During forensic investigations, composite sketches are frequently used to track down suspects when photographic evidence is unavailable. Descriptions from victims or eyewitnesses are used to create these sketches. Forensic artists are essential due to the sketches’ investigative and prosecutorial applications, but their work is expensive and time-consuming. Existing systems for generating face sketches from facial attributes have shown reasonable performance but struggle with limited training samples. This work uses data augmentation, specifically adaptive discriminator augmentation (ADA), to mitigate these challenges. Our framework uses a pre-trained text encoder, bidirectional LSTM, to encode descriptions into sentence embeddings, and a style-based generative adversarial network, StyleGAN2, fine-tuned for sketch generation. Extensive experiments demonstrate that this approach outperforms existing state-of-the-art models.