tawawhite/wavernn

WaveRNN Vocoder + TTS

WaveRNN

(Update: Vanilla Tacotron One TTS system just implemented - more coming soon!)

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

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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 :)

Samples

Can be found here.

Pretrained Models

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

Both are trained on LJSpeech

• WaveRNN (Mixture of Logistics output) trained to 800k steps
• Tacotron trained to 180k steps

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References

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

Acknowlegements

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