The CHiME-7 unsupervised domain adaptation speech enhancement (UDASE) challenge targets domain adaptation to unlabelled speech data. This paper describes the University of Sheffield team’s system submitted to the challenge. A generative adversarial network (GAN) methodology based on a conformer-based metric GAN (CMGAN) is employed as opposed to the unsupervised RemixIT strategy used in the CHiME-7 baseline system. The discriminator of the GAN is trained to predict the output score of a Deep Noise Suppression Mean Opinion Score (DNSMOS) metric. Additional data augmentation strategies are employed which provide the discriminator with historical training data outputs as well as more diverse training examples from an additional pseudo-generator. The proposed approach, denoted as CMGAN+/+, achieves significant improvement in DNSMOS evaluation metrics with the best proposed system achieving 3.51 OVR-MOS, a 24% improvement over the baseline.