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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
wav: binary
__key__: string
__url__: string
csv: null
to
{'__key__': Value('string'), '__url__': Value('string'), 'csv': Value('binary')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2431, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1984, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2192, in cast_table_to_features
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              wav: binary
              __key__: string
              __url__: string
              csv: null
              to
              {'__key__': Value('string'), '__url__': Value('string'), 'csv': Value('binary')}
              because column names don't match

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NaturalVoices VC 870h

A large voice conversion (VC) dataset curated from spontaneous, in-the-wild podcast speech as part of the NaturalVoices project in collaboration with 🤗MSP Lab at CMU LTI. This release provides the 870-hour VC dataset and subsets mainly intended for training and evaluating emotion-aware voice conversion systems but not limited to VC tasks.

Dataset Summary

NaturalVoices VC compiles real-life, expressive podcast speech and provides automatic annotations designed for VC research (e.g., emotion attributes, speaker identity, speech quality, transcripts). The broader NaturalVoices corpus contains thousands of hours of podcast speech; this repository hosts the VC_870h subset.

What’s in this repo

  • ~870 hours of podcast speech tailored and preprocessed for VC.

  • A wide range of speakers >2670, both manually & automatically annotated.

  • Annotations archive (NV_VC_870h_Annotation.tar.gz) with per-utterance annotations including:

    • Emotion categorical labels & dimensional attributes (valence/arousal/dominance),
    • Speech quality indicators,
    • Text, Gender, and Duration.

Subsets

Subset Description Link
NaturalVoices_VC_870h 870h of speech data curated for VC This repo
NaturalVoices_EVC Emotion-balanced subset for Emotional Voice Conversion (EVC) 🤗JHU-SmileLab/NaturalVoices_EVC
NaturalVoices_VC_01 (10%) A smaller subset uniformly sampled from 870h (10%) 🤗JHU-SmileLab/NaturalVoices_VC_0.1

How to use

You can directly download the dataset using the following command:

huggingface-cli download JHU-SmileLab/NaturalVoices_VC_870h --repo-type=dataset --local-dir=YOUR_LOCAL_DIR 

Streaming support will be available

Cite & Contribute

If you use this dataset, please cite the paper:

@misc{du2025naturalvoiceslargescalespontaneousemotional,
      title={NaturalVoices: A Large-Scale, Spontaneous and Emotional Podcast Dataset for Voice Conversion}, 
      author={Zongyang Du and Shreeram Suresh Chandra and Ismail Rasim Ulgen and Aurosweta Mahapatra and Ali N. Salman and Carlos Busso and Berrak Sisman},
      year={2025},
      eprint={2511.00256},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/2511.00256}, 
}
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