Skip to content
Snippets Groups Projects
Commit a44f9df6 authored by Simon's avatar Simon
Browse files

update readme

parent f88b8e8d
Branches
No related tags found
No related merge requests found
# To Complete
\ No newline at end of file
# A Recurrent Variational Autoencoder for Speech Enhancement
This repository contains the implementation of the speech enhancement method proposed in:
>S. Leglaive, X. Alameda-Pineda, L. Girin, R. Horaud, [A Recurrent Variational Autoencoder for Speech Enhancement](https://hal.archives-ouvertes.fr/hal-02329000/document), in Proc. of the IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2020.
Audio examples are available [here](https://sleglaive.github.io/demo-icassp2020.html).
If you use this code, please cite the above-mentioned paper ([Bibtex](https://hal.archives-ouvertes.fr/hal-02329000v1/bibtex)).
## Repository structure
```bash
.
├── audio
│ ├── mix_qut_wsj0.wav
│ ├── mix_sunrise.wav
│ └── mix_thierry_roland.wav
├── environment.yml
├── LICENSE.txt
├── main.py
├── README.md
├── saved_model
│ ├── WSJ0_2019-07-15-10h01_RVAE_BRNNenc_BRNNdec_latent_dim=16
│ │ ├── final_model_RVAE_epoch145.pt
│ │ ├── loss.pdf
│ │ ├── loss_RVAE.pckl
│ │ ├── parameters.pckl
│ │ └── parameters.txt
│ ├── WSJ0_2019-07-15-10h14_RVAE_RNNenc_RNNdec_latent_dim=16
│ │ ├── final_model_RVAE_epoch121.pt
│ │ ├── loss.pdf
│ │ ├── loss_RVAE.pckl
│ │ ├── parameters.pckl
│ │ └── parameters.txt
│ └── WSJ0_2019-07-15-10h21_FFNN_VAE_latent_dim=16
│ ├── final_model_RVAE_epoch65.pt
│ ├── loss.pdf
│ ├── loss_RVAE.pckl
│ ├── parameters.pckl
│ └── parameters.txt
├── SE_algorithms.py
└── training
├── speech_dataset.py
├── train_BRNN_WSJ0.py
├── train_FFNN_WSJ0.py
├── train_RNN_WSJ0.py
└── VAEs.py
```
## Python files
* ```main.py```: Main script to run the speech enhancement algorithms. If you just want to test the method quickly, run this script. Input and output audio files are located in the ```audio``` folder.
* ```SE_algorithms.py```: Implementation of the speech enhancement algorithms (MCEM, PEEM, VEM).
* ```./training/speech_dataset.py```: Custom Pytorch dataset for training.
* ```./training/VAEs.py```: Pytorch implementation of the FFNN, RNN and BRNN variational autoencoders (VAEs).
* ```./training/train_FFNN_WSJ0.py```: Script to train the FFNN VAE.
* ```./training/train_RNN_WSJ0.py```: Script to train the RNN VAE.
* ```./training/train_BRNN_WSJ0.py```: Script to train the BRNN VAE.
## Conda environment
```environment.yml``` describes the conda environment that was used for the experiments.
## License
GNU Affero General Public License (version 3), see ```LICENSE.txt```.
\ No newline at end of file
0% Loading or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment