A Tensorflow implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis( https://arxiv.org/abs/1811.00002)
- Set parameters in hparam.py
python process.py --wav_dir='wavs' --output='data'
train.py
is the entry point:
$ python train.py--wave_dir="data/train/audio" --lc_dir="data/train/mel"
Trained models are saved under the logdir/waveglow
directory.
generate.py
is the entry point:
$ python generate.py --lc_dir="data/test/mel" --out_dir="samples" --restore_from="logdir/waveglow"
- baseline model is trained using data form here(https://weixinxcxdb.oss-cn-beijing.aliyuncs.com/gwYinPinKu/BZNSYP.rar)