syncnet-python 0.1.1

SyncNet: Audio-visual synchronization detection using deep learning. Updated version of https://github.com/joonson/syncnet_python for modern Python versions.

SyncNet Python

Audio-visual synchronization detection using deep learning.

This is an updated version of the original SyncNet implementation by Joon Son Chung, compatible with modern Python versions (3.9+).

Overview

SyncNet Python is a PyTorch implementation of the SyncNet model, which detects audio-visual synchronization in videos. It can identify lip-sync errors by analyzing the correspondence between mouth movements and spoken audio.

Features

• 🎥 Audio-Visual Sync Detection: Accurately detect synchronization between audio and video
• 🔍 Face Detection: Automatic face detection and tracking using S3FD
• 🚀 Batch Processing: Process multiple videos efficiently
• 🐍 Python API: Easy-to-use Python interface
• 📊 Confidence Scores: Get confidence metrics for sync quality

Installation

pip install syncnet-python

Additional Requirements

1. FFmpeg: Required for video processing

   # Ubuntu/Debian
   sudo apt-get install ffmpeg
   
   # macOS
   brew install ffmpeg
   

2. Model Weights: Download pre-trained weights

   • Download sfd_face.pth and syncnet_v2.model
   • Place them in a weights/ directory

Quick Start

from syncnet_python import SyncNetPipeline

# Initialize pipeline
pipeline = SyncNetPipeline(
    s3fd_weights="weights/sfd_face.pth",
    syncnet_weights="weights/syncnet_v2.model",
    device="cuda"  # or "cpu"
)

# Process video
results = pipeline.inference(
    video_path="video.mp4",
    audio_path=None  # Extract from video
)

# Get results
offset, confidence = results['offset'], results['confidence']
print(f"AV Offset: {offset} frames")
print(f"Confidence: {confidence:.3f}")

Command Line Usage

# Process single video
syncnet-python video.mp4

# Process multiple videos
syncnet-python video1.mp4 video2.mp4 --output results.json

# Use CPU instead of GPU
syncnet-python video.mp4 --device cpu

Requirements

• Python 3.9+
• PyTorch 2.0+
• CUDA (optional but recommended)
• FFmpeg

Credits

This package is based on the original SyncNet implementation by Joon Son Chung.

Citation

If you use this code in your research, please cite the original paper:

@inproceedings{chung2016out,
  title={Out of time: automated lip sync in the wild},
  author={Chung, Joon Son and Zisserman, Andrew},
  booktitle={Asian Conference on Computer Vision},
  year={2016}
}

License

MIT License - see LICENSE file for details.

Links

• GitHub: https://github.com/yourusername/syncnet-python
• Documentation: https://syncnet-python.readthedocs.io
• Issues: https://github.com/yourusername/syncnet-python/issues