tiberiu44 / subscale-wavernn

Implementation of the subscale framework from the WaveRNN paper, building on top of Fatchord's WaveRNN repo

Papercup Subscale WaveRNN repository

This code implements the Subscale part of the WaveRNN paper on top of Fatchord's original implementation.

Please refer to this accompanying blogpost for details of our interpretation: Subscale WaveRNN

Original publication: Efficient Neural Audio Synthesis

Initial implementation: Fatchord's Repo

Samples after training for 1M iterations on the Sharvard dataset with current hparams: samples

Quickstart: train a new model

To train the model, use the command below:

CUDA_VISIBLE_DEVICES={your_gpus} python train.py --data ../data/{your_dataset} --expName {your_experiment}

THE TRAINING SCRIPT IS MULTI-GPU BY DEFAULT, TO USE SINGLE GPUS PLEASE SET CUDA_VISIBLE_DEVICES TO YOUR PREFERRED GPU

The {your_dataset} folder is assumed to have the following folder structure:

• {args.data}/train/mel;
• {args.data}/train/wav_24khz;
• {args.data}/valid/mel;
• {args.data}/valid/wav_24khz

If you are providing the features, the data loader will create a set based on the intersection of filenames present in the mel folder and the wav_24khz folder.

You can download a version of the Sharvard dataset with this folder structure here

Extract with: tar xvf Sharvard.tar.gz

Inspect your trained model with tensorboard

To use within WaveRNN: tensorboard --logdir tensorboard-runs --port {MY PORT}.

To generate from a trained model

Using gen_wavs.py:

CUDA_VISIBLE_DEVICES={} python gen_wavs.py --data {mel_directory} --checkpoint {checkpoint_path} --out_dir {out_folder}

Play around with HParams

The most fun hyperparameters to play around with are the three subscale parameters:

• batch_factor
• horizon
• lookback

As well as tweaking the Condition Network. Any issues or cool findings let us know!