Installation

Requirements

• Python 3.6.1+

• gcc 4.9+ for PyTorch1.0.0+

Optionally, GPU environment requires the following libraries:

• Cuda 8.0, 9.0, 9.1, 10.0 depending on each DNN library

• Cudnn 6+, 7+

• NCCL 2.0+ (for the use of multi-GPUs)

(If you’ll use anaconda environment at installation step2, the following packages are installed using Anaconda, so you can skip them.)

• cmake3 for some extensions

  # For Ubuntu
  $ sudo apt-get install cmake
  

• sox

  # For Ubuntu
  $ sudo apt-get install sox
  # For CentOS
  $ sudo yum install sox
  

• sndfile

  # For Ubuntu
  $ sudo apt-get install libsndfile1-dev
  # For CentOS
  $ sudo yum install libsndfile
  

• ffmpeg (This is not required when installing, but used in some recipes)

  # For Ubuntu
  $ sudo apt-get install ffmpeg
  # For CentOS
  $ sudo yum install ffmpeg
  

• flac (This is not required when installing, but used in some recipes)

  # For Ubuntu
  $ sudo apt-get install flac
  # For CentOS
  $ sudo yum install flac
  

Supported Linux distributions and other requirements

We support the following Linux distributions with CI. If you want to build your own Linux by yourself, please also check our CI configurations. to prepare the appropriate environments

• ubuntu18

• centos7

• debian9

Step 1) [Optional] Install Kaldi

• If you’ll use ESPnet1 (under egs/): You need to compile Kaldi.

• If you’ll use ESPnet2 (under egs2/): You can skip installation of Kaldi.

Click to compile Kaldi...

Related links:

• Kaldi Github

• Kaldi Documentation

  • Downloading and installing Kaldi

  • The build process (how Kaldi is compiled)

• Kaldi INSTALL

Kaldi’s requirements:

• OS: Ubuntu, CentOS, MacOSX, Windows, Cygwin, etc.

• GCC >= 4.7

1. Git clone Kaldi

   $ cd <any-place>
   $ git clone https://github.com/kaldi-asr/kaldi
   

2. Install tools

   $ cd <kaldi-root>/tools
   $ make -j <NUM-CPU>
   

   1. Select BLAS library from ATLAS, OpenBLAS, or MKL

   • OpenBLAS

   $ cd <kaldi-root>/tools
   $ ./extras/install_openblas.sh
   

   • MKL (You need sudo privilege)

   $ cd <kaldi-root>/tools
   $ sudo ./extras/install_mkl.sh
   

   • ATLAS (You need sudo privilege)

   # Ubuntu
   $ sudo apt-get install libatlas-base-dev
   

3. Compile Kaldi & install

   $ cd <kaldi-root>/src
   # [By default MKL is used] ESPnet uses only feature extractor, so you can disable CUDA
   $ ./configure --use-cuda=no
   # [With OpenBLAS]
   # $ ./configure --openblas-root=../tools/OpenBLAS/install --use-cuda=no
   # If you'll use CUDA
   # ./configure --cudatk-dir=/usr/local/cuda-10.0
   $ make -j clean depend; make -j <NUM-CPU>
   

We also have prebuilt Kaldi binaries.

Step 2) Installation ESPnet

1. Git clone ESPnet

   $ cd <any-place>
   $ git clone https://github.com/espnet/espnet
   

2. [Optional] Put compiled Kaldi under espnet/tools

   If you have compiled Kaldi at Step1, put it under tools.

   $ cd <espnet-root>/tools
   $ ln -s <kaldi-root> .
   

   If you don’t have espnet/tools/kaldi when make, Kaldi repository is automatically put without compiling.

3. Setup Python environment

   You have to create <espnet-root>/tools/activate_python.sh to specify the Python interpreter used in espnet recipes. (To understand how ESPnet specifies Python, see path.sh for example.)

   We also have some scripts to generate tools/activate_python.sh.

   • Option A) Setup Anaconda environment

     $ cd <espnet-root>/tools
     $ ./setup_anaconda.sh [output-dir-name|default=venv] [conda-env-name|default=root] [python-version|default=none]
     # e.g.
     $ ./setup_anaconda.sh anaconda espnet 3.8
     

     This script tries to create a new miniconda if the output directory doesn’t exist. If you already have Anaconda and you’ll use it then,

     $ cd <espnet-root>/tools
     $ CONDA_TOOLS_DIR=$(dirname ${CONDA_EXE})/..
     $ ./setup_anaconda.sh ${CONDA_TOOLS_DIR} [conda-env-name] [python-version]
     # e.g.
     $ ./setup_anaconda.sh ${CONDA_TOOLS_DIR} espnet 3.8
     

   • Option B) Setup venv from system Python

     $ cd <espnet-root>/tools
     $ ./setup_venv.sh $(command -v python3)
     

   • Option C) Setup system Python environment

     $ cd <espnet-root>/tools
     $ ./setup_python.sh $(command -v python3)
     

   • Option D) Without setting Python environment.

     Option C and Option D are almost same. This option might be suitable for Google colab.

     $ cd <espnet-root>/tools
     $ rm -f activate_python.sh && touch activate_python.sh
     

4. Install ESPnet

   $ cd <espnet-root>/tools
   $ make
   

   The Makefile tries to install ESPnet and all dependencies including PyTorch. You can also specify PyTorch version, for example:

   $ cd <espnet-root>/tools
   $ make TH_VERSION=1.10.1
   

   Note that the CUDA version is derived from nvcc command. If you’d like to specify the other CUDA version, you need to give CUDA_VERSION.

   $ cd <espnet-root>/tools
   $ make TH_VERSION=1.10.1 CUDA_VERSION=11.3
   

   If you don’t have nvcc command, packages are installed for CPU mode by default. If you’ll turn it on manually, give CPU_ONLY option.

   $ cd <espnet-root>/tools
   $ make CPU_ONLY=0
   

Step 3) [Optional] Custom tool installation

Some packages used only for specific tasks, e.g. Transducer ASR, Japanese TTS, or etc. are not installed by default, so if you meet some installation error when running these recipe, you need to install them optionally.

e.g.

• To install Warp Transducer

  cd <espnet-root>/tools
  . activate_python.sh
  . ./setup_cuda_env.sh <cuda-root>  # e.g. <cuda-root> = /usr/local/cuda
  ./installers/install_warp-transducer.sh
  

• To install PyOpenJTalk

  cd <espnet-root>/tools
  . activate_python.sh
  ./installers/install_pyopenjtalk.sh
  

• To install a module using pip: e.g. to intstall ipython

  cd <espnet-root>/tools
  . activate_python.sh
  pip install ipython
  

Check installation

You can check whether your installation is successfully finished by

cd <espnet-root>/tools
. ./activate_python.sh; python3 check_install.py

Note that this check is always called in the last stage of the above installation.