enskd

KO | EN

gitlite — search

#javascript #typescript #llm #machine-learning #rust #deep-learning #linux #docker #java #nodejs #kubernetes #awesome-list #openai #open-source

(★ 16)

[ICASSP'24] Emphasized Non-Target Speaker Knowledge in Knowledge Distillation for Speaker Verification

enskd Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Emphasized Non-Target Speaker Knowledge in Knowledge Distillation for Speaker Verification

This Repository contains the code and pretrained models for the following ICASSP 2024 paper:

* **Title** : Emphasized Non-Target Speaker Knowledge in Knowledge Distillation for Speaker Verification
* **Autor** : Duc-Tuan Truong, Ruijie Tao, Jia Qi Yip, Kong Aik Lee, Eng Siong Chng

# Prerequisites

## Environment Setting
Follow the below commands to install the required packages for preparing the dataset, training and testing the model.

``` sh
conda create -n wespeaker python=3.9
conda activate wespeaker
conda install pytorch=1.12.1 torchaudio=0.12.1 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install -r requirements.txt
```

## Datasets

We used VoxCeleb dataset for training and test. For noise augmentation, we used the MUSAN and RIRS corpus. To download and preprocesing data, please run the following snippet

``` sh
bash prepare_data.sh --stage 1 --stop_stage 2
```

## Pretrained Model
The pretrained teacher model WavLM-Large can be found at [microsoft/UniSpeech](https://github.com/microsoft/UniSpeech/tree/main/downstreams/speaker_verification) and download at [link](https://drive.google.com/file/d/1-aE1NfzpRCLxA4GUxX9ITI3F9LlbtEGP/view?usp=sharing). The WavLM-Large checkpoint should be put at `pretrained_model/`

We have uploaded pretrained models of our experiments. You can download pretrained models from [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/truongdu001_e_ntu_edu_sg/EpjkpezMPC9Fmrng3w73iVcB7aFORZoiNWg458Y3RlOGOA?e=FhYn6Q) and put in the corresponding directory in `exp/` folder.

## Training
To perform model training, run:
```
bash run_train.sh --config=/path/to/exp_config.yaml --exp_dir=/path/to/exp_dir
```

## Testing
To run evaluation on VoxCeleb evaluation sets: Vox-O, Vox-H, and Vox-E. 
```
bash run_test.sh --stage 1 --stop_stage 3 --config=/path/to/exp_config.yaml --exp_dir=/path/to/exp_dir --model_path=/path/to/pretrained_model.pt
```

## Citation

If you find our repository valuable for your work, please consider citing our paper:
```
@INPROCEEDINGS{enskd,
  author={Truong, Duc-Tuan and Tao, Ruijie and Yip, Jia Qi and Aik Lee, Kong and Chng, Eng Siong},
  booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Emphasized Non-Target Speaker Knowledge in Knowledge Distillation for Automatic Speaker Verification}, 
  year={2024},
  volume={},
  number={},
  pages={10336-10340},
  doi={10.1109/ICASSP48485.2024.10447160}}
```

## License
[MIT](https://choosealicense.com/licenses/mit/)

### Acknowledge

Our work is built upon the [wenet-e2e/wespeaker](https://github.com/wenet-e2e/wespeaker) toolkit. We also follow some parts of the following codebases:

[microsoft/UniSpeech](https://github.com/microsoft/UniSpeech) (for WavLM model architechture).

[megvii-research/mdistiller](https://github.com/megvii-research/mdistiller) (for Decouple Knowledge Distillation).

Thanks for these authors for sharing their work!

enskd

enskd Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

enskd

enskd Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README