Wifaq is our entry to NADI 2026 Subtask 1.1, which evaluates country-level Arabic automatic speech recognition across eight dialects under noisy conditions. We fully fine-tune four 600M-parameter W2v-BERT 2.0 CTC models on nested mixtures of task and ex- ternal speech, decode them with a shared 5-gram KenLM, and combine their hypotheses by word voting; each run fits on one 16 GB NVIDIA RTX 4070 Ti SUPER. The submitted four-way ensemble ranked first among nine entries on the blind test with 43.23% WER and 20.0% CER. On a reused 800-utterance local holdout, it lowers WER from 46.12% for the strongest single model to 44.0%