Pytorch implementation of Tarotron and WaveRNN model.

Ensure you have:

• Python >= 3.6
• Pytorch 1 with CUDA

Then install the rest with pip:

pip install -r requirements.txt

Download your Dataset.

Edit hparams.py, point wav_path to your dataset and run:

python preprocess.py

or use preprocess.py --path to point directly to the dataset

Here's my recommendation on what order to run things:

1 - Train Tacotron with:

python train_tacotron.py

2 - You can leave that finish training or at any point you can use:

python train_tacotron.py --force_gta

this will force tactron to create a GTA dataset even if it hasn't finish training.

3 - Train WaveRNN with:

python train_wavernn.py --gta

NB: You can always just run train_wavernn.py without --gta if you're not interested in TTS.

4 - Generate Sentences with both models using:

python gen_tacotron.py wavernn

this will generate default sentences. If you want generate custom sentences you can use

python gen_tacotron.py --input_text "this is whatever you want it to be" wavernn

And finally, you can always use --help on any of those scripts to see what options are available :)

• Efficient Neural Audio Synthesis
• Tacotron: Towards End-to-End Speech Synthesis
• Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions

• https://github.com/fatchord/WaveRNN
• https://github.com/keithito/tacotron
• https://github.com/r9y9/wavenet_vocoder
• Special thanks to github users G-Wang, geneing & erogol