用轻量微调让Whisper更准转录执法录像，还能在普通电脑上跑。
Enhancing Law-Enforcement Audio Transcription: A LoRA-Based Adaptation of Whisper for BWC Footage
- 用LoRA微调Whisper，仅改少量参数就适配执法场景音频。
- 在4GB显卡上用8比特量化实现高效推理，准确率达93.7%。
- 适合需要低成本自动化转录的警务系统与司法透明研究者。
现代执法面临“可见性悖论”：警员佩戴摄像头（BWC）积累了海量视频资料，却因人工转录成本过高而难以用于问责或系统性审查。本研究提出一种框架，将OpenAI Whisper模型适配至执法环境特有的语音挑战。通过低秩适应（LoRA）实现参数高效微调，有效缓解零样本模型在高压情境、警笛和无线电干扰下的性能下降问题。关键成果在于，该方法可在消费级硬件（搭载NVIDIA 4GB GTX GPU的Acer Nitro笔记本）上完成训练与推理，结合8位量化与梯度检查点技术。进一步将转录结果接入领域特定本体的符号推理流程，生成证据关联的事件图谱，实现93.7%的词汇映射率，助力程序正义与透明化治理。
原文摘要 · Abstract (English)
Modern policing faces a "visibility paradox" where law enforcement agencies possess petabytes of Body-Worn Camera (BWC) footage that remains largely unutilized for accountability or systemic review due to the prohibitive labor costs of manual transcription. This research presents a framework for adapting the OpenAI Whisper architecture to the unique acoustic and linguistic challenges of the policing environment. By employing Parameter-Efficient Fine-Tuning (PEFT) through Low-Rank Adaptation (LoRA), we address the significant performance degradation observed in zero-shot models when confronted with high-stress scenarios, sirens, and radio interference. Crucially, we demonstrate that this adaptation is feasible on consumer-grade hardware (Acer Nitro local machine with NVIDIA 4GB GTX GPU) using 8-bit quantization and gradient checkpointing. We further integrate these transcriptions into a symbolic reasoning pipeline using a domain-specific ontology to transform raw audio into evidence-linked incident graphs, achieving a 93.7% lexicon mapping rate for the advancement of procedural justice and transparency.
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