Pliers is a Python package for automated extraction of features from multimodal stimuli. It provides a unified, standardized interface to dozens of different feature extraction tools and services--including many state-of-the-art deep learning-based models and content analysis APIs. It's designed to let you rapidly and flexibly extract all kinds of useful information from videos, images, audio, and text.

You might benefit from pliers if you need to accomplish any of the following tasks (and many others!):

• Identify objects or faces in a series of images
• Transcribe the speech in an audio or video file
• Apply sentiment analysis to text
• Extract musical features from an audio clip
• Apply a part-of-speech tagger to a block of text

Each of the above tasks can typically be accomplished in 2 - 3 lines of code with pliers. Combining them all--and returning a single, standardized DataFrame--might take a bit more work. Say maybe 5 or 6 lines.

In a nutshell, pliers provides a high-level, unified interface to a large number of feature extraction tools spanning a wide range of modalities.

The official pliers documentation on ReadTheDocs is comprehensive, and contains a quickstart, API Reference, and more.

Pliers is a general purpose tool, this is just one domain where it's useful.

Tutorial Video

The above video is from a tutorial as a part of a course about naturalistic data.

If you use pliers in your work, please cite both the pliers and the following paper:

McNamara, Q., De La Vega, A., & Yarkoni, T. (2017, August). Developing a comprehensive framework for multimodal feature extraction. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1567-1574). ACM.