JHU-SmileLab/NaturalVoices_VC_870h

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.

• —📄 Paper: NaturalVoices: A Large-Scale, Spontaneous and Emotional Podcast Dataset for Voice Conversion — https://arxiv.org/abs/2511.00256 \
• —🧺 Dataset collection (related subsets, e.g., 10% of data & emotional VC): https://huggingface.co/collections/JHU-SmileLab/naturalvoices-voice-conversion-datasets \
• —<span style="display:inline-flex;align-items:center;gap:-6px"> <img src="https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white" height=20 alt="GitHub badge"> <span>The extensive (unfiltered) NaturalVoices dataset and the code for the data collection & curation pipeline: <a href="https://github.com/Lab-MSP/NaturalVoices">https://github.com/Lab-MSP/NaturalVoices</a></span> </span>

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

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}, 
}