This paper presents our method for tackling the CHIME-8 chal- lenge’s NOTSOFAR-1 task, which requires participants to per- form multi-speaker automatic speech recognition (ASR) using audio from distant microphone arrays. We modify the Pyan- note3 diarization pipeline, incorporating pre-trained WavLM as local EEND to adapt effectively to new domains, and we intro- duce two diarization-aware approaches to ASR by condition- ing Whisper on diarization outputs for target-speaker ASR. The first method, which we refer to as Query-Key Biasing, modi- fies Whisper’s attention mechanism and positional embeddings with a learnable attention mask to exclude non-target speaker segments in the audio. The second method, called Frame- Level Diarization-Dependent Transformations, applies affine, diarization-dependent transformations with trainable parame- ters to the inputs of one or more transformer blocks. We also extend both the ASR and diarization systems to a multichannel setup by incorporating cross-channel communication into our models. Finally, we report the performance of these approaches on the NOTSOFAR-1 dataset.