About This Project
This project was developed as a solution to a problem statement given by AMAGI, a leading media technology company.
It implements a deep learning-based audio-visual synchronization detection system using the SyncNet architecture. The system analyzes video content to detect lip-sync errors and dubbing mismatches by comparing audio signals with visual lip movements. It uses a Fully Convolutional Network (FCN) approach to process video frames and audio spectrograms, generating synchronization confidence scores that indicate whether the audio and video are properly aligned.
Key Features
- Detects lip-sync errors in video content with high accuracy
- Uses SyncNet FCN architecture for temporal alignment analysis
- Processes both audio waveforms and video frames simultaneously
- Generates frame-by-frame synchronization confidence scores
- Identifies dubbing mismatches in movies and TV shows
- Supports batch processing of multiple video files
Technology Stack
- Python for core development
- PyTorch/TensorFlow for deep learning implementation
- SyncNet architecture with FCN modifications
- OpenCV for video frame extraction
- Librosa for audio processing and spectrogram generation
- NumPy for numerical computations