This study focuses on speech recognition for low-resource languages, with Hakka as the case study. Since there is currently a lack of dedicated speech models for Taiwanese Southern Min, Hakka, and indigenous languages, we adopt OpenAI Whisper-Medium as the base model and apply Low-Rank Adaptation (LoRA) for fine-tuning. Two models with different output forms were developed: a Hakka character-based model and a Hakka phonetic-based model. The experimental dataset contains approximately 80 hours of speech, covering the Dapu and Zhao’an dialects, and the models were evaluated using Character Error Rate (CER) and Word Error Rate (WER).