SMIIP-NV: A Multi-Annotation Non-Verbal Expressive Speech Corpus

Dataset Description

SMIIP-NV is a multi-annotated non-verbal expressive speech corpus designed for training and evaluating LLM-based text-to-speech systems. It contains a diverse set of non-verbal sounds (e.g., laughter, crying, coughing) along with emotion labels (happy, sad, neutral, angry, surprised), enabling the synthesis of natural and expressive speech.

Key Features:

• Multi-dimensional annotations: Detailed emotion and non-verbal sound categories for each clip.
• Rich non-verbal expressions: Over 33 hours of audio from 37 speakers, covering laughter, crying, coughing, etc.
• Precise timestamps: Start and end times for every non-verbal event for fine-grained control.

This corpus empowers researchers and developers to build more natural and emotionally expressive TTS systems that seamlessly integrate non-verbal cues.

➡️ Download the SMIIP-NV Dataset: https://huggingface.co/datasets/xunyi/SMIIP-NV

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Dataset Structure

SMIIP_NV/
├── MIC-0001/
│   ├── neutral/
│   │   ├── annotation.txt
│   │   ├── MIC-0001_0001.wav
│   │   └── ...
│   ├── sad/
│   │   ├── annotation.txt
│   │   ├── MIC-0001_0013.wav
│   │   └── ...
│   ├── surprised/
│   │   ├── annotation.txt
│   │   ├── MIC-0001_0029.wav
│   │   └── ...
│   ├── angry/
│   │   ├── annotation.txt
│   │   ├── MIC-0001_0041.wav
│   │   └── ...
│   └── happy/
│       ├── annotation.txt
│       ├── MIC-0001_0048.wav
│       └── ...
├── MIC-0002/
│   ├── neutral/
│   │   └── ...
│   └── ...
└── ...

🚀 Demo

We provide both a static preview and an interactive online demonstration:

• Static Preview:
  • Visit our demo page: https://axunyii.github.io/SMIIP-NV
• Interactive Demo:
  • Live on Hugging Face Spaces: https://huggingface.co/spaces/xunyi/SMIIP-NV_Finetuned_CosyVoice2

🛠️ Fine-Tuning CosyVoice2 Process

🔗 Source Code: https://huggingface.co/xunyi/SMIIP-NV_finetune_CosyVoice2

1. Clone & Environment Setup

   git clone https://huggingface.co/xunyi/SMIIP-NV_finetune_CosyVoice2.git
   cd SMIIP-NV_finetune_CosyVoice2
   conda create -n SMIIP_NV_finetune -y python=3.10
   conda activate SMIIP_NV_finetune
   conda install -y -c conda-forge pynini==2.1.5
   pip install -r requirements.txt \
       -i https://mirrors.aliyun.com/pypi/simple/ \
       --trusted-host mirrors.aliyun.com
   

2. Prepare Data

   • Unzip SMIIP_NV_SPK_finetune.zip into corpus/ under the repository root.

   • Navigate to the example directory:

     cd examples/nv/cosyvoice2
     

3. Run Fine-Tuning

   • Edit run.sh: set stage=0 and stop_stage=3 for initial data prep.

   • Execute training:

     bash run.sh
     

   • To continue full training, adjust stop_stage=5 and rerun:

     bash run.sh
     

🔍 Inference Process

• Batch Inference:

  • Set stage=4 and stop_stage=4 in run.sh, then:

    bash run.sh
    

• Single Utterance Inference:

  • Use Python script:

    python inference.py
    

• Visualization & Web UI:

  • Launch the web interface as described below.

🖥️ Web UI Implementation

The demo UI is built with Gradio.

Start Web UI:

python3 webui.py --port 50000 \
    --model_dir pretrained_models/CosyVoice2-0.5B

Then open http://localhost:50000/ in your browser.

📜 License & Citation

This project is released under CC BY-NC-SA 4.0. Details at https://creativecommons.org/licenses/by-nc-sa/4.0/.

Please cite:

SMIIP-NV: A Multi-Annotation Non-Verbal Expressive Speech Corpus