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Showing content from https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts below:

GitHub - nii-yamagishilab/project-NN-Pytorch-scripts: see README

This is a set of Python / Pytorch scripts and tools for various speech-processing projects.

It is maintained by Xin Wang since 2021.

XW is a Pytorch newbie. Please feel free to give suggestions and feedback.

The repo is relatively large. Please use --depth 1 option for fast cloning.

git clone --depth 1 https://github.com/nii-yamagishilab/project-NN-Pytorch-scripts.git

Latest updates:
1. Code, databases, and resources for paper below were added. Please check project/10-asvspoof-vocoded-trn-ssl/
  
  Xin Wang, and Junichi Yamagishi. Can Large-scale vocoded spoofed data improve speech spoofing countermeasure with a self-supervised front end? ICASSP 2024
2. Neural vocoders pretrained on VoxCeleb2 dev and other datasets are available in tutorial notebook chapter_a3.ipynb
3. Code, databases, and resources for the paper below were added. Please check project/09-asvspoof-vocoded-trn/ for more details.
  
  Xin Wang, and Junichi Yamagishi. Spoofed training data for speech spoofing countermeasure can be efficiently created using neural vocoders. Proc. ICASSP 2023, accepted. https://arxiv.org/abs/2210.10570
4. Code for the paper for the paper below were added. Please check project/08-asvspoof-activelearn for more details.
  
  Xin Wang, and Junichi Yamagishi. Investigating Active-Learning-Based Training Data Selection for Speech Spoofing Countermeasure. In Proc. SLT, accepted. 2023.
5. Pointer to tutorials on neural vocoders were moved to ./tutorials/b1_neural_vocoder.
6. All pre-trained models were moved to Zenodo.

This repository contains a few projects and tutorials.

See project/README.md for an overview.

See tutorials/README.md for an overview.

Projects above use either one of the two environments:

For most of the projects, install env.yml is sufficient

# create environment
conda env create -f env.yml

# load environment (whose name is pytorch-1.7)
conda activate pytorch-1.7

For projects using SSL models, use ./env-fs-install.sh to install the dependency.

# make sure other conda envs are not loaded
bash env-fs-install.sh

# load
conda activate fairseq-pip2

Most of the projects include a simple demonstration script. Take project/01-nsf/cyc-noise-nsf as an example:

# cd into one project
cd project/01-nsf/cyc-noise-nsf-4

# add PYTHONPATH and activate conda environment
source ../../../env.sh 

# run the script
bash 00_demo.sh

The printed messages will show what is happening.

Detailed instruction is in README of each project.

Name Function ./core_scripts scripts (Numpy or Pytorch code) to manage the training process, data io, etc. ./core_modules finalized pytorch modules ./sandbox new functions and modules to be test ./project project directories, and each folder correspond to one model for one dataset ./project/*/*/main.py script to load data and run training and inference ./project/*/*/model.py model definition based on Pytorch APIs ./project/*/*/config.py configurations for training/val/test set data ./project/*/*/*.sh scripts to wrap the python codes

See more instructions on the design and conventions of this repository misc/DESIGN.md

By Xin Wang

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