Making Chant Computing Easy: CantusCorpus v1.0 and the PyCantus Library

2026-03-12

AI Summary

This study addresses the fragmentation and lack of standardized, computable formats in existing digital resources for Gregorian chant, which have hindered the application of computational methods. To overcome this limitation, the authors present CantusCorpus v1.0—a unified, standardized corpus comprising nearly 900,000 chants—by integrating heterogeneous sources such as the Cantus Index for the first time. They introduce a cross-repository alignment mechanism based on Cantus IDs to ensure interoperability across datasets. Additionally, they develop PyCantus, a lightweight, open-source Python library that facilitates data access, dynamic updates, and multi-source integration. By decoupling the data model from specific implementations, this work establishes a reproducible and extensible infrastructure for computational chant studies, significantly enhancing accessibility and interoperability in digital humanities research.

Technology Category

• Humans and AI: Crowd Sourcing and Human Computation
• Application Domains: Humanities & Computational Social Science
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Web Mining and Content Analysis: Web data integration and cleaning
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Digital Gregorian chant scholarship has for decades enjoyed the privilege of a large digital resource cataloguing chant sources: the Cantus ecosystem, with nearly 900,000 chants catalogued across more than 2000 sources. The Cantus Database data model and the Cantus ID mechanism has been adopted by 18 more chant databases, jointly accessible through the Cantus Index interface. However, this data has only been available piecemeal via the individual online user interfaces; computational methods have so far had only a limited opportunity to process these immense resources. To overcome this hurdle, we compiled CantusCorpus v1.0, a dataset that combines everything that was available across the Cantus Index-centered network of databases as of mid-2025, and we have also provided the code for updating the dataset as the databases grow. We then created the lightweight PyCantus library for working with this data. PyCantus decouples the data model from the Cantus codebase and thus allows integration of further chant data sources, which we illustrate with harmonising pilot data from the Corpus Monodicum project. Computational chant research is attractive - and CantusCorpus v1.0 and PyCantus are infrastructures that should make work in this field more transparent, replicable, and accessible to digital humanities practitioners beyond chant scholars themselves.

Problem

• Gregorian chant
• digital scholarship
• computational access
• data integration
• Cantus ecosystem

Innovation

• CantusCorpus
• PyCantus
• Gregorian chant
• digital humanities
• computational musicology