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Deep learning models, such as recurrent neural networks (RNNs) and transformers, are commonly used for AI voice synthesis. These models are capable of capturing the complex dependencies and long-range relationships in speech, allowing them to generate more natural and coherent voices. The training process can take a significant amount of time and computational resources, depending on the size of the dataset and the complexity of the model.
