Rapid, accurate wave forecasts are critical to coastal communities and nearshore research. Observational data assimilation improves predictive skill, but is difficult to implement in current adjoint variational systems. Machine learning offers an alternative. Here, a previously proposed framework SWRL Net (Mooneyham et al. 2020) is applied to an array of buoys along the U. S. West Coast to quantify the effect of training data size, determine the impacts of transfer learning using archived wave prediction hindcasts, and evaluate the potential skill on recent wave forecasts. Results across buoy locations show diminishing returns for training data sets greater than 5-years, with error reductions of 10-60%. Experiments trained with shorter (1-year) forecast records have higher error, but the application of transfer learning using wave hindcasts substantially improves model performance.