AutoCut: AI Video Editing 200% Faster | Text-Based Tool

AutoCut is an innovative open-source AI tool developed by Mu Li, a senior principal scientist at Amazon and Ph.D. graduate from Carnegie Mellon University’s Computer Science department. This groundbreaking software allows users to edit videos using a text editor, revolutionizing the traditional video editing process.

Key Features of AutoCut

1. Flexible Whisper Model Selection: Users can choose from different Whisper models to suit their transcription needs.
2. Versatile Processing Options: AutoCut supports both CPU inference and GPU acceleration, catering to various hardware setups.
3. Batch Processing Capability: The tool can transcribe multiple videos in a specified folder, streamlining workflow for large projects.
4. Online and Offline Functionality: While it can work offline, AutoCut also supports online OpenAI Whisper models for enhanced performance.
5. Multiple Export Formats: The tool can export transcriptions in both SRT and Markdown formats, offering flexibility in post-processing.
6. Markdown-Based Editing: Any Markdown software can be used for video editing, providing a familiar interface for many users.

Getting Started with AutoCut

Installation Options

1. Python Package Installation:

   pip install git+https://github.com/mli/autocut.git

2. Local Installation:

• Clone the repository: git clone https://github.com/mli/autocut
• Navigate to the project directory: cd autocut
• Install dependencies: pip install .

2. Docker Installation:

• For CPU version:
  docker build -t autocut . docker run -it --rm -v /path/to/your/videos:/autocut/video autocut /bin/bash
• For GPU version (requires NVIDIA GPU):
  docker build -f ./Dockerfile.cuda -t autocut-gpu . docker run --gpus all -it --rm -v /path/to/your/videos:/autocut/video autocut-gpu

Additional Requirements

Install FFmpeg, which is essential for video processing. Installation commands vary by operating system (e.g., sudo apt install ffmpeg for Ubuntu/Debian).

Using AutoCut

Once installed, you can start leveraging AutoCut’s powerful features:

1. Transcribing a Video:

   autocut -t video_file.mp4 --whisper-model large

Note: The default Whisper model is ‘small’. For better quality, use ‘medium’ or ‘large’ models.

2. Editing a Video:

   autocut -c video_file.mp4 video_file.srt video_file.md

If you’re not familiar with Markdown, you can edit the SRT file directly and omit the MD file in the command.

Project Structure

The AutoCut project is organized as follows:

autocut
│  .gitignore
│  LICENSE
│  README.md
│  setup.py
│
└─autocut
   │  cut.py
   │  daemon.py
   │  main.py
   │  transcribe.py
   │  utils.py
   └─ __init__.py

The core functionality is implemented in the autocut folder, where new features and modifications are typically made.

Conclusion

AutoCut represents a significant advancement in video editing technology, offering a text-based approach that can dramatically improve editing efficiency. Whether you’re a professional video editor or a content creator, AutoCut provides a powerful set of tools to enhance your capabilities. As an open-source project, it invites collaboration and improvement from the developer community, ensuring its continued growth and adaptation to user needs.

For more information and to contribute to the project, visit the AutoCut GitHub repository.