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chainer-WaveGlow

A Chainer implementation of WaveGlow( https://nv-adlr.github.io/WaveGlow ).

Generated samples

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

Requirements

I trained and generated with

  • python(3.5.2)
  • chainer (5.0.0)
  • librosa (0.6.2)
  • matplotlib (3.0.1)

Usage

download dataset

You can download VCTK Corpus(en multi speaker)/LJ-Speech(en single speaker) very easily via my repository.

set parameters

I'll write details later.

training

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.

generating

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.