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issac_languageid's Introduction

ISSAC - Interpretability of Speech Signal under Adverse Conditions - Language ID

GitHub Link: https://github.com/TonnyTran/ISSAC_LanguageID

Installation:

Setting up environment

  1. Install Kaldi
git clone -b 5.4 https://github.com/kaldi-asr/kaldi.git kaldi
cd kaldi/tools/; 
# Run this next line to check for dependencies, and then install them
extras/check_dependencies.sh
make; cd ../src; ./configure; make depend; make
  1. Install EspNet
git clone -b v.0.9.7 https://github.com/espnet/espnet.git
cd espnet/tools/        # change to tools folder
ln -s {kaldi_root}      # Create link to Kaldi. e.g. ln -s home/theanhtran/kaldi/
  1. Set up Conda environment
./setup_anaconda.sh anaconda espnet 3.7.9   # Create a anaconda environmetn - espnet with Python 3.7.9
make TH_VERSION=1.8.0 CUDA_VERSION=10.2     # Install Pytorch and CUDA
. ./activate_python.sh; python3 check_install.py  # Check the installation
conda install torchvision==0.9.0 torchaudio==0.8.0 -c pytorch
  1. Install Kaldi IO
conda install kaldi_io

Download the project

  1. Clone the project from GitHub into your workspace
git clone https://github.com/TonnyTran/ISSAC_LanguageID
  1. Point to your espnet

Open ISSAC_LanguageID/path.sh file, change $MAIN_ROOT$ to your espnet directory, e.g. MAIN_ROOT=/home/theanhtran/espnet

How to run Language ID systems

  1. Data preparation step Open ISSAC_LanguageID/prepare_data.sh file, update raw LRE 2017 data location of train, dev and test set
bash prepare_data.sh --steps 1-6     # we can run step by step
  1. Run the program: train Kaldi x-vector baseline
bash baseline_xvector.sh --steps 1-7
  1. Test the pretrained model: Kaldi x-vector baseline
bash test.sh --steps 1-2

issac_languageid's People

Contributors

tonnytran avatar

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