Simple and Effective ASR for Archives

Archives are traditionally identified as holders of text-based information. However, they also possess audio and video materials, which are the focus of this paper. In archival institutions, the absence of transcriptions for audio and video materials presents significant challenges. These materials often hold historical, cultural, and research value, but without transcriptions, their accessibility and usability are limited. The lack of transcriptions makes it difficult to index and search the content, hindering effective utilization. While existing ASR (Automatic Speech Recognition) technologies can assist, these may suffer from mediocre accuracy, especially with older or poor-quality materials. This work addresses the challenge by utilizing state of the art multilingual LLM (Large Language Model), simple to use UI (User Interface) and GPU (Graphics Processing Unit) ready containers to create a simple and effective multilingual transportable ASR module.