We present a compact baseline for the For- mosa Speech Recognition (FSR-2025) Tai- wanese Hakka ASR challenge. Our system fine-tunes Whisper-large-v2 (Track 1) and Whisper-large-v3-turbo (Track 2) (Radford et al., 2022) with LoRA (Hu et al., 2021), under a consistent normalization policy and balanced speaker-based dev splits. On the official warm-up set, we obtain 10.94% CER for Track 1 (Hanzi) and 28.48% SER for Track 2 (Pinyin). We provide simple, reproducible pipelines covering data prepa- ration, training, inference, and evaluation, without using external data or language models.