YuanPJ / acoustic-probing-model

Initial Layer-to-layer Analysis of End-to-end Automatic Speech Recognition Systems with A Probing-by-reconstruction Model

This project is forked from End-to-end-ASR-Pytorch

See more details in my paper and master thesis.

Demo Page

Demo Page

Usage

Setup

• Clone the project

  git clone [e-mail]:YuanPJ/acoustic-probing-model.git

• Download datasets LibriSpeech and MUSAN and put them in save/{dataset_name}

• Install required packages requirements.txt

  pip install -r requirements.txt

Train ASR

0. Follow End-to-end-ASR-Pytorch carefully to train your ASR model.

1. Modify model part in configurations libri_asr_example.yaml

2. Run train_asr.sh

   bash train_asr.sh your_asr_model_name

3. ASR model training logs (tensorboard) and configurations will be saved in log directory

4. ASR model checkpoints will be saved in checkpoint directory

Train Probing-by-reconstruction Model

1. Modify probing part in configurations libri_probing_example.yaml

2. Run train_probing.sh with trained ASR model checkpoint

   bash train_probing.sh your_probing_model_name checkpoint/your_asr_model_name/best_ctc.pth

3. Probing model training logs (tensorboard) and configurations will be saved in log directory

4. Probing model checkpoints will be saved in checkpoint directory

Test Probing-by-reconstruction Model

• Run test_probing.sh with configurations libri_probing_test_example.yaml

• Specify the name of dataset. You can augment the original LibriSpeech dataset with noises and put it in save/ directory.

  bash test_probing.sh your_probing_model_name dataset_name

• Reconstructed speech waveform files are in save/your_probing_model_name/ directory

Citation

@INPROCEEDINGS{9054675,  
    author={Li, Chung-Yi and Yuan, Pei-Chieh and Lee, Hung-Yi},  
    booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},   
    title={What Does a Network Layer Hear? Analyzing Hidden Representations of End-to-End ASR Through Speech Synthesis},   
    year={2020},
    pages={6434-6438},  
    doi={10.1109/ICASSP40776.2020.9054675}
}