leoauri/waveflow

A PyTorch implementation of "WaveFlow: A Compact Flow-based Model for Raw Audio"

This is a hack of the repo to enable installation in a pip environment and import.

WaveFlow: A Compact Flow-based Model for Raw Audio

Update: Pretrained weights are now available. See links below.

This is an unofficial PyTorch implementation of WaveFlow (Ping et al, ICML 2020) model.

The aim for this repo is to provide easy-to-use PyTorch version of WaveFlow as a drop-in alternative to various neural vocoder models used with NVIDIA's Tacotron2 audio processing backend.

Please refer to the official implementation written in PaddlePaddle for the official results.

Setup

1. Clone this repo and install requirements

   git clone https://github.com/L0SG/WaveFlow.git
   cd WaveFlow
   pip install -r requirements.txt

2. Install Apex for mixed-precision training

Train your model

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

   -n+10 and -n10 indicates that this example reserves the first 10 audio clips for model testing.

3. Edit the configuration file and train the model.

   Below are the example commands using waveflow-h16-r64-bipartize.json

   nano configs/waveflow-h16-r64-bipartize.json
   python train.py -c configs/waveflow-h16-r64-bipartize.json

   Single-node multi-GPU training is automatically enabled with DataParallel (instead of DistributedDataParallel for simplicity).

   For mixed precision training, set "fp16_run": true on the configuration file.

   You can load the trained weights from saved checkpoints by providing the path to checkpoint_path variable in the config file.

   checkpoint_path accepts either explicit path, or the parent directory if resuming from averaged weights over multiple checkpoints.

   Examples

   insert checkpoint_path: "experiments/waveflow-h16-r64-bipartize/waveflow_5000" in the config file then run

   python train.py -c configs/waveflow-h16-r64-bipartize.json

   for loading averaged weights over 10 recent checkpoints, insert checkpoint_path: "experiments/waveflow-h16-r64-bipartize" in the config file then run

   python train.py -a 10 -c configs/waveflow-h16-r64-bipartize.json

   you can reset the optimizer and training scheduler (and keep the weights) by providing --warm_start

   python train.py --warm_start -c configs/waveflow-h16-r64-bipartize.json

4. Synthesize waveform from the trained model.

   insert checkpoint_path in the config file and use --synthesize to train.py. The model generates waveform by looping over test_files.txt.

   python train.py --synthesize -c configs/waveflow-h16-r64-bipartize.json

   if fp16_run: true, the model uses FP16 (half-precision) arithmetic for faster performance (on GPUs equipped with Tensor Cores).

Pretrained Weights

We provide pretrained weights via Google Drive. The models are trained for 5 M steps, then we averaged weights over 20 last checkpoints with -a 20. Audio quality almost matches the original paper.

Models	Download
waveflow-h16-r64-bipartize	Link
waveflow-h16-r128-bipartize	Link

Reference

NVIDIA Tacotron2: https://github.com/NVIDIA/waveglow

NVIDIA WaveGlow: https://github.com/NVIDIA/waveglow

r9y9 wavenet-vocoder: https://github.com/r9y9/wavenet_vocoder

FloWaveNet: https://github.com/ksw0306/FloWaveNet

Parakeet: https://github.com/PaddlePaddle/Parakeet