| Issue |
E3S Web Conf.
Volume 698, 2026
First International Conference on Research and Advancements in Electronics, Energy, and Environment (ICRAEEE 2025)
|
|
|---|---|---|
| Article Number | 01016 | |
| Number of page(s) | 7 | |
| Section | Electrical and Electronic Engineering | |
| DOI | https://doi.org/10.1051/e3sconf/202669801016 | |
| Published online | 16 March 2026 | |
An AI-Driven Microservices Framework for Scalable Personalized Marketing Video Generation with Neural Voice Cloning
1 STIC Laboratory, Faculty of Sciences, Chouaib Doukkali University, 24000 El Jadida, Morocco
2 Department of Computer Science, Ecole Supérieure de Technologie (EST), Ibn Zohr University, 81000 Guelmim, Morocco
* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
Personalized marketing content has become a key driver for customer engagement in modern digital platforms. However, generating customized video content at scale remains a significant challenge due to the need for dynamic adaptation and automation. The proposed pipeline leverages Whisper-based speech transcription and Coqui TTS voice cloning to perform CSV driven keyword replacement, enabling the automatic generation and delivery of one personalized video per client entry. This paper proposes an automated and scalable system for generating personalized marketing videos based on structured client profiles and predefined multimedia templates. The approach integrates client data preprocessing, dynamic content selection, and automated video composition within a unified framework. Experimental validation confirms the feasibility and robustness of the proposed system, demonstrating its capability to efficiently generate customized marketing videos while ensuring scalability and flexibility for real-world deployment.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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