This repository provides a PyTorch implementation of the Conformer model, which augments Transformer architectures with convolutional modules to improve local inductive bias. The implementation includes the convolutional module, individual Conformer blocks, and full multi-block Conformer models with configurable dimensions, attention heads, and feed-forward multipliers. It is designed for sequence-to-sequence tasks, particularly automatic speech recognition.