we are encountering significant challenges in the realm of speech recognition and the overall efficiency of our machine learning (ML) model. These challenges are multifaceted, affecting various aspects of our system's performance and user experience. The primary issue lies in the accuracy of our speech recognition capabilities. Despite using state-of-the-art algorithms and models, our system often struggles to accurately transcribe spoken language, particularly in noisy environments or with speakers who have strong accents or unique speech patterns. This inaccuracy not only frustrates users but also leads to incorrect data processing and results, thereby undermining the reliability of our application. Moreover, the efficiency of our ML model is another critical concern. Our current model requires substantial computational resources and processing time, which hampers its responsiveness and scalability.