Wave-U-Net for Speech-Enhancement (NNabla)

Implement of Improved Speech Enhancement with the Wave-U-Net.

Requrement

Python

• Python 3.6
• CUDA 10.1 & CuDNN 7.6
  • Please choose the appropriate CUDA and CuDNN version to match your NNabla version

Packages

Please install the following packages with pip. (If necessary, install latest pip first.)

• nnabla (over v1.1)
• nnabla-ext-cuda (over v1.1)
• scipy
• numba
• joblib
• pyQT5
• pyqtgraph (after installing pyQT5)
• pypesq (see "install with pip" on offical site)
  In the latest version, the package name is changed to pesq. If you install pesq, change 'pypesq' to pesq in 25th line in wave-u-net.py as

  from pesq import pesq as pypesq
  

Contents

• wave-u-net.py
  Main source code. Run this.

• data.py
  This is for creating Batch Data. Before runnning, please download wav dataset as seen below.

• settings.py
  This includes setting parameters.

Download & Create Database

1. Download wave-u-net.py, settings.py, data.py and save them into the same directory.

2. In the directory, make three folders data, pkl, params .

   • data folder : save wav data.
   • pickle folder : save pickled database "~.pkl".
   • params folder : save parameters including network models.

3. Download the following 4 dataset, and unzip them from

   Noisy speech database for training speech enhancement algorithms and TTS models
   https://datashare.is.ed.ac.uk/handle/10283/2791

   [ Direct Links ]

   • clean_trainset_wav.zip
   • noisy_trainset_wav.zip
   • clean_testset_wav.zip
   • noisy_testset_wav.zip

4. Move those unzipped 4 folders into data folder.

5. Convert the sampling frequency of all the wav data to 16kHz. For example, this site is useful. After converting, you can delete the original wav data.

Train & Predict

If train, in wave-u-net.py,

 Train = 1

If predict, in wave-u-net.py,

 Train = 0