Cardiac digital twins represent the required functional mechanisms of patient hearts to evaluate therapies and inform clinical decision-making virtually. A scalable generation of cardiac digital twins can enable virtual clinical trials on virtual cohorts to fast-track therapy development. Here, we present an open-source digital twinning framework for personalising electrophysiological function based on routinely acquired magnetic resonance imaging (MRI) data and the standard 12-lead electrocardiogram (ECG). We extended a Bayesian-based inference framework to infer electrical repolarisation characteristics. Fast simulations are conducted with a decoupled reaction-Eikonal model, including the Purkinje network and biophysically-detailed subcellular ionic current dynamics. Parameter uncertainty is represented by inferring a population of ventricular models rather than a single one, which means that parameter uncertainty can be propagated to virtual therapy evaluations. The framework is demonstrated in a healthy female subject, where our inferred reaction-Eikonal models reproduced the patient’s ECG with a Pearson’s correlation coefficient of 0.93. The methodologies for cardiac digital twinning presented here are a step towards personalised virtual therapy testing. The tools developed for this study are open-source, ensuring accessibility, inclusivity, and reproducibility, this is available on GitHub.