This project provides a simple web application for converting audio to subtitles using OpenAI's Whisper model. It is designed for users who need a straightforward way to generate transcriptions from audio files via a web interface, suitable for local testing and development.

How It Works

The application leverages OpenAI's Whisper model to perform speech-to-text conversion. It exposes a web interface, allowing users to upload audio files and receive subtitle outputs. The architecture is a standard Python web application, likely using a framework like Flask or FastAPI, serving the Whisper model's inference capabilities.

Quick Start & Requirements

• Install/Run:
  • Clone the repository: git clone git@github.com:Kabanosk/whisper-website.git
  • Navigate to the directory: cd whisper-website
  • Create and activate a virtual environment: python3 -m venv venv then source venv/bin/activate
  • Install dependencies: pip install -r requirements.txt
  • Run the application: cd src && python3 run.py
  • Access via browser: http://127.0.0.1:8000/
• Docker:
  • Build image: docker build -t app .
  • Run container: docker run --name app_container -p 80:80 app
  • Access via browser: http://127.0.0.1:80/
• Prerequisites: Python 3, pip, Docker (for Docker installation). The Whisper model itself will have its own computational requirements (CPU/GPU).

Highlighted Details

• Provides both direct Python execution and Docker deployment options.
• Designed for local hosting and testing.
• Utilizes OpenAI's Whisper model for transcription.

Maintenance & Community

No specific information on contributors, community channels, or roadmap is provided in the README.

Licensing & Compatibility

The README does not specify a license. Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

The project appears to be a basic implementation for local use. It does not detail performance benchmarks, scalability options, or advanced features. The lack of explicit licensing information may pose a barrier to commercial adoption.