This is a PyTorch implementation of our work "FloWaveNet : A Generative Flow for Raw Audio". (We'll update soon.)
For a purpose of parallel sampling, we propose FloWaveNet, a flow-based generative model for raw audio synthesis. FloWaveNet can generate audio samples as fast as ClariNet and Parallel WaveNet, while the training procedure is really easy and stable with a single-stage pipeline. Our generated audio samples are available at https://ksw0306.github.io/flowavenet-demo/. Also, our implementation of ClariNet (Gaussian WaveNet and Gaussian IAF) is available at https://github.com/ksw0306/ClariNet
python preprocessing.py --in_dir ljspeech --out_dir DATASETS/ljspeech
python train.py --model_name flowavenet --batch_size 2 --n_block 8 --n_flow 6 --n_layer 2 --block_per_split 4
python train.py --model_name flowavenet --batch_size 8 --n_block 8 --n_flow 6 --n_layer 2 --block_per_split 4 --num_gpu 4
NVIDIA TITAN V (12GB VRAM) : batch size 2 per GPU
NVIDIA Tesla V100 (32GB VRAM) : batch size 8 per GPU
--load_step CHECKPOINT
: the # of the pre-trained model's global training step (also depicted in the trained weight file)
--temp
: Temperature (standard deviation) value implemented as z ~ N(0, 1 * TEMPERATURE^2)
ex) python synthesize.py --model_name flowavenet --n_block 8 --n_flow 6 --n_layer 2 --load_step 100000 --temp 0.8 --num_samples 10 --block_per_split 4
Sample Link : https://ksw0306.github.io/flowavenet-demo/
Our implementation of ClariNet (Gaussian WaveNet, Gaussian IAF) : https://github.com/ksw0306/ClariNet
Results 1 : Model Comparisons (WaveNet (MoL, Gaussian), ClariNet and FloWaveNet)
Results 2 : Temperature effect on Audio Quality Trade-off (Temperature T : 0.0 ~ 1.0, Model : FloWaveNet)
Results 3 : Analysis of ClariNet Loss Terms (Loss functions : 1. Only KL 2. KL + Frame 3. Only Frame)
Results 4 : Causality of WaveNet Dilated Convolutions (FloWaveNet : Non-causal WaveNet Affine Coupling Layers, FloWaveNet_causal : Causal WaveNet Affine Coupling Layers)