In our recent paper, we propose WaveGlow: a flow-based network capable of generating high quality speech from mel-spectrograms. WaveGlow combines insights from Glow and WaveNet in order to provide fast, efficient and high-quality audio synthesis, without the need for auto-regression. WaveGlow is implemented using only a single network, trained using only a single cost function: maximizing the likelihood of the training data, which makes the training procedure simple and stable.

Our PyTorch implementation produces audio samples at a rate of 1200 kHz on an NVIDIA V100 GPU. Mean Opinion Scores show that it delivers audio quality as good as the best publicly available WaveNet implementation.

Visit our website for audio samples.

1. Clone our repo and initialize submodule

   git clone https://github.com/NVIDIA/waveglow.git
   cd waveglow
   git submodule init
   git submodule update

2. Install requirements (pytorch 1.0 and others) pip3 install -r requirements.txt

1. Download our published model
2. Download mel-spectrograms
3. Generate audio python3 inference.py -f <(ls mel_spectrograms/*.pt) -w waveglow_old.pt -o . --is_fp16 -s 0.6

N.b. use convert_model.py to convert your older models to the current model with fused residual and skip connections.

1. Download LJ Speech Data. In this example it's in data/

2. Make a list of the file names to use for training/testing

   ls data/*.wav | tail -n+10 > train_files.txt
   ls data/*.wav | head -n10 > test_files.txt

3. Train your WaveGlow networks

   mkdir checkpoints
   python train.py -c config.json

   For multi-GPU training replace train.py with distributed.py. Only tested with single node and NCCL.

4. Make test set mel-spectrograms

   python mel2samp.py -f test_files.txt -o . -c config.json

5. Do inference with your network

   ls *.pt > mel_files.txt
   python3 inference.py -f mel_files.txt -w checkpoints/waveglow_10000 -o . --is_fp16 -s 0.6