semvec/pypi-packages huggingface dataset:
auto-subtitle 1.0.1
Automatically generate and embed subtitles into your videos

# Automatic subtitles in your videos

This repository uses `ffmpeg` and [OpenAI's Whisper](https://openai.com/blog/whisper) to automatically generate and overlay subtitles on any video.

## Installation

To get started, you'll need Python 3.7 or newer. Install the binary by running the following command:

    pip install git+https://github.com/m1guelpf/auto-subtitle.git

You'll also need to install [`ffmpeg`](https://ffmpeg.org/), which is available from most package managers:

```bash
# on Ubuntu or Debian
sudo apt update && sudo apt install ffmpeg

# on MacOS using Homebrew (https://brew.sh/)
brew install ffmpeg

# on Windows using Chocolatey (https://chocolatey.org/)
choco install ffmpeg
```

## Usage

The following command will generate a `subtitled/video.mp4` file contained the input video with overlayed subtitles.

    auto_subtitle /path/to/video.mp4 -o subtitled/

The default setting (which selects the `small` model) works well for transcribing English. You can optionally use a bigger model for better results (especially with other languages). The available models are `tiny`, `tiny.en`, `base`, `base.en`, `small`, `small.en`, `medium`, `medium.en`, `large`.

    auto_subtitle /path/to/video.mp4 --model medium

Adding `--task translate` will translate the subtitles into English:

    auto_subtitle /path/to/video.mp4 --task translate

Run the following to view all available options:

    auto_subtitle --help

## License

This script is open-source and licensed under the MIT License. For more details, check the [LICENSE](LICENSE) file.
WebScraperAPI 1.0.4
A web scraping API that allows users to easily extract data from any website by simply providing the URL. The API utilizes advanced parsing and data storage techniques to ensure accurate and efficient data extraction. The package is easy to install and use, making it perfect for data scientists, researchers, and developers looking to quickly and easily access web data.
Requires Python: >=3.6

# WebScraperAPI

A web scraping API that allows users to easily extract data from any website by simply providing the URL. The API utilizes advanced parsing and data storage techniques to ensure accurate and efficient data extraction. The package is easy to install and use, making it perfect for data scientists, researchers, and developers looking to quickly and easily access web data.

## Installation

To install the package, use pip:

`pip install WebScraperAPI`

## Usage

You can use the package by importing it in your code and using the `scrape` function.

```python
from WebScraperAPI.scraper import Scraper
from WebScraperAPI.parser import Parser
from WebScraperAPI.data_storage import DataStorage

url = 'https://example.com'

# Scrape the data from the website
scraper_obj = Scraper(url)
data = scraper_obj.get_html()

if data:
    # Parse the data
    parser_obj = Parser(data)
    parsed_data = parser_obj.parse_data()

    # Save the data to a CSV file
    file_name = 'example_output.csv'
    data_storage_obj = DataStorage(parsed_data, file_name)
    data_storage_obj.save_data()
else:
    print("An error occurred while scraping the website")
```

## Dependencies
```
beautifulsoup4
requests
```
## Contributing
If you want to contribute to this project, please fork the repository and make a pull request.

## License
This project is licensed under the MIT License.
amp-atomistics 1.0.1
Atomistic Machine-learning Package

# Amp: Atomistic Machine-learning Package #


*Amp* is an open-source package designed to easily bring machine-learning to atomistic calculations. This project is being developed at Brown University in the School of Engineering, primarily by Andrew Peterson and Alireza Khorshidi, and is released under the GNU General Public License.  *Amp* allows for the modular representation of the potential energy surface, enabling the user to specify or create descriptor and regression methods.

This project lives at:
https://bitbucket.org/andrewpeterson/amp

Documentation lives at:
http://amp.readthedocs.org

Users' mailing list lives at:
https://listserv.brown.edu/?A0=AMP-USERS
(Subscribe page:)
https://listserv.brown.edu/?SUBED1=AMP-USERS&A=1

If you would like to compile a local version of the documentation, see the README file in the docs directory.


License
=======

 This program is free software: you can redistribute it and/or modify
 it under the terms of the GNU General Public License as published by
 the Free Software Foundation, either version 3 of the License, or
 (at your option) any later version.

 This program is distributed in the hope that it will be useful,
 but WITHOUT ANY WARRANTY; without even the implied warranty of
 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 GNU General Public License for more details.

 You should have received a copy of the GNU General Public License
 along with this program.  If not, see <http://www.gnu.org/licenses/>.


Installation
============

You can find the installation instructions for this version of Amp in the
documentation file `docs/installation.rst`.

Documentation
=============

We currently host multiple versions of the documentation, which includes
installation instructions, at http://amp.readthedocs.io. 

You can build a local copy of the documentation for this version of Amp.
You will find instructions to do this in the "Documentation" section of the
file `docs/develop.rst`.