Corresponding Author: Umara Noor Abstract Recent developments in voice cloning and talking-head generation demonstrate impressive capabilities in synthesizing natural speech and realistic lip synchronization. Current methods typically require and are trained on large-scale datasets and computationally intensive processes using clean, studio-recorded inputs, which is infeasible in noisy or low-resource environments. In this paper, we introduce a new modular pipeline comprising Tortoise text to speech, a transformer-based latent diffusion model that can perform high-fidelity zero-shot voice cloning given only a few training samples, and Wav2Lip, a lightweight generative adversarial network architecture for robust real-time lip synchronization. The solution will contribute to many essential tasks concerning less reliance on massive pretraining, generation of emotionally expressive speech, and lip-sync in noisy and unconstrained scenarios. In addition, the modular structure of the pipeline allows an easy extension for future multimodal and text-guided voice modulation, and it could be used in real-world systems. Our experimental results show that the proposed system produces competition-level sound quality and lip-sync with a much smaller computational cost, indicating the possibility of deploying it in resource-constrained scenarios. Keywords Zero-Shot Voice Cloning, Latent Diffusion Models, Real-Time Lip Synchronization, GAN-Based Talking-Head Generation, Low-Resource Speech Synthesis, Emotionally Expressive Speech 1. Introduction Voice clone and talking head generation systems have made tremendous progress in the past few years, benefiting from the development of deep and generative models. These devices can be employed for virtual assistants, entertainment, telepresence, and assistive communication, making human-computer interaction more realistic and personalized, based on interactive and audio-visual context. Despite advancements, the state-of-the-art solutions heavily rely on big data and sophisticated computational resources and therefore may not be practical for real-world low-resource or noisy settings.

Voice cloning is a prominent feature in personalized speech interfaces. A neural vocal cloning system can mimic someone's voice using just a few audio samples. Both speaker encoding and speaker adaptation are topics of research in the field of voice cloning. Speaker adaptation relies on fine-tuning a multi-speaker generative model, which involves training a separate model to infer a new speaker embedding used for speaker encoding. Both methods can achieve excellent performance, even with a small number of cloning audios, in terms of the speech's naturalness and similarity to the original speaker. Speaker encoding approaches are more appropriate for low-resource deployment since they require significantly less memory and have a faster cloning time than speaker adaption, which can offer slightly greater naturalness and similarity. The main goal is to create a vocal cloning system that produces audio output with a Nepali accent or that sounds like Nepali. For the further advancement of TTS, the idea of transfer learning was effectively used to address several issues that were encountered in the development of this system, including the poor audio quality and the lack of available data.

In the rapidly evolving field of speech generative models, there is a pressing need to ensure audio authenticity against the risks of voice cloning. We present AudioSeal, the first audio watermarking technique designed specifically for localized detection of AI-generated speech. AudioSeal employs a generator/detector architecture trained jointly with a localization loss to enable localized watermark detection up to the sample level, and a novel perceptual loss inspired by auditory masking, that enables AudioSeal to achieve better imperceptibility. AudioSeal achieves state-of-the-art performance in terms of robustness to real life audio manipulations and imperceptibility based on automatic and human evaluation metrics. Additionally, AudioSeal is designed with a fast, single-pass detector, that significantly surpasses existing models in speed - achieving detection up to two orders of magnitude faster, making it ideal for large-scale and real-time applications.

The model can take only a few seconds of audio, and realistically generate new audio.