Audio-Visual Alignment

1. 1
   Setup Environment

   Install necessary libraries for audio and video processing. We'll use `librosa` for audio feature extraction, `opencv-python` for video handling, and `numpy` for numerical operations. Create a virtual environment to manage dependencies.
2. 2
   Extract Audio Features

   Load an audio file and extract Mel-Frequency Cepstral Coefficients (MFCCs). MFCCs are commonly used features for audio analysis and speech recognition.
3. 3
   Extract Visual Features

   Load a video file and extract visual features from each frame. We'll use a simple approach of resizing and flattening each frame into a vector. More advanced methods could use pre-trained CNNs.
4. 4
   Temporal Alignment (Simple)

   Perform a basic temporal alignment by assuming a fixed frame rate for the video and audio. Calculate the number of audio samples per video frame and create corresponding audio segments.
5. 5
   Lip-Sync Scoring (Conceptual)

   This is a conceptual step. To score lip-sync, you would train a model (e.g., a neural network) to predict audio features from visual features (or vice versa). The prediction error would then serve as a lip-sync score. This requires a labeled dataset of aligned audio and video.

Start with short, clean audio and video clips for initial experimentation.  Ensure the audio and video are recorded simultaneously for best results.

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