Current multilingual automatic speech recognition (ASR) models have substantially improved speech recognition across many languages, yet recognition quality remains limited for low-resource languages such as Urdu. A common approach to improve recognition quality is to fine-tune the ASR model itself, but this requires labelled speech data and retraining the recognizer. A recently emerging alternative is Generative Error Correction (GEC), in which a language model post-processes ASR output to correct transcription errors while leaving the underlying speech recognizer unchanged. This paper investigates this approach for Urdu by keeping Whisper-Turbo frozen and fine-tuning small language models to correct its transcription errors. To address data scarcity in Urdu, we construct a pseudo-gold training dataset using a strong teacher model. Fine-tuning 3.5–4B parameter language models on this data achieves a ~33% relative WER reduction (from 11.74% to 7.81%), outperforming zero-shot baselines as well as significantly larger 12–14B zero-shot models. These results show that combining a frozen recognizer with a small fine-tuned corrector provides a practical, resource-efficient framework for Urdu ASR improvement without relying on ASR retraining or substantially larger models.