This project has been fully tested on Python 3.6.8
and TensorFlow 1.14
under Ubuntu 18.04.2 LTS
.
We recommend that users use Docker
or a virtual environment such as conda
to install the python requirements.
conda create -p <path>/<env_name> python=3.6
source activate <path>/<env_name>
conda install tensorflow-gpu=1.14.0
Delta dependient on third party tools, so when run the program, need blow to install tools:
activate the environment and use below
cd tools && make
For case you want install Tensorflow Gpu 1.14.0
, under machine which has Gpu Driver 410.48
.
It has problem of runtime not compariable with driver version, when isntall using conda.
Then we can install tensorflow from Pip
as below:
Same to conda install.
See CUDA Compatibility for CUDA Toolkit and Compatible Driver Version
.
conda install cudatoolkit==10.0.130
conda install cudnn==7.6.0
For user in China, we can set conda mirror as below:
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config --set show_channel_urls yes
pip install -i https://pypi.tuna.tsinghua.edu.cn/simple tensorflow-gpu==1.14.0
Same to conda install.
Install DELTA without speech
dependences:
cd tools && make basic check_install
By default we will install DELTA with Kaldi
toolkit:
cd tools && make delta
If user has installed Kaldi
, please DELTA as below:
cd tools && make delta KALDI=<kaldi-path>
it is simply link the <kaldi-path>
to tools/kaldi
.
Please see delta
target of tools/Makefile
.
Install DELTANN as below:
cd tools && make deltann
For more details, please see deltann
target of tools/Makefile