multimodal-fusion

Multimodal Fusion for Speaker Diarization

Overview

When working with video files, you can significantly improve speaker diarization by combining audio features with visual features like face detection and lip movement analysis.

When to Use

• Processing video files (not just audio)
• Multiple speakers visible on screen
• Need to disambiguate speakers with similar voices
• Improve accuracy by leveraging visual cues

Visual Feature Extraction

Face Detection

import cv2
import numpy as np

# Initialize face detector
face_cascade = cv2.CascadeClassifier(
    cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
)

# Process video frames
cap = cv2.VideoCapture(video_path)
fps = cap.get(cv2.CAP_PROP_FPS)
faces_by_time = {}

frame_count = 0
frame_skip = max(1, int(fps / 2))  # Process every other frame

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break

    if frame_count % frame_skip == 0:
        timestamp = frame_count / fps
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        faces = face_cascade.detectMultiScale(gray, 1.1, 4)
        faces_by_time[timestamp] = len(faces)

    frame_count += 1