Rainbow Keywords - Official PyTorch Implementation

This repository contains the Official PyTorch implementation for the Paper "Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword Spotting " by Yang Xiao, Nana Hou and Eng Siong Chng.

If you have any questions on this repository or the related paper, feel free to create an issue or send me an email.

• NEWS：This paper is accepted to INTERSPEECH 2022, Korea.

Abstract

Catastrophic forgetting is a thorny challenge when updating keyword spotting (KWS) models after deployment. This problem will be more challenging if KWS models are further required for edge devices due to their limited memory. To alleviate such an issue, we propose a novel diversity-aware incremental learning method named Rainbow Keywords (RK). Specifically, the proposed RK approach introduces a diversity-aware sampler to select a diverse set from historical and incoming keywords by calculating classification uncertainty. As a result, the RK approach can incrementally learn new tasks without forgetting prior knowledge. Besides, the RK approach also proposes data augmentation and knowledge distillation loss function for efficient memory management on the edge device. Experimental results show that the proposed RK approach achieves 4.2% absolute improvement in terms of average accuracy over the best baseline on Google Speech Command dataset with less required memory.

Overview of the results of Rainbow Keywords

Here is a list of continual learning methods available for KWS:

• Elastic Weight Consolidation (EWC) [view]
• Riemannian Walk (RWalk) [view]
• Incremental Classifier and Representation Learning(iCaRL) [view]
• Bias Correction(BiC) [view]
• Rainbow Keywords(RK) [view]

If you want to see more details, see the paper.

Getting Started

Requirements

• Python3
• audiomentations==0.20.0
• einops==0.3.2
• matplotlib==3.4.3
• numpy==1.20.3
• pandas==1.3.4
• seaborn==0.11.2
• torch==1.10.0
• torch_optimizer==0.3.0
• torchaudio==0.10.0

Setup Environment

You need to create the running environment by Anaconda,

conda env create -f environment.yml
conda active kws

If you don't have Anaconda, you can install (Linux version) by

cd <ROOT>
bash conda_install.sh

Download Dataset

We use the Google Speech Commands Dataset (GSC) as the training data. By running the script, you can download the training data:

cd <ROOT>/dataset
bash download_gsc.sh

Usage

To run the experiments in the paper, you just run run.sh.

bash run.sh 

For various experiments, you should know the role of each argument.

• MODE: CIL methods. Our method is called rainbow_keywords. [finetune, native_rehearsal, joint, rwalk, icarl, rainbow_keywords, ewc, bic] (joint calculates accuracy when training all the datasets at once.)
• MEM_MANAGE: Memory management method. default uses the memory method which the paper originally used. [default, random, reservoir, uncertainty, prototype].
• RND_SEED: Random Seed Number
• DATASET: Dataset name [gsc]
• STREAM: The setting whether current task data can be seen iteratively or not.[online]
• MEM_SIZE: Memory size: {300, 500, 1000, 1500}
• TRANS: Augmentation. Multiple choices [mixup,specaugment]

Results

There are three types of logs during running experiments; logs, results, tensorboard. The log files are saved in logs directory, and the results which contains accuracy of each task are saved in results directory.

root_directory
    |_ logs 
        |_ [dataset]
            |_.log
            |_ ...
    |_ results
        |_ [dataset]
            |_.npy
            |_...

In addition, you can also use the tensorboard as following command.

tensorboard --logdir tensorboard

Citation

Please cite our paper if it is helpful to your work:

@inproceedings{huang2022progressive,
  title={Progressive Continual Learning for Spoken Keyword Spotting},
  author={Huang, Yizheng and Hou, Nana and Chen, Nancy F},
  booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={7552--7556},
  year={2022},
}
@article{xiao2022rainbow,
  title={Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword Spotting},
  author={Xiao, Yang and Hou, Nana and Chng, Eng Siong},
  journal={arXiv preprint arXiv:2203.16361},
  year={2022}
}

Acknowledgements

Our implementations use the source code from the following repositories and users:

• rainbow-memory
• online-continual-learning
• icarl
• kws-continual-learning

License

The project is available as open source under the terms of the MIT License.