A cardiac digital twin is a virtual tool representing a patient's heart that can simulate new events, such as evaluating therapies to inform clinical decision-making. We present a sex-specific digital twinning framework for personalising electrophysiological function based on routinely acquired magnetic resonance imaging (MRI) data and the standard 12-lead electrocardiogram (ECG). We generate the MRI-based biventricular geometry. Then, we use a sequential Monte Carlo approximate Bayesian computation algorithm to infer the heart's electrical activation and repolarisation properties from the QRS, and from the T wave, respectively. We use a dictionary of action potential models generated using sex-specific electrophysiology characteristics implemented on the ToR-ORd cellular model by calibrating the ionic conductances using relative mRNA expression ratios of ion channel markers from non-diseased adult human male and female ventricular myocytes. These new sex-specific models, combined with the MRI-derived geometries for the heart and torso, enabled personalising to the sex of the subject considered for the study. We applied our framework to a female subject, and the inferred population matched the clinical data's QRS and T wave with a Pearson's correlation coefficient of 0.89. These methodologies for cardiac digital twinning are a step towards personalised virtual therapy testing. These tools are available at github.com/juliacamps/Cardiac-Digital-Twin.