WaveRNN Server

Web server wrapper for WaveRNN by fatchord. Start with python server.py and POST text to localhost:5700 to generate .wav-files.

WaveRNN

Pytorch implementation of Deepmind's WaveRNN model from Efficient Neural Audio Synthesis

Installation

Ensure you have:

• Python >= 3.6
• Pytorch 1 with CUDA

Then install the rest with pip:

pip install -r requirements.txt

How to Use

Quick Start

If you want to use TTS functionality immediately you can simply use:

python quick_start.py

This will generate everything in the default sentences.txt file and output to a new 'quick_start' folder where you can playback the wav files and take a look at the attention plots

You can also use that script to generate custom tts sentences and/or use '-u' to generate unbatched (better audio quality):

python quick_start.py -u --input_text "What will happen if I run this command?'

Training your own Models

Download the LJSpeech 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

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"

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

Samples

Can be found here.

Pretrained Models

Currently there are two pretrained models available in the /pretrained/ folder':

Both are trained on LJSpeech

• WaveRNN trained to 800k steps (400k normal mels / 400k gta finetuned)
• Tacotron(r=1) trained to 196k steps

Acknowledgments

• Efficient Neural Audio Synthesis
• keithito tacotron
• Special thanks to github users G-Wang, geneing