Overview

The project is designed to automate the process of summarizing videos by analyzing their audio content and intelligently adjusting playback speed based on the perceived loudness. This can be particularly useful for skipping through less relevant sections, such as silent periods or segments where only background noise is present. The project is divided into several components, including audio processing, machine learning model training, video summarization, and indexing.

Components

1. video_summarizer.py

This script serves as the main entry point for video summarization. It utilizes modules for audio processing, machine learning, and indexing to process a given video file. The process involves extracting audio features, predicting loudness using a trained model, and indexing the video for future reference.

2. audio_processing.py

Responsible for handling audio-related tasks, this module extracts features from audio chunks of the video. It uses the PyDub library to convert the video's audio into manageable segments and extracts basic features. More sophisticated feature extraction methods can be implemented based on project requirements.

3. model.py

Handles the training and utilization of the machine learning model. Currently, a Random Forest Classifier is used for predicting loudness. The model is trained on simulated data representing loud and silent audio segments. It can be expanded and replaced with more advanced models for improved accuracy.

4. indexing.py

Deals with indexing videos for efficient future access. It checks whether a video has been previously indexed, and if not, it generates an index file containing the video's timestamp and corresponding loudness predictions. This index is then sent to a server for future reference.

Usage

1. Training the Model:

   • Run video_summarizer.py.
   • The script will check if the machine learning model has been trained. If not, it will train the model using simulated data.

2. Summarizing a Video:

   • Specify the path to the video in video_summarizer.py.
   • Run the script to extract audio features, predict loudness, and adjust playback speed.

3. Server Interaction:

   • The script communicates with a server (https://videosummarizer.soptik.tech/) for video indexing.
   • Simulated requests are used for illustration purposes. Actual server interaction depends on the project's implementation.

Project Structure

|-- video_summarizer.py
|-- audio_processing.py
|-- model.py
|-- indexing.py
|-- model.pkl
|-- data/
|   |-- simulated_data.npy
|   |-- ...
|-- documentation/
|   |-- README.md
|   |-- ...

Dependencies

• PyDub
• NumPy
• scikit-learn
• requests

Conclusion

The Video Summarizer project provides a foundation for automating the video summarization process. It combines audio processing, machine learning, and indexing to intelligently adjust playback speed based on audio content. While the current implementation uses simulated data, the project can be extended with real-world data and more advanced features for accurate and efficient video summarization.