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A voice cloning machine learning (ML) model receives a speech and text input and creates a new speech output reading the text input in the voice of the speaker input. Our project aims to both speed up and reduce the computational resources necessary to run a voice cloning ML model, which can then be uploaded to a low-end system. The project uses a pre-existing machine learning toolkit repository to speed up the productivity of machine learning engineers. By implementing a modified ML model with updated sub-models into the existing model, we gain access to an improved training and evaluation environment that is more accessible to a broader audience. Once the updated model is complete, it can be implemented on a low-end system for user interaction. The user peripherals consist of a miniature button keyboard with an attachable display and a microphone for user inputs, and a speaker for a cloned voice output.