Modeling of distributions mixtures has rested on Gaussian distributions and/or a conditional independence hypothesis for a long time. Only recently researchers have started to construct and study broader generic models without appealing to these hypotheses. Some of these extensions use copulas as a tool to build flexible models, as they permit to model the dependence and the marginal distributions separately. But this approach also has drawbacks. First, it increases much the number of choices the practitioner has to make, and second, marginal misspecification may loom on the horizon. This paper aims at overcoming these limitations by presenting a copula-based mixture model which is semiparametric. Thanks to a location-shift hypothesis, semiparametric estimation, also, is feasible, which allows for data adaptation without any modeling efforts.