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wake-up word emotion recognition [APSIPA 2022]

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// repository documentation

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# Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words

Wake-up word emotion recognition is a task to capture the speakers’ emotional state using short lexically-matched speech such as Ok Google or Hey Siri.

- [Dataset & Pretrained Model at Zenodo](https://zenodo.org/record/7091465)

## Reference
Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words [[ArXiv](https://arxiv.org/abs/2211.03371)]

```
@inproceedings{kim2022hi,
  title={Hi, KIA: A Speech Emotion Recognition Dataset for Wake-Up Words},
  author={Taesu Kim, SeungHeon Doh, Gyunpyo Lee, Hyung seok Jun, Juhan Nam, Hyeon-Jeong Suk},
  booktitle={Proceedings of the 14th Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)},
  year={2022}
}
```

## Requirements

1. Install python and PyTorch:
    - python==3.7
    - pytorch-lightning==1.4.9 (important!)
    - torch==1.7.1 (Please install it according to your [CUDA version](https://pytorch.org/get-started/previous-versions/#linux-and-windows-4).)
    
2. Other requirements:
    - pip install -r requirements.txt

```
conda create -n YOUR_ENV_NAME python=3.7
conda activate YOUR_ENV_NAME
pip install -r requirements.txt
```

## Training
1. Download the data files from [HERE](https://zenodo.org/record/6989810)
    
2. Preprocessing
    audio: resampling to 16000

python preprocessing.py

3. Transfer Learning training options:

python train.py --freeze_type none
        python train.py --freeze_type feature # best option
        python train.py --freeze_type context
        python train.py --freeze_type all

## Reproduce results in paper

Fore more examples, check bash files under `scripts` folder. 
- you can check ML performance in [notebook](https://github.com/SeungHeonDoh/hi_kia/blob/master/notebook/ML%20Classification.ipynb)
- Reproduce performance in [notebook](https://github.com/SeungHeonDoh/hi_kia/blob/master/notebook/Reproduce.ipynb)

## Inference using your own data (WIP)

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