in _split_generators
                  first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 33, in _get_pipeline_from_tar
                  for filename, f in tar_iterator:
                                     ^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/track.py", line 49, in __iter__
                  for x in self.generator(*self.args):
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 1380, in _iter_from_urlpath
                  yield from cls._iter_tar(f)
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 1331, in _iter_tar
                  stream = tarfile.open(fileobj=f, mode="r|*")
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/tarfile.py", line 1886, in open
                  t = cls(name, filemode, stream, **kwargs)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/tarfile.py", line 1762, in __init__
                  self.firstmember = self.next()
                                     ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/tarfile.py", line 2750, in next
                  raise ReadError(str(e)) from None
              tarfile.ReadError: invalid header
              
              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 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                         ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/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.

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huper-clean100-proxyphones

LibriSpeech train-clean-100 audio paired with HuPER-style proxy ARPAbet phone labels (machine-generated / proxy, not human verified).
This corresponds to the 100h train-clean-100 split (28,539 utterances).
Note: HuggingFace Dataset Viewer is not supported because the data is provided as tar+zstd shards. Follow the instructions below to download and extract locally.

What’s inside

The data is stored as 5 shards under blobs/:

blobs/train.tar.zst.part-000 … blobs/train.tar.zst.part-004

After extraction, you will obtain a train/ directory (same structure as the original repo on disk), containing:

audio files (LibriSpeech train-clean-100 audio)
phone labels / metadata file(s) with fields such as:
- id: utterance id
- file_name: audio path
- phones: list of ARPAbet phones

(If you rename the metadata file, update the loading example below accordingly.)

Quickstart: download + extract

Option A: Git LFS (recommended)

git lfs install
git clone https://huggingface.co/datasets/huper29/huper-clean100-proxyphones
cd huper-clean100-proxyphones
git lfs pull

Reconstruct and extract:

cat blobs/train.tar.zst.part-* > train.tar.zst

# If your tar supports zstd:
tar -I zstd -xf train.tar.zst

# If tar does NOT support zstd, use:
# unzstd -c train.tar.zst | tar -xf -

You should now see a train/ directory.

Option B: download shards from Python (no git)

from huggingface_hub import hf_hub_download

repo_id = "huper29/huper-clean100-proxyphones"
parts = [f"blobs/train.tar.zst.part-{i:03d}" for i in range(5)]
local_paths = [hf_hub_download(repo_id=repo_id, filename=p, repo_type="dataset") for p in parts]
print(local_paths)

Then cat + extract as above.

Load in Python (HuggingFace Datasets)

If you have a JSONL metadata file like train/metadata.jsonl with columns
id, file_name, phones:

from datasets import load_dataset, Audio

ds = load_dataset("json", data_files="train/metadata.jsonl", split="train")
ds = ds.cast_column("file_name", Audio(sampling_rate=16_000))
print(ds[0])

If your metadata is TSV/CSV, replace "json" with "csv" and set the correct delimiter.

Notes / Limitations

Labels are proxy phones produced by HuPER-style pipelines; they are not human-verified.
Please follow the original LibriSpeech licensing terms (CC BY 4.0).

Citation

If you use this dataset, please cite:

@article{guo2026huper,
  title   = {HuPER: A Human-Inspired Framework for Phonetic Perception},
  author  = {Guo, Chenxu and Lian, Jiachen and Liu, Yisi and Huang, Baihe and Narayanan, Shriyaa and Cho, Cheol Jun and Anumanchipalli, Gopala},
  journal = {arXiv preprint arXiv:2602.01634},
  year    = {2026}
}

@inproceedings{panayotov2015librispeech,
  title={Librispeech: an ASR corpus based on public domain audio books},
  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
  booktitle={ICASSP},
  year={2015}
}

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Total file size:

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Paper for huper29/huper-clean100-proxyphones

HuPER: A Human-Inspired Framework for Phonetic Perception

Paper • 2602.01634 • Published Feb 2