https://github.com/heatingma/vidfetch

An open-source dataset download tool to obtain copyright-free videos from various free video websites.
https://github.com/heatingma/vidfetch

fetch free video web-scrapping

Last synced: about 2 months ago
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An open-source dataset download tool to obtain copyright-free videos from various free video websites.

• Host: GitHub
• URL: https://github.com/heatingma/vidfetch
• Owner: heatingma
• Created: 2024-03-15T12:48:40.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-04-16T06:11:17.000Z (9 months ago)
• Last Synced: 2024-11-11T18:51:19.136Z (about 2 months ago)
• Topics: fetch, free, video, web-scrapping
• Language: Python
• Homepage:
• Size: 6.7 MB
• Stars: 4
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        






[![PyPi version](https://badgen.net/pypi/v/vidfetch/)](https://pypi.org/pypi/vidfetch/) [![PyPI pyversions](https://img.shields.io/badge/dynamic/json?color=blue&label=python&query=info.requires_python&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fvidfetch%2Fjson)](https://pypi.python.org/pypi/vidfetch/) [![Downloads](https://static.pepy.tech/badge/vidfetch)](https://pepy.tech/project/vidfetch) [![GitHub stars](https://img.shields.io/github/stars/heatingma/VidFetch.svg?style=social&label=Star&maxAge=8640)](https://GitHub.com/heatingma/VidFetch/stargazers/) 

``Latte``, **a novel latent diffusion transformer for video generation**, utilizes spatio-temporal tokens extracted from input videos and employs a series of Transformer blocks to model the distribution of videos in the latent space. ``Latte`` achieves state-of-the-art performance on four standard video generation datasets ``FaceForensics``, ``SkyTimelapse``, ``UCF101``, and ``Taichi-HD``. [paper](https://arxiv.org/pdf/2401.03048v1.pdf), [code](https://github.com/Vchitect/Latte?tab=readme-ov-file), [pretrained](https://huggingface.co/maxin-cn/Latte)

However, ``Latte`` still falls short in terms of video generation length and quality compared to ``Sora``. To achieve training and generation effects close to Sora, the Latte model requires more high-quality text-video paired datasets. Therefore, we have created ``VidFetch``, an open-source dataset download tool to obtain copyright-free videos from various free video websites.

## Free Video Support

| website | windows | macos | linux |

| :-----: | :-----: | :---: | :---: |

| [Pexels](https://www.pexels.com) | ✔ | 📆 | 📆 |

| [Mazwai](https://mazwai.com/stock-video-footage) | 📆 | 📆 | 📆 |

| [Mixkit](https://mixkit.co/free-stock-video) | ✔ | 📆 | ✔ |

| [Pixabay](https://pixabay.com/videos/search/?order=ec) | ✔ | 📆 | 📆 |

| [Coverr](https://coverr.co/stock-video-footage) | 📆 | 📆 | 📆 |

## Installation

You can install the stable release on PyPI:

```bash

$ pip install vidfetch

```

or get the latest version by running:

```bash

$ pip install -U https://github.com/heatingma/VidFetch/archive/master.zip # with --user for user install (no root)

```

The following packages are required, and shall be automatically installed by ``pip``:

```

aiohttp>=3.9.3,

async_timeout>=4.0.3

tqdm>=4.66.2

texttable>=1.7.0

moviepy>=1.0.3

bs4>=0.0.2

selenium>=4.18.1

requests>=2.31.0

texttable>=1.7.0

huggingface_hub>=0.22.2

```

## Usage Examples

### website

**You only need three lines of code to start downloading the video**

```python

from vidfetch.website import MixkitVideoDataset

mixkit = MixkitVideoDataset(root_dir="mixkit")

mixkit.download(platform="windows")

```