WSRGlow

The official implementation of the Interspeech 2021 paper WSRGlow: A Glow-based Waveform Generative Model for Audio Super-Resolution. Audio samples can be found here. Interactive web demo on Replicate: https://replicate.ai/zkx06111/wsrglow

Feel free to create issues or send an email to kexunz@zju.edu.cn if you have problems running the code.

Before running the code, you need to install the dependicies by pip install -r requirements.txt.

The configs for model architecture and training scheme is saved in config.yaml. You can overwrite some of the attributes by adding the --hparams flag when running a command. The general way to run a python script is

python $SRC$ --config $CONFIG$ --hparams $KEY1$=$VALUE1$,$KEY2$=$VALUE2$,...

See hparams.py for more details.

To prepare data

Before training, you need to binarize the data first. The raw wav files should be put in the hparams['raw_data_path']. The binarized data would be put in the hparams['binary_data_path'].

Specifically, for the VCTK corpus, the file structure should be like

.
|--data
    |--raw
        |--VCTK-Corpus
            |--wav48
                |--$WAVS
|--checkpoints
    |--wsrglow

where the model checkpoints are in checkpoints/wsrglow.

The command to binarize is

python binarizer.py --config config.yaml

To modify the architecture of the model

The current WSRGlow model in model.py is designed for x4 super-resolution and takes waveform, spectrogram and phase information as input.

To train

Run python train.py --config config.yaml on a GPU.

To infer

Change the code in infer.py to specify the checkpoint you want to load and the sample inputs you want to use for inference. Run python infer.py --config config.yaml on a GPU, modify the code for the correct path of checkpoints and wav files.

Colab Sample

You can experiment with the colab sample and trained checkpoint here.

Note that the released checkpoint is trained for 2x super-resolution. You should select the GPU runtime to run the sample.