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--- |
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task_categories: |
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- audio-to-audio |
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- text-to-speech |
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- audio-classification |
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language: |
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- en |
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--- |
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# NaturalVoices EVC |
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A large emotional voice conversion (EVC) dataset curated from spontaneous, in-the-wild podcast speech as part of the **NaturalVoices** project in collaboration with 🤗[MSP Lab at CMU LTI](https://huggingface.co/Lab-MSP). This release provides the emotion balanced subset of the NaturalVoices **870-hour** VC dataset and intended for training and evaluating emotion-aware voice conversion systems but not limited to VC tasks. |
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- 📄 Paper: *NaturalVoices: A Large-Scale, Spontaneous and Emotional Podcast Dataset for Voice Conversion* — https://arxiv.org/abs/2511.00256 \ |
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- 🧺 Dataset collection (related subsets, e.g., 10% of data & emotional VC): https://huggingface.co/collections/JHU-SmileLab/naturalvoices-voice-conversion-datasets \ |
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- <span style="display:inline-flex;align-items:center;gap:-6px"> |
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<img src="https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white" height=20 alt="GitHub badge"> |
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<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> |
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</span> |
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## Dataset Summary |
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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 **EVC** subset. |
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**What’s in this repo** |
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- ~370 hours of podcast speech tailored and preprocessed for EVC. |
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- Balanced distribution of categorical emotions (Angry, Happy, Neutral, Sad) |
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- A wide range of speakers both manually & automatically annotated. |
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- Annotations archive with per-utterance annotations including: |
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- Emotion categorical labels & dimensional attributes (valence/arousal/dominance), |
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- Speech quality indicators, |
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- Text, Gender, and Duration. |
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### Subsets |
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| Subset | Description | Link | |
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| --------------------------- | :------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- | |
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| NaturalVoices_VC_870h | 870h of speech data curated for VC | 🤗[JHU-SmileLab/NaturalVoices_VC_870h](https://JHU-SmileLab/NaturalVoices_VC_870h) | |
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| NaturalVoices_EVC | Emotion-balanced subset for Emotional Voice Conversion (EVC) | This repo | |
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| NaturalVoices_VC_01 (10%) | A smaller subset uniformly sampled from 870h (10%) | 🤗[JHU-SmileLab/NaturalVoices_VC_0.1](https://huggingface.co/datasets/JHU-SmileLab/NaturalVoices_VC_0.1) | |
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## How to use |
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You can directly download the dataset using the following command: |
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```bash |
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huggingface-cli download JHU-SmileLab/NaturalVoices_EVC --repo-type=dataset --local-dir=YOUR_LOCAL_DIR |
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``` |
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*Streaming support will be available* |
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## Cite & Contribute |
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If you use this dataset, please cite the paper: |
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```sql |
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@misc{du2025naturalvoiceslargescalespontaneousemotional, |
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title={NaturalVoices: A Large-Scale, Spontaneous and Emotional Podcast Dataset for Voice Conversion}, |
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author={Zongyang Du and Shreeram Suresh Chandra and Ismail Rasim Ulgen and Aurosweta Mahapatra and Ali N. Salman and Carlos Busso and Berrak Sisman}, |
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year={2025}, |
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eprint={2511.00256}, |
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archivePrefix={arXiv}, |
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primaryClass={eess.AS}, |
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url={https://arxiv.org/abs/2511.00256}, |
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} |
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``` |
