The quality of Automatic Speech Recognition (ASR) systems largely depends on the availability of training data, which is predominantly accessible for either high-resource or low-resource languages. In contrast, languages such as Armenian face significant challenges due to the almost zero availability of public speech and text corpora. In this paper, we introduce a comprehensive framework that elevates data availability for a zero-resource language to a new level, thereby enabling the development of a fully operational online ASR model. Our approach involves data collection and processing through diverse resources, including audiobooks, paid crowdsourcing, and leveraging the volunteer platform to assemble a labeled dataset totaling 149 hours. This data made it possible to apply pseudo-labeling techniques on additional 145 hours of public audio data, achieving a new state-of-the-art Word Error Rate (WER) of 9.90% on Common Voice test. All datasets and ASR models are open-sourced.