Arabic is a complex language with many varieties and dialects spoken by over
450 millions all around the world. Due to the linguistic diversity and
variations, it is challenging to build a robust and generalized ASR system for
Arabic. In this work, we address this gap by developing and demoing a system,
dubbed VoxArabica, for dialect identification (DID) as well as automatic speech
recognition (ASR) of Arabic. We train a wide range of models such as HuBERT
(DID), Whisper, and XLS-R (ASR) in a supervised setting for Arabic DID and ASR
tasks. Our DID models are trained to identify 17 different dialects in addition
to MSA. We finetune our ASR models on MSA, Egyptian, Moroccan, and mixed data.
Additionally, for the remaining dialects in ASR, we provide the option to
choose various models such as Whisper and MMS in a zero-shot setting. We
integrate these models into a single web interface with diverse features such
as audio recording, file upload, model selection, and the option to raise flags
for incorrect outputs. Overall, we believe VoxArabica will be useful for a wide
range of audiences concerned with Arabic research. Our system is currently
running at this https URL

Arabic is a complex language, and this research introduces VoxArabica, a system for dialect identification and automatic speech recognition (ASR) of Arabic, which includes training models for 17 different dialects, MSA, Egyptian, Moroccan, and mixed data, and offers options to choose various ASR models in a web interface.