Pytorch Implementation of SEGAN (Speech Enhancement GAN)

Implementation of SEGAN by Pascual et al. in 2017, using pytorch. Original Tensorflow version can be found here.

Prerequisites

• python v3.5.2 or higher
• pytorch v0.3.0 (other versions not tested)
• CUDA preferred
• noisy speech dataset downloaded from here
• libraries specified in requirements.txt

Installing Libraries

pip install -r requirements.txt

Data Preprocessing

Use data_preprocess.py file to preprocess downloaded data. Adjust the file paths at the beginning of the file to properly locate the data files, output folder, etc. Uncomment functions in __main__ to perform desired preprocessing stage.

Data preprocessing consists of three main stages:

1. Downsampling - downsample original audio files (48k) to sampling rate of 16000.
2. Serialization - Splitting the audio files into 2^14-sample (about 1 second) snippets.
3. Verification - whether it contains proper number of samples.

Note that the second stage takes a fairly long time - more than an hour.

Training

python model.py

Again, fix and adjust datapaths in model.py according to your needs. Especially, provide accurate path to where serialized data are stored.