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--- |
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tags: |
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- audio |
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license: apache-2.0 |
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language: |
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- en |
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pretty_name: NonverbalTTS |
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size_categories: |
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- 1K<n<10K |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: default/train/** |
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- split: dev |
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path: default/dev/** |
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- split: test |
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path: default/test/** |
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- split: other |
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path: default/other/** |
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--- |
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# NonverbalTTS Dataset 🎵🗣️ |
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[](https://arxiv.org/abs/2507.13155) |
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[](https://huggingface.co/datasets/deepvk/NonverbalTTS) |
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**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. |
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## Key Features ✨ |
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- **17 hours** of high-quality speech data |
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- **10 NV types**: Breathing, laughter, sighing, sneezing, coughing, throat clearing, groaning, grunting, snoring, sniffing |
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- **8 emotion categories**: Angry, disgusted, fearful, happy, neutral, sad, surprised, other |
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- **Diverse speakers**: 2296 speakers (60% male, 40% female) |
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- **Multi-source**: Derived from [VoxCeleb](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/) and [Expresso](https://speechbot.github.io/expresso/) corpora |
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- **Rich metadata**: Emotion labels, NV annotations, speaker IDs, audio quality metrics |
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- **Sampling rate**: 16kHz for audio from VoxCeleb, 48kHz for audio from Expresso |
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<!-- ## Dataset Structure 📂 |
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NonverbalTTS/ |
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├── wavs/ # Audio files (16-48kHz WAV format) |
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│ ├── ex01_sad_00265.wav |
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│ └── ... |
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├── .gitattributes |
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├── README.md |
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└── metadata.csv # Metadata annotations --> |
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<!-- ## Metadata Schema (`metadata.csv`) 📋 |
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| Column | Description | Example | |
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|--------|-------------|---------| |
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| `index` | Unique sample ID | `ex01_sad_00265` | |
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| `file_name` | Audio file path | `wavs/ex01_sad_00265.wav` | |
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| `Emotion` | Emotion label | `sad` | |
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| `Initial text` | Raw transcription | `"So, Mom, 🌬️ how've you been?"` | |
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| `Annotator response {1,2,3}` | Refined transcriptions | `"So, Mom, how've you been?"` | |
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| `Result` | Final fused transcription | `"So, Mom, 🌬️ how've you been?"` | |
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| `dnsmos` | Audio quality score (1-5) | `3.936982` | |
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| `duration` | Audio length (seconds) | `3.6338125` | |
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| `speaker_id` | Speaker identifier | `ex01` | |
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| `data_name` | Source corpus | `Expresso` | |
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| `gender` | Speaker gender | `m` | --> |
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<!-- **NV Symbols**: 🌬️=Breath, 😂=Laughter, etc. (See [Annotation Guidelines](https://zenodo.org/records/15274617)) --> |
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## Loading the Dataset 💻 |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("deepvk/NonverbalTTS") |
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``` |
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<!-- # Access train split |
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```print(dataset["train"][0])``` |
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# Output: {'index': 'ex01_sad_00265', 'file_name': 'wavs/ex01_sad_00265.wav', ...} |
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--> |
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## Annotation Pipeline 🔧 |
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1. **Automatic Detection** |
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- NV detection using [BEATs](https://arxiv.org/abs/2409.09546) |
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- Emotion classification with [emotion2vec+](https://huggingface.co/emotion2vec/emotion2vec_plus_large) |
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- ASR transcription via Canary model |
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2. **Human Validation** |
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- 3 annotators per sample |
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- Filtered non-English/multi-speaker clips |
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- NV/emotion validation and refinement |
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3. **Fusion Algorithm** |
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- Majority voting for final transcriptions |
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- Pyalign-based sequence alignment |
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- Multi-annotator hypothesis merging |
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## Benchmark Results 📊 |
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Fine-tuning CosyVoice-300M on NonverbalTTS achieves parity with state-of-the-art proprietary systems: |
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|Metric | NVTTS | CosyVoice2 | |
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| ------- | ------- | ------- | |
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|Speaker Similarity | 0.89 | 0.85 | |
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|NV Jaccard | 0.8 | 0.78 | |
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|Human Preference | 33.4% | 35.4% | |
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## Use Cases 💡 |
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- Training expressive TTS models |
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- Zero-shot NV synthesis |
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- Emotion-aware speech generation |
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- Prosody modeling research |
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## License 📜 |
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- Annotations: CC BY-NC-SA 4.0 |
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- Audio: Adheres to original source licenses (VoxCeleb, Expresso) |
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<!-- ## Citation 📝 |
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TODO --> |
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<!-- |
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``` |
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@dataset{nonverbaltts2024, |
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author = {Borisov Maksim, Spirin Egor, Dyatlova Darya}, |
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title = {NonverbalTTS: A Public English Corpus of Text-Aligned Nonverbal Vocalizations with Emotion Annotations for Text-to-Speech}, |
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month = April, |
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year = 2025, |
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publisher = {Zenodo}, |
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version = {1.0}, |
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doi = {10.5281/zenodo.15274617}, |
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url = {https://zenodo.org/records/15274617} |
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} |
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``` --> |
