Cities, counties, and states throughout the USA are bound by law to archive recordings of public meetings. Most local governments comply with these laws by posting documents, audio, or video recordings online. As there is no set standard for municipal data archives however, parsing and processing such data is typically time consuming and highly dependent on each municipality. Council Data Project (CDP) is a set of open-source tools that improve the accessibility of local government data by systematically collecting, transforming, and republishing this data to the web. The data re-published by CDP is packaged and presented within a searchable web application that vastly simplifies the process of finding specific information within the archived data. We envision this project being used by a variety of groups including civic technologists hoping to promote government transparency, researchers focused on public policy, natural language processing, machine learning, or information retrieval and discovery, and many others. Statement of NeedComparative research into municipal governance in the USA is often prohibitively difficult due to a broad federal system where states, counties, and cities divide legislative powers differently. This has contributed to the lack of large-scale quantitative studies of municipal government, and impeded necessary research into effective procedural elements of administrative and legislative processes (Trounstine, 2009). Council Data Project enables large-scale quantitative studies by generating standardized municipal governance corpora -including legislative voting records, timestamped transcripts, and full legislative matter attachments (related reports, presentations, amendments, etc.).

Automated speaker identification is a modeling challenge for research when large-scale corpora, such as audio recordings or transcripts, are relied upon for evidence (e.g. Journalism, Qualitative Research, Law, etc.). To address current difficulties in training speaker identification models, we propose Speakerbox: a method for few-shot fine-tuning of an audio transformer. Specifically, Speakerbox makes multi-recording, multi-speaker identification model fine-tuning as simple as possible while still fitting an accurate, useful model for application. Speakerbox works by ensuring data are safely stratified by speaker id and held-out by recording id prior to fine-tuning of a pretrained speaker identification Transformer on a small number of audio examples. We show that with less than an hour of audio-recorded input, Speakerbox can fine-tune a multi-speaker identification model for use in assisting researchers in audio and transcript annotation.