Event extraction from text is a complex taskthat involves the identification of event triggersand their supporting arguments. Whenapplied to speech, this task becomes evenmore challenging due to the continuous natureof audio signals and the need for robustAutomatic Speech Recognition (ASR). Thispaper proposes an approach that integratesASR with event extraction by utilizing theWhisper model for speech recognition and aText2Event2 Transformer for extracting eventsfrom English audio samples. The Whispermodel is used to generate transcripts from audio,which are then fed into the Text2Event2Transformer to identify event triggers and theirarguments. This approach combines two difficulttasks into one, streamlining the processof extracting structured event information directlyfrom audio. Our approach leverages arobust ASR system (Whisper) followed by aparameter-efficient transformer (Text2Event2fine-tuned via LoRA) to extract structuredevents from raw speech. Unlike prior worktrained on gold textual input, our pipeline istrained end-to-end on noisy ASR outputs. Despitesignificant resource constraints and datanoise, our system ranked first in the ACL 2025XLLM Shared Task II.