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---
tags:
- audio
license: apache-2.0
language:
- en
pretty_name: NonverbalTTS
size_categories:
- 1K<n<10K
---
# NonverbalTTS Dataset π΅π£οΈ
[](https://doi.org/10.5281/zenodo.15274617)
[](https://huggingface.co/datasets/deepvk/NonverbalTTS)
**NonverbalTTS** is a 17-hour open-access English speech corpus with aligned text annotations for **nonverbal vocalizations (NVs)** and **emotional categories**, designed to advance expressive text-to-speech (TTS) research.
## Key Features β¨
- **17 hours** of high-quality speech data
- **10 NV types**: Breathing, laughter, sighing, sneezing, coughing, throat clearing, groaning, grunting, snoring, sniffing
- **8 emotion categories**: Angry, disgusted, fearful, happy, neutral, sad, surprised, other
- **Diverse speakers**: 2296 speakers (60% male, 40% female)
- **Multi-source**: Derived from [VoxCeleb](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/) and [Expresso](https://speechbot.github.io/expresso/) corpora
- **Rich metadata**: Emotion labels, NV annotations, speaker IDs, audio quality metrics
<!-- ## Dataset Structure π
NonverbalTTS/
βββ wavs/ # Audio files (16-48kHz WAV format)
β βββ ex01_sad_00265.wav
β βββ ...
βββ .gitattributes
βββ README.md
βββ metadata.csv # Metadata annotations -->
## Metadata Schema (`metadata.csv`) π
| Column | Description | Example |
|--------|-------------|---------|
| `index` | Unique sample ID | `ex01_sad_00265` |
| `file_name` | Audio file path | `wavs/ex01_sad_00265.wav` |
| `Emotion` | Emotion label | `sad` |
| `Initial text` | Raw transcription | `"So, Mom, π¬οΈ how've you been?"` |
| `Annotator response {1,2,3}` | Refined transcriptions | `"So, Mom, how've you been?"` |
| `Result` | Final fused transcription | `"So, Mom, π¬οΈ how've you been?"` |
| `dnsmos` | Audio quality score (1-5) | `3.936982` |
| `duration` | Audio length (seconds) | `3.6338125` |
| `speaker_id` | Speaker identifier | `ex01` |
| `data_name` | Source corpus | `Expresso` |
| `gender` | Speaker gender | `m` |
**NV Symbols**: π¬οΈ=Breath, π=Laughter, etc. (See [Annotation Guidelines](https://zenodo.org/records/15274617))
## Loading the Dataset π»
```python
from datasets import load_dataset
dataset = load_dataset("deepvk/NonverbalTTS")
```
<!-- # Access train split
```print(dataset["train"][0])```
# Output: {'index': 'ex01_sad_00265', 'file_name': 'wavs/ex01_sad_00265.wav', ...}
-->
## Annotation Pipeline π§
1. **Automatic Detection**
- NV detection using [BEATs](https://arxiv.org/abs/2409.09546)
- Emotion classification with [emotion2vec+](https://huggingface.co/emotion2vec/emotion2vec_plus_large)
- ASR transcription via Canary model
2. **Human Validation**
- 3 annotators per sample
- Filtered non-English/multi-speaker clips
- NV/emotion validation and refinement
3. **Fusion Algorithm**
- Majority voting for final transcriptions
- Pyalign-based sequence alignment
- Multi-annotator hypothesis merging
## Benchmark Results π
Fine-tuning CosyVoice-300M on NonverbalTTS achieves parity with state-of-the-art proprietary systems:
|Metric | NVTTS | CosyVoice2 |
| ------- | ------- | ------- |
|Speaker Similarity | 0.89 | 0.85 |
|NV Jaccard | 0.8 | 0.78 |
|Human Preference | 33.4% | 35.4% |
## Use Cases π‘
- Training expressive TTS models
- Zero-shot NV synthesis
- Emotion-aware speech generation
- Prosody modeling research
## License π
- Annotations: CC BY-NC-SA 4.0
- Audio: Adheres to original source licenses (VoxCeleb, Expresso)
## Citation π
```
@dataset{nonverbaltts2024,
author = {Anonymous},
title = {NonverbalTTS Dataset},
month = December,
year = 2024,
publisher = {Zenodo},
version = {1.0},
doi = {10.5281/zenodo.15274617},
url = {https://zenodo.org/records/15274617}
}
``` |