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Clone the repository:\n\n    ```bash\n    git clone https://github.com/yourusername/VideoAudioSummarizationApp.git\n    cd VideoAudioSummarizationApp\n    ```\n\n2. Install dependencies:\n\n    ```bash\n    pip install -r requirements.txt\n    ```\n\n3. Run the Streamlit app:\n\n    ```bash\n    streamlit run app.py\n    ```\n\n## Usage\n\n1. Open the application in your browser (typically at `http://localhost:8501`).\n   \n2. Upload a video or audio file in **.mp4**, **.wav**, or **.mp3** format.\n\n3. The app will extract audio (if a video file is uploaded), transcribe it, and display the transcription.\n\n4. View the summarized content in the Summary section.\n\n## Adding Audio/Video Samples to GitHub\n\nTo add sample audio or video files to GitHub:\n\n1. Place sample files in a directory within the project, such as `sample_files/`.\n2. In your README, provide links to these files for easy access.\n3. Use these sample files for demo purposes or to facilitate testing and contributions.\n\n## Built With\n\n- [Python](https://www.python.org/) - The programming language used.\n- [Streamlit](https://streamlit.io/) - For the interactive web application.\n- [Hugging Face Transformers](https://huggingface.co/) - For speech-to-text and summarization models.\n- [Librosa](https://librosa.org/) - For audio processing.\n\n## Contributing\n\nContributions are welcome! To contribute, please submit a pull request and follow the standard GitHub workflow.\n\n## Acknowledgments\n\n- Hugging Face community for providing state-of-the-art NLP models.\n- Inspiration from various NLP resources for implementing the summarization feature.\n\n## Author\n\nKush Kalsi\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkushxkalsi%2Fvideo-audio-summarization-app","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkushxkalsi%2Fvideo-audio-summarization-app","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkushxkalsi%2Fvideo-audio-summarization-app/lists"}