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+# NonverbalTTS Dataset 🎵🗣️
+
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15274617.svg)](https://doi.org/10.5281/zenodo.15274617)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-blue)](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 ✨
+
+- **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://arxiv.org/abs/2308.05725) corpora
+- **Rich metadata**: Emotion labels, NV annotations, speaker IDs, audio quality metrics
+
+<!-- ## Dataset Structure 📂
+
+NonverbalTTS/
+├── wavs/ # Audio files (16-48kHz WAV format)
+│ ├── ex01_sad_00265.wav
+│ └── ...
+├── .gitattributes
+├── README.md
+└── metadata.csv # Metadata annotations -->
+
+
+## Metadata Schema (`metadata.csv`) 📋
+
+| 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, 🌬️ how've you been?"` |
+| `Annotator response {1,2,3}` | Refined transcriptions | `"So, Mom, how've you been?"` |
+| `Result` | Final fused transcription | `"So, Mom, 🌬️ 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**: 🌬️=Breath, 😂=Laughter, etc. (See [Annotation Guidelines](https://zenodo.org/records/15274617))
+
+## Loading the Dataset 💻
+
+```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 🔧
+
+1. **Automatic Detection**  
+   - NV detection using [BEATs](https://arxiv.org/abs/2409.09546)  
+   - Emotion classification with [emotion2vec+](https://arxiv.org/abs/2402.XXX)  
+   - 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 📊
+
+
+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 (Laugh)	| 0.92 |	0.74 |
+|Human Preference | 	33.4% |	35.4% |
+
+
+## Use Cases 💡
+- Training expressive TTS models
+- Zero-shot NV synthesis
+- Emotion-aware speech generation
+- Prosody modeling research
+
+## License 📜
+- Annotations: CC BY-NC-SA 4.0
+- Audio: Adheres to original source licenses (VoxCeleb, Expresso)
+
+
+## Citation 📝
+
+```
+@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}
+}
+```
+