  We present HebDB, a weakly supervised dataset for spoken language processing
in the Hebrew language. HebDB offers roughly 2500 hours of natural and
spontaneous speech recordings in the Hebrew language, consisting of a large
variety of speakers and topics. We provide raw recordings together with a
pre-processed, weakly supervised, and filtered version. The goal of HebDB is to
further enhance research and development of spoken language processing tools
for the Hebrew language. Hence, we additionally provide two baseline systems
for Automatic Speech Recognition (ASR): (i) a self-supervised model; and (ii) a
fully supervised model. We present the performance of these two methods
optimized on HebDB and compare them to current multi-lingual ASR alternatives.
Results suggest the proposed method reaches better results than the evaluated
baselines considering similar model sizes. Dataset, code, and models are
publicly available under https://pages.cs.huji.ac.il/adiyoss-lab/HebDB/.
