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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65575
Volume:
10 |
6 |
Jun 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of computer applications
Nehru Institute of Information Technology and Management
Department of computer applications
Nehru Institute of Information Technology and Management
Video Voice Swap is an advanced artificial intelligence-based application designed to replace the original voice in a video with a target voice while maintaining natural speech quality and synchronization. The project combines modern speech processing, voice cloning, and video editing techniques to create realistic voice-swapped videos. This technology has significant applications inNcontent creation, dubbing, entertainment, education, accessibility, and multilingual communication. The system works by extracting the audio track from a video and processing it using speech recognition and voice conversion models. The original speech is analyzed to capture linguistic content, tone, and timing. A target voice model is then used to generate new speech that preserves the spoken content while changing the speaker's vocal characteristics. The generated audio is synchronized with the video and merged back into the original footage, producing a seamless voiceswapped output. The architecture of VideoVoiceSwap integrates audio extraction, speech processing, voice conversion, synchronization, and video rendering modules. These components work together to ensure high-quality results while minimizing distortion and synchronization errors. The project demonstrates the growing potential of AI in multimedia processing and human-computer interaction.
KEYWORDS
The VideoVoiceSwap: AI-Based Voice Replacement in Videos project successfully demonstrates the application of Artificial Intelligence, Deep Learning, and Speech Processing technologies in multimedia content creation. The system provides an efficient and automated solution for replacing the original voice in a video with a target voice while preserving speech content, audio quality, and synchronization with the video. By integrating voice cloning, speech recognition, voice conversion, and audio-video synchronization techniques, the system significantly reduces the time, effort, and cost associated with traditional dubbing processes. The Streamlit-based user interface offers a simple and interactive platform for uploading videos, selecting voice models, monitoring processing activities, and downloading the final output. The developed system generates realistic and high-quality voice-swapped videos, making it suitable for applications such as movie dubbing, content creation, educational videos, digital marketing, multilingual communication, and accessibility enhancement. Through comprehensive testing and evaluation, the system has proven to be reliable, user-friendly, and capable of meeting its intended objectives. In conclusion, VideoVoiceSwap represents a modern and innovative approach to AI-driven voice replacement in videos. The project highlights the growing potential of Artificial Intelligence in multimedia processing and provides a strong foundation for future enhancements such as realtime voice conversion,
[1] Corentin Jemine, "Real-Time Voice Cloning," GitHub Repository, 2018. Available: https://github.com/CorentinJ/Real-Time- VoiceCloning [2] Aäron van den Oord, Sander Dieleman, Heiga Zen, et al., "WaveNet: A Generative Model for Raw Audio," DeepMind, 2016. Available: https://arxiv.org/abs/1609.03499 [3] Dan Ruta, Adrian Bulat, Konstantinos Vougioukas, and Georgios Tzimiropoulos, "A Lip Sync Expert Is All You Need for Speech to Lip Generation in the Wild," ACM Multimedia, 2020. Available: https://arxiv.org/abs/2008.10010 [4] Ye Jia, Yu Zhang, Ron J. Weiss, et al., "Transfer Learning from Speaker Verification to Multispeaker Text-to-Speech Synthesis," NeurIPS, 2018. Available: https://arxiv.org/abs/1806.04558 [5] Ashish Vaswani, Noam Shazeer, Niki Parmar, et al., "Attention Is All You Need," NeurIPS, 2017. Available: https://arxiv.org/abs/1706.03762 [6] Alec Radford, Jong Wook Kim, Tao Xu, et al., "Whisper: Robust Speech Recognition via LargeScale Weak Supervision," OpenAI, 2022. Available: https://arxiv.org/abs/2212.04356 [7] FFmpeg Developers, "FFmpeg: Complete, Cross-Platform Solution for Audio and Video Processing." Available: https://ffmpeg.org/ [8] Streamlit Inc., "Streamlit: The Fastest Way to Build and Share
Publication Date
6/27/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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