K2-FSA Installation

It is recommended to install K2-FSA in separate virtual environments. We use the recommended pre-compiled wheels.

  • K2-CPU Installation (Torch version 2.0.1) 
      pip install torch==2.0.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
  pip install k2==1.24.3.dev20230718+cpu.torch2.0.1 -f https://k2-fsa.github.io/k2/cpu.html
  • K2-GPU Installation (CUDA version 11.7) 
      pip install torch==2.0.1+cu117 -f https://download.pytorch.org/whl/torch_stable.html
  pip install k2==1.24.3.dev20230718+cuda11.7.torch2.0.1 -f https://k2-fsa.github.io/k2/cuda.html

IceFall Installation

Installation source: IceFall Documentation

git clone https://github.com/k2-fsa/icefall
cd icefall
pip install -r ./requirements.txt
export PYTHONPATH=$PWD:$PYTHONPATH

Lhotse Installation

Installation source: Lhotse Documentation

git clone https://github.com/lhotse-speech/lhotse.git
pip install -e '.[dev]'
pre-commit install
pip install lhotse[kaldi]

Installing CUDA (11.7) and cuDNN (8.9)

Installation source: Icefall Documentation

  • Installing CUDA 
      ## Choose installation dir
  INSTALL_PATH="/home/sagar/bin/cuda/11.7.1/"
  mkdir -p ${INSTALL_PATH}

  ## Download cuda
  wget https://developer.download.nvidia.com/compute/cuda/11.7.1/local_installers/cuda_11.7.1_515.65.01_linux.run

  chmod +x cuda_11.7.1_515.65.01_linux.run

  ./cuda_11.7.1_515.65.01_linux.run \
  --silent \
  --toolkit \
  --installpath=${INSTALL_PATH} \
  --no-opengl-libs \
  --no-drm \
  --no-man-page

  • Installing cuDNN

    Check other available versions here: cuDNN Archive by Nvidia

      wget https://huggingface.co/csukuangfj/cudnn/resolve/main/cudnn-linux-x86_64-8.9.1.23_cuda11-archive.tar.xz

  tar xvf cudnn-linux-x86_64-8.9.1.23_cuda11-archive.tar.xz --strip-components=1 -C ${INSTALL_PATH}
  • Set environment variables 
      export CUDA_HOME=${INSTALL_PATH}
  export PATH=$CUDA_HOME/bin:$PATH
  export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH
  export LD_LIBRARY_PATH=$CUDA_HOME/lib:$LD_LIBRARY_PATH

  export CUDA_TOOLKIT_ROOT_DIR=$CUDA_HOME
  export CUDA_TOOLKIT_ROOT=$CUDA_HOME
  export CUDA_BIN_PATH=$CUDA_HOME
  export CUDA_PATH=$CUDA_HOME
  export CUDA_INC_PATH=$CUDA_HOME/targets/x86_64-linux
  export CFLAGS=-I$CUDA_HOME/targets/x86_64-linux/include:$CFLAGS
  • Verify installation 
      which nvcc

  nvcc --version