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NaturalVoices VC 10%

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 10% subset uniformly sampled from 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_01 subset.

What’s in this repo

  • ~90 hours of podcast speech tailored and preprocessed for VC.

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

  • Annotations archive 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 🤗JHU-SmileLab/NaturalVoices_VC_870h
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%) This repo

How to use

You can directly download the dataset using the following command:

huggingface-cli download JHU-SmileLab/NaturalVoices_VC_0.1 --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}, 
}