1. Natural sounds have been recorded for millions of hours over the previous decades using passive acoustic monitoring. Improvements in deep learning models have vastly accelerated the analysis of large portions of this data. While new models advance the state-of-the-art, accessing them using tools to harness their full potential is not always straightforward. Here we present bacpipe, a collection of bioacoustic deep learning models and evaluation pipelines accessible through a graphical and programming interface, designed for both ecologists and computer scientists. Bacpipe is a modular software package intended as a point of convergence for bioacoustic models. 2. Bacpipe streamlines the usage of state-of-the-art models on custom audio datasets, generating acoustic feature vectors (embeddings) and classifier predictions. A modular design allows evaluation and benchmarking of models through interactive visualizations, clustering and probing. 3. We believe that access to new deep learning models is important. By designing bacpipe to target a wide audience, researchers will be enabled to answer new ecological and evolutionary questions in bioacoustics. 4. In conclusion, we believe accessibility to developments in deep learning to a wider audience benefits the ecological questions we are trying to answer.

翻译：摘要：1. 过去数十年间，利用被动声学监测技术已录制了数百万小时的自然声音。深度学习模型的改进极大加速了对此类庞大数据集的分析进程。尽管新模型持续推动技术前沿发展，但通过工具充分挖掘其潜力并非总是一蹴而就。本文介绍bacpipe——一个集生物声学深度学习模型与评估流程于一体的工具包，通过图形界面与编程接口双轨接入，专为生态学家与计算机科学家设计。Bacpipe作为模块化软件包，旨在成为生物声学模型的汇聚枢纽。2. Bacpipe简化了将前沿模型应用于自定义音频数据集的流程，可生成声学特征向量（嵌入向量）与分类器预测结果。其模块化设计支持通过交互式可视化、聚类分析与探测技术实现模型评估与基准测试。3. 我们坚信新型深度学习模型的易用性至关重要。通过面向广泛受众设计bacpipe，研究者将能够解答生物声学领域新的生态学与进化生物学问题。4. 总结而言，我们认为深度学习技术对更广泛受众的可及性将推动我们试图解答的生态学问题研究。