Parallel waveform generation with DiffusionGAN
- DiffWave: A Versatile Diffusion Model for Audio Synthesis, Zhifeng Kong et al., 2020. [arXiv:2009.09761]
- Tackling the Generative Learning Trilemma with Denoising Diffusion GANs, Xiao et al., 2021. [2112.07804]
Tested in python 3.7.9 conda environment.
Download LJSpeech dataset from official:keithito.
To train model, run train.py
python -m utils.dump \
--data-dir /datasets/LJSpeech-1.1 \
--output-dir /datasets/LJSpeech-1.1/vocdump \
--num-proc 8
python train.py \
--data-dir /datasets/LJSpeech-1.1/vocdump \
--from-dump
To start to train from previous checkpoint, --load-epoch is available.
python train.py \
--data-dir /datasets/LJSpeech-1.1/vocdump \
--from-dump \
--load-epoch 20 \
--config ./ckpt/t1.json
Checkpoint will be written on TrainConfig.ckpt, tensorboard summary on TrainConfig.log.
tensorboard --logdir ./log
To inference model, run inference.py
python inference.py \
--config ./ckpt/t1.json \
--ckpt ./ckpt/t1/t1_200.ckpt \
--wav /datasets/LJSpeech-1.1/audio/LJ048-0186.wav
Pretrained checkpoints are relased on releases.
To use pretrained model, download files and unzip it. Followings are sample script.
from config import Config
from dwg import DiffusionWaveGAN
with open('t1.json') as f:
config = Config.load(json.load(f))
ckpt = torch.load('t1_200.ckpt', map_location='cpu')
dwg = DiffusionWaveGAN(config.model)
dwg.load(ckpt)
dwg.eval()