The low-resource dialectal Automatic Speech Recognition (ASR) in languages like Tamil is a critical issue because of phonological differences, lack of labeled data and because of the differences in the acoustic of speech patterns among regions. This paper will introduce a dialect-conscious Tamil ASR model that is trained on the Conformer-CTC-BPE-Large framework via the NVIDIA NeMo framework. This model is an integration of convolutional subsampling, multi-head self-attention, and Connectionist Temporal Classification (CTC) decoding along with a BPE tokenizer to make possible both efficient end-to-end speech recognition. The system is tested on the audio recordings of dialectal Tamil, in which mono-channel audio normalization and batch transcription are used. Our findings indicate that even using large pretrained Conformer models, dialectal ASR tasks are successfully implemented even in zero-shot. Transcriptions generated are examined and the challenges associated with the dialectal differences and acoustic models, and we comment on the possible future directions of enhancing data-efficient adaptation in low-resource speech recognition.