International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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An Offline Modular System for Profanity Detection and Speaker Diarization in Movies and Video Clips Using Whisper and PyAnnote

Author(s) Mr. Rusheil Singh Baath, Mr. Kushal Rao Meesala, Mr. Jatin Umakant Garad, Ms. Samruddhi Sahane, Prof. Sarika Bobde, Mr. Umang Tiwari
Country India
Abstract With the explosive growth of multimedia content online, detecting inappropriate language in videos has become vital for compliance, moderation, and accessibility. This paper presents an offline, modular system that performs profanity detection in English-language movie clips using OpenAI's Whisper (for transcription) and PyAnnote (for speaker diarization). Implemented as both a Streamlit GUI (app.py) and a CLI module (final_gpu.py), the system extracts audio, segments speakers, transcribes dialogue, and identifies cuss words using a lemmatization-based filter. Our method supports speaker-gender mapping and outputs visual analyses to compare profanity trends. Evaluation on selected English-language movies from 2010 to 2020 reveals strong performance, achieving 94.8% accuracy, 93.4% F1-score, and effective profanity segmentation across speakers. Though not designed for real-time use, the system serves as a powerful post-processing tool for media editors, educators, and researchers analyzing language trends and compliance risks.
Keywords Profanity Detection, Whisper, PyAnnote, Speech-to-Text, Audio Transcription, Content Moderation, Gender-based Language Analysis, Streamlit Visualization
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-05-11
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E-ISSN 2582-2160
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