rafaykhattak/youtubegpt

YoutubeGPT is a web application powered by OpenAI's Whisper model for speech recognition and GPT-3 for text summarization. It extracts transcriptions from YouTube videos and provides users with concise summaries based on the video content.

YoutubeGPT

YoutubeGPT is a web application that utilizes OpenAI's state-of-the-art models to enhance the accessibility and comprehension of YouTube videos. By leveraging the power of Whisper, a general-purpose speech recognition model, and GPT-3, a language model capable of generating human-like text, YoutubeGPT extracts transcriptions from YouTube videos and provides users with concise and accurate summaries based on the video content.

Features

• Speech Recognition: YoutubeGPT employs OpenAI's Whisper model to transcribe the audio content of YouTube videos, ensuring accurate and reliable speech-to-text conversion.

• Summarization: Leveraging GPT-3, YoutubeGPT generates concise summaries of the video content, enabling users to quickly grasp the main points without having to watch the entire video.

• User-Friendly Interface: The web application offers a user-friendly interface where users can simply enter the URL of the YouTube video they want to process and receive the transcription and summarized text promptly.

Getting Started

To run YoutubeGPT locally, follow these steps:

1. Clone this repository to your local machine.

2. Install the required dependencies by running pip install -r requirements.txt.

3. Install ffmpeg, which is required for audio processing. You can download ffmpeg from the official website (https://ffmpeg.org/) and add it to the environment variable path.

4. Set up your OpenAI API key by following the instructions in the .env.example file.

5. Run the application using the command streamlit run app.py.

6. Access the web application in your browser at http://localhost:8501.

Technologies Used

• Python
• OpenAI Whisper
• OpenAI GPT-3
• Streamlit

Limitations

Please note the following limitations of YoutubeGPT:

• The accuracy of speech recognition may vary depending on the quality of audio in the YouTube video.
• GPT-3 summarization is based on the extracted transcription and may not capture all nuances or context from the video.
• Due to OpenAI API limits, there may be restrictions on the number of API calls that can be made.

License

YoutubeGPT is licensed under the MIT License. Feel free to use, modify, and distribute the code as per the terms of the license.

Acknowledgements

• This project utilizes OpenAI's Whisper and GPT-3 models. Special thanks to OpenAI for providing access to these powerful AI technologies.

• The web application is built using Streamlit, a fantastic framework for creating interactive data applications in Python.