# waveglow-cli
[](https://pypi.python.org/pypi/waveglow-cli)
[](https://pypi.python.org/pypi/waveglow-cli)
[](https://github.com/stefantaubert/waveglow/blob/main/LICENSE)
[](https://pypi.python.org/pypi/waveglow-cli)
[](https://pypi.python.org/pypi/waveglow-cli)
[](https://github.com/stefantaubert/waveglow/compare/v0.0.2...master)
[](https://doi.org/10.5281/zenodo.10569141)
Command-line interface (CLI) to train WaveGlow using .wav files.
## Features
- train/synthesize on CPU or GPU
- download pre-trained models by Nvidia
## Installation
```sh
pip install waveglow-cli --user
```
## Usage
```txt
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](https://tuc.cloud/index.php/s/yBRaWz5oHrFwigf): Adapted model trained on LJ Speech dataset by [Nvidia](https://api.ngc.nvidia.com/v2/models/nvidia/waveglow_ljs_256channels/versions/3/files/waveglow_256channels_ljs_v3.pt).
## 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](https://tuc.cloud/index.php/s/gzaYDNKinHw6GCz) (headphones recommended)
## Roadmap
- Outsource method to convert audio files to mel-spectrograms before training
- Improve logging
- Add more audio examples
- Adding tests
## Development setup
```sh
# 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
```sh
# 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:
```log
py38: commands succeeded
py39: commands succeeded
py310: commands succeeded
py311: commands succeeded
congratulations :)
```
## License
MIT License
## Acknowledgments
Model code adapted from [Nvidia](https://github.com/NVIDIA/waveglow).
Paper:
- [Waveglow: A Flow-based Generative Network for Speech Synthesis](https://ieeexplore.ieee.org/document/8683143)
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*).
```txt
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)
```
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"description": "# waveglow-cli\n\n[](https://pypi.python.org/pypi/waveglow-cli)\n[](https://pypi.python.org/pypi/waveglow-cli)\n[](https://github.com/stefantaubert/waveglow/blob/main/LICENSE)\n[](https://pypi.python.org/pypi/waveglow-cli)\n[](https://pypi.python.org/pypi/waveglow-cli)\n[](https://github.com/stefantaubert/waveglow/compare/v0.0.2...master)\n[](https://doi.org/10.5281/zenodo.10569141)\n\nCommand-line interface (CLI) to train WaveGlow using .wav files.\n\n## Features\n\n- train/synthesize on CPU or GPU\n- download pre-trained models by Nvidia\n\n## Installation\n\n```sh\npip install waveglow-cli --user\n```\n\n## Usage\n\n```txt\nusage: waveglow-cli [-h] [-v] {download,train,continue-train,validate,synthesize,synthesize-wav} ...\n\nThis program trains WaveGlow.\n\npositional arguments:\n {download,train,continue-train,validate,synthesize,synthesize-wav}\n description\n download download pre-trained checkpoint from Nvidia\n train start training\n continue-train continue training\n validate validate checkpoint(s)\n synthesize synthesize mel-spectrograms into an audio signal\n synthesize-wav synthesize audio file into an audio signal\n\noptions:\n -h, --help show this help message and exit\n -v, --version show program's version number and exit\n```\n\n## Pretrained Models\n\n- [LJS-v3-580000](https://tuc.cloud/index.php/s/yBRaWz5oHrFwigf): Adapted model trained on LJ Speech dataset by [Nvidia](https://api.ngc.nvidia.com/v2/models/nvidia/waveglow_ljs_256channels/versions/3/files/waveglow_256channels_ljs_v3.pt).\n\n## Audio Example\n\n\"The North Wind and the Sun were disputing which was the stronger, when a traveler came along wrapped in a warm cloak.\" [Listen here](https://tuc.cloud/index.php/s/gzaYDNKinHw6GCz) (headphones recommended)\n\n## Roadmap\n\n- Outsource method to convert audio files to mel-spectrograms before training\n- Improve logging\n- Add more audio examples\n- Adding tests\n\n## Development setup\n\n```sh\n# update\nsudo apt update\n# install Python 3.8-3.11 for ensuring that tests can be run\nsudo apt install python3-pip \\\n python3.8 python3.8-dev python3.8-distutils python3.8-venv \\\n python3.9 python3.9-dev python3.9-distutils python3.9-venv \\\n python3.10 python3.10-dev python3.10-distutils python3.10-venv \\\n python3.11 python3.11-dev python3.11-distutils python3.11-venv\n# install pipenv for creation of virtual environments\npython3.8 -m pip install pipenv --user\n\n# check out repo\ngit clone https://github.com/stefantaubert/waveglow.git\ncd waveglow\n# create virtual environment\npython3.8 -m pipenv install --dev\n```\n\n## Running the tests\n\n```sh\n# first install the tool like in \"Development setup\"\n# then, navigate into the directory of the repo (if not already done)\ncd waveglow\n# activate environment\npython3.8 -m pipenv shell\n# run tests\ntox\n```\n\nFinal lines of test result output:\n\n```log\npy38: commands succeeded\npy39: commands succeeded\npy310: commands succeeded\npy311: commands succeeded\ncongratulations :)\n```\n\n## License\n\nMIT License\n\n## Acknowledgments\n\nModel code adapted from [Nvidia](https://github.com/NVIDIA/waveglow).\n\nPaper:\n\n- [Waveglow: A Flow-based Generative Network for Speech Synthesis](https://ieeexplore.ieee.org/document/8683143)\n\nFunded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) \u2013 Project-ID 416228727 \u2013 CRC 1410\n\n## Citation\n\nIf you want to cite this repo, you can use the BibTeX-entry generated by GitHub (see *About => Cite this repository*).\n\n```txt\nTaubert, S. (2024). waveglow-cli (Version 0.0.2) [Computer software]. [https://doi.org/10.5281/zenodo.10569141](https://doi.org/10.5281/zenodo.10569141)\n```\n",
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