bacpipe: a Python package to make bioacoustic deep learning models accessible
This work addresses the accessibility barrier for ecologists and computer scientists to use advanced bioacoustic deep learning models, but it is an incremental software tool rather than a novel method.
Bacpipe is a Python package that provides a graphical and programming interface to access and evaluate bioacoustic deep learning models, generating embeddings and classifier predictions for custom audio datasets. It aims to make state-of-the-art models accessible to ecologists and computer scientists.
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.