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Accurate and efficient annotation of bilingual clinical recordings remains a persistent challenge, as existing solutions often require high demand for manual work by bilingual clinicians and their assistants and significant training related to annotation tools. To address this issue, we introduce Speech Annotation for A (SAFA)—an end-to-end, user-friendly “lazy mode” annotation workflow. By pairing annotation drafts generated from large language models with chunk-based editing, real-time difference highlighting, and speaker & language tagging - even in multi-speaker code-switching scenarios - SAFA delivers high-quality audio annotations ready for research with minimal setup and minimal human check. It further provides standardized CSV/TXT exports, bridging the gap between fully automated approaches and the meticulous accuracy demanded by multilingual clinical research, while facilitating the creation and expansion of high-quality labeled datasets for downstream studies.