Pytorch implementation of Deepmind's WaveRNN model from Efficient Neural Audio Synthesis
Ensure you have:
- Python >= 3.6
- Pytorch 1 with CUDA
Then install the rest with pip:
pip install -r requirements.txt
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?'
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 :)
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
- Efficient Neural Audio Synthesis
- Tacotron: Towards End-to-End Speech Synthesis
- Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
- https://github.com/keithito/tacotron
- https://github.com/r9y9/wavenet_vocoder
- Special thanks to github users G-Wang, geneing & erogol