📄 中文摘要
高质量的睡眠不仅仅依赖于智能手表的监测，音频模式是呼吸健康的重要指标。通过音频信号处理和深度学习技术，构建高精度监测系统成为可能。利用 OpenAI Whisper 的微调和 PyTorch，将标准的语音转文本模型转变为专门的声学传感器，能够识别打鼾、重度呼吸以及最重要的睡眠呼吸暂停的静默状态。该系统为医疗人工智能提供了生产就绪的架构模式，适合需要高精度监测的用户。
睡眠黑客：微调 OpenAI Whisper 实现高精度打鼾与呼吸暂停识别
📄 English Summary
Sleep Hacker: Fine-Tuning OpenAI Whisper for High-Precision Snoring & Apnea Recognition
The quality of sleep is not solely determined by smartwatch metrics, as audio patterns serve as critical indicators of respiratory health. A high-precision monitoring system can be developed through audio signal processing and deep learning techniques. By fine-tuning OpenAI Whisper and utilizing PyTorch, a standard Speech-to-Text model is transformed into a specialized acoustic sensor capable of identifying snoring, heavy breathing, and most importantly, the silence associated with Sleep Apnea. This system offers production-ready architectural patterns for medical AI, catering to users requiring high-precision monitoring.