https://github.com/fatchord/wavernn

WaveRNN Vocoder + TTS
https://github.com/fatchord/wavernn

neural-vocoder pytorch speech-synthesis tacotron text-to-speech tts wavernn

Last synced: 3 days ago
JSON representation

WaveRNN Vocoder + TTS

• Host: GitHub
• URL: https://github.com/fatchord/wavernn
• Owner: fatchord
• License: mit
• Created: 2018-03-16T14:03:52.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2022-07-02T14:21:35.000Z (over 2 years ago)
• Last Synced: 2024-12-21T03:03:05.049Z (3 days ago)
• Topics: neural-vocoder, pytorch, speech-synthesis, tacotron, text-to-speech, tts, wavernn
• Language: Python
• Homepage: https://fatchord.github.io/model_outputs/
• Size: 236 MB
• Stars: 2,144
• Watchers: 86
• Forks: 698
• Open Issues: 107
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Awesome Lists containing this project

README

        
# WaveRNN

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

![Tacotron with WaveRNN diagrams](assets/tacotron_wavernn.png)

Pytorch implementation of Deepmind's WaveRNN model from [Efficient Neural Audio Synthesis](https://arxiv.org/abs/1802.08435v1)

# Installation

Ensure you have:

* Python >= 3.6

* [Pytorch 1 with CUDA](https://pytorch.org/)

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

![Attenion and Mel Training GIF](assets/training_viz.gif)

Download the [LJSpeech](https://keithito.com/LJ-Speech-Dataset/) 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 :)

# Samples

[Can be found here.](https://fatchord.github.io/model_outputs/)

# 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

____

### References

* [Efficient Neural Audio Synthesis](https://arxiv.org/abs/1802.08435v1)

* [Tacotron: Towards End-to-End Speech Synthesis](https://arxiv.org/abs/1703.10135)

* [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://arxiv.org/abs/1712.05884)

### Acknowlegements

* [https://github.com/keithito/tacotron](https://github.com/keithito/tacotron)

* [https://github.com/r9y9/wavenet_vocoder](https://github.com/r9y9/wavenet_vocoder)

* Special thanks to github users [G-Wang](https://github.com/G-Wang), [geneing](https://github.com/geneing) & [erogol](https://github.com/erogol)