A Chainer implementation of WaveGlow( https://nv-adlr.github.io/WaveGlow ).
I uploaded generated samples to SoundCloud. https://soundcloud.com/dhgrs/sets/waveglow
And uploaded pretrained model for LJSpeech to Google Drive. https://drive.google.com/drive/folders/1Rsr9tymkrMrPYvo_PMC3o4_Q6C0cqxQR?usp=sharing
I trained and generated with
- python(3.5.2)
- chainer (5.0.0)
- librosa (0.6.2)
- matplotlib (3.0.1)
You can download VCTK Corpus(en multi speaker)/LJ-Speech(en single speaker) very easily via my repository.
I'll write details later.
You can use same command in each directory.
(without GPU)
python train.py
(with GPU #n)
python train.py -g n
You can resume snapshot and restart training like below.
python train.py -r snapshot_iter_100000
Other arguments -f and -p are parameters for multiprocess in preprocessing. -f means the number of prefetch and -p means the number of processes.
python generate.py -i <input file> -o <output file> -m <trained model>
If you don't set -o, default file name result.wav is used. If you don't set -s, the speaker is same as input file that got from filepath.