jhu-neural-wpe

jhu-neural-wpe is a Neural network based dereverberation toolkit which includes an open source implementation of Keisuke Kinoshita, et al. "Neural network-based spectrum estimation for online WPE dereverbertion.", Interspeech 2017. jhu-neural-wpe uses chainer as the deep learning engine. The WPE filtering code in src/wpe.py is based on NARA-WPE by Lukas Drude, et. al. "NARA-WPE: A Python package for weighted prediction error dereverberation in Numpy and Tensorflow for online and offline processing.", ITG 2018.

Installation

Step 1) setting of the environment

To use cuda (and cudnn), make sure to set paths in your .bashrc or .bash_profile appropriately.

CUDAROOT=/path/to/cuda

export PATH=$CUDAROOT/bin:$PATH
export LD_LIBRARY_PATH=$CUDAROOT/lib64:$LD_LIBRARY_PATH
export CUDA_HOME=$CUDAROOT
export CUDA_PATH=$CUDAROOT

Step 2) installation of tools

Install Python libraries with miniconda and other required tools

$ cd tools
$ make

Step 3) installation check

You can check whether the install is succeeded via the following commands

$ cd tools
$ source venv/bin/activate && python check_install.py

If you have no warning, you are ready to run the recipe!

If there are some problems in python libraries, you can re-setup only python environment via following commands

$ cd tools
$ make clean

Execution with REVERB data

$ cd egs/reverb
$ ./run.sh

Setup in your cluster

Change $cuda_cmd in path.sh according to your cluster setup. For more information see http://kaldi-asr.org/doc/queue.html.

Acknowledgement

This work was supported by Yahoo Japan Corporation.

chenxinglili / jhu-neural-wpe