Skip to content
A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis
Branch: master
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
waveglow
.gitignore Initial commit Nov 4, 2018
AUTHORS Initial commit Nov 4, 2018
LICENSE Initial commit Nov 4, 2018
README.md offical -> official Nov 6, 2018
generate.py Initial commit Nov 4, 2018
requirements.txt Initial commit Nov 4, 2018
train.py
waveglow_params.json Initial commit Nov 4, 2018

README.md

WaveGlow

A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis

Quick Start:

  1. Install requirements:
pip install -r requirements.txt
  1. Download dataset:
wget http://festvox.org/cmu_arctic/cmu_arctic/packed/cmu_us_slt_arctic-0.95-release.tar.bz2
tar xf cmu_us_slt_arctic-0.95-release.tar.bz2
  1. Extract features: feature extracting pipeline is the same as tacotron

  2. Training with default hyperparams:

python train.py
  1. Synthesize from model:
python generate.py --checkpoint=/path/to/model --local_condition_file=/path/to/local_conditon

Notes:

  • This is not official implementation, some details are not necessarily correct.
  • Work in progress.
You can’t perform that action at this time.