waveglow-cli 0.0.2

Description:

waveglowcli 0.0.2

waveglow-cli







Command-line interface (CLI) to train WaveGlow using .wav files.
Features

train/synthesize on CPU or GPU
download pre-trained models by Nvidia

Installation
pip install waveglow-cli --user

Usage
usage: waveglow-cli [-h] [-v] {download,train,continue-train,validate,synthesize,synthesize-wav} ...

This program trains WaveGlow.

positional arguments:
{download,train,continue-train,validate,synthesize,synthesize-wav}
description
download download pre-trained checkpoint from Nvidia
train start training
continue-train continue training
validate validate checkpoint(s)
synthesize synthesize mel-spectrograms into an audio signal
synthesize-wav synthesize audio file into an audio signal

options:
-h, --help show this help message and exit
-v, --version show program's version number and exit

Pretrained Models

LJS-v3-580000: Adapted model trained on LJ Speech dataset by Nvidia.

Audio Example
"The North Wind and the Sun were disputing which was the stronger, when a traveler came along wrapped in a warm cloak." Listen here (headphones recommended)
Roadmap

Outsource method to convert audio files to mel-spectrograms before training
Improve logging
Add more audio examples
Adding tests

Development setup
# update
sudo apt update
# install Python 3.8-3.11 for ensuring that tests can be run
sudo apt install python3-pip \
python3.8 python3.8-dev python3.8-distutils python3.8-venv \
python3.9 python3.9-dev python3.9-distutils python3.9-venv \
python3.10 python3.10-dev python3.10-distutils python3.10-venv \
python3.11 python3.11-dev python3.11-distutils python3.11-venv
# install pipenv for creation of virtual environments
python3.8 -m pip install pipenv --user

# check out repo
git clone https://github.com/stefantaubert/waveglow.git
cd waveglow
# create virtual environment
python3.8 -m pipenv install --dev

Running the tests
# first install the tool like in "Development setup"
# then, navigate into the directory of the repo (if not already done)
cd waveglow
# activate environment
python3.8 -m pipenv shell
# run tests
tox

Final lines of test result output:
py38: commands succeeded
py39: commands succeeded
py310: commands succeeded
py311: commands succeeded
congratulations :)

License
MIT License
Acknowledgments
Model code adapted from Nvidia.
Paper:

Waveglow: A Flow-based Generative Network for Speech Synthesis

Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 416228727 – CRC 1410
Citation
If you want to cite this repo, you can use the BibTeX-entry generated by GitHub (see About => Cite this repository).
Taubert, S. (2024). waveglow-cli (Version 0.0.2) [Computer software]. [https://doi.org/10.5281/zenodo.10569141](https://doi.org/10.5281/zenodo.10569141)