chainer-ClariNet
A Chainer implementation of ClariNet( https://arxiv.org/abs/1807.07281 ).
Results
Autoregressive WaveNet(Single Gaussian ver.) trained with VCTK Corpus
https://nana-music.com/sounds/04027269/
Student Gaussian IAF trained with LJ-Speech
https://nana-music.com/sounds/043ba7b4/
Requirements
I trained and generated with
- python(3.5.2)
- chainer (5.0.0b4)
- librosa (0.6.2)
- matplotlib (2.2.3)
- tqdm (4.25.0)
Usage
download dataset
You can download VCTK Corpus(en multi speaker)/LJ-Speech(en single speaker) very easily via my repository.
set parameters
Almost parameters in params.py and teacher_params.py are same as params.py in my other repositories like VQ-VAE. If you modified params.py in AutoregressiveWavenet, you have to replace teacher_params.py with it to train student.
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.(Now support AutoregressiveWaveNet only)
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. I highly recommend to modify -f to large number like 64. If GPU-Util is stil low, modify -p to large number like 8.
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.
Caution
I only check the results for
- Autoregressive WaveNet(Single Gaussian ver.)
- Student Gaussian IAF