Technological advancements have led to innovations in audio generation, particularly in high-fidelity audio synthesis. However, existing models often struggle with spectral discontinuities and lack clarity in higher frequencies, hindering the production of realistic audio. Researchers have introduced the Enhanced Various Audio Generation via Scalable Generative Adversarial Networks (EVA-GAN), a model that addresses these challenges. EVA-GAN leverages a large dataset of high-fidelity audio and incorporates a Context Aware Module to improve spectral and high-frequency reconstruction. The model also includes a Human-In-The-Loop artifact measurement toolkit to align the generated audio with human perceptual standards. EVA-GAN outperforms existing models in terms of robustness and quality, achieving higher scores in perceptual evaluation of speech quality and similarity mean option score. The model represents a significant advancement in audio generation technology, opening new possibilities in speech synthesis and music generation.