1. Passive acoustic monitoring (PAM) coupled with artificial intelligence (AI) is becoming an essential tool for biodiversity monitoring. Traditional PAM systems require manual data offloading and impose substantial demands on storage and computing infrastructure. The combination of on-device AI-based processing and network connectivity enables local data analysis and transmission of only relevant information, greatly reducing storage needs. However, programming these devices for robust operation is challenging, requiring expertise in embedded systems and software engineering. Despite the increase in AI-based models for bioacoustics, their full potential remains unrealized without accessible tools to deploy them on custom hardware and tailor device behaviour to specific monitoring goals. 2. To address this challenge, we develop acoupi, an open-source Python framework that simplifies the creation and deployment of smart bioacoustic devices. acoupi integrates audio recording, AI-based data processing, data management, and real-time wireless messaging into a unified and configurable framework. By modularising key elements of the bioacoustic monitoring workflow, acoupi allows users to easily customise, extend, or select specific components to fit their unique monitoring needs. 3. We demonstrate the flexibility of acoupi by integrating two bioacoustic classifiers: BirdNET, for the classification of bird species, and BatDetect2, for the classification of UK bat species. We test the reliability of acoupi over a month-long deployment of two acoupi-powered devices in a UK urban park. 4. acoupi can be deployed on low-cost hardware such as the Raspberry Pi and can be customised for various applications. acoupi standardised framework and simplified tools facilitate the adoption of AI-powered PAM systems for researchers and conservationists. acoupi is on GitHub at https://github.com/acoupi/acoupi.

翻译：1. 被动声学监测与人工智能的结合正成为生物多样性监测的重要工具。传统PAM系统需要人工数据导出，并对存储和计算基础设施提出巨大需求。基于设备端AI处理与网络连接的结合，能够实现本地数据分析并仅传输相关信息，从而大幅降低存储需求。然而，为这些设备编程以实现稳健运行具有挑战性，需要嵌入式系统和软件工程的专业知识。尽管基于AI的生物声学模型日益增多，但若缺乏可访问的工具将其部署到定制硬件上并根据特定监测目标调整设备行为，其全部潜力仍无法实现。2. 为应对这一挑战，我们开发了acoupi——一个简化智能生物声学设备创建与部署的开源Python框架。acoupi将音频录制、基于AI的数据处理、数据管理和实时无线消息传递集成到一个统一且可配置的框架中。通过对生物声学监测工作流关键要素的模块化设计，acoupi允许用户轻松定制、扩展或选择特定组件，以满足其独特的监测需求。3. 我们通过集成两个生物声学分类器展示了acoupi的灵活性：用于鸟类物种分类的BirdNET，以及用于英国蝙蝠物种分类的BatDetect2。我们在英国某城市公园对两个搭载acoupi的设备进行了为期一个月的部署测试，验证了其可靠性。4. acoupi可部署在树莓派等低成本硬件上，并能针对不同应用进行定制。其标准化框架和简化工具促进了研究人员和保护工作者对AI驱动的PAM系统的采用。acoupi项目位于GitHub：https://github.com/acoupi/acoupi。