Nuha-Speech: Building General-Purpose Arabic Speech-LLMs
Nuha-Speech initiative builds general-purpose Arabic speech-LLMs using a 1.5M-sample speech QA corpus and fine-tuned Qwen-Omni variants.
The paper introduces Nuha-Speech, an initiative covering dataset construction, model training, and evaluation for Arabic speech large language models. The authors built an Arabic Speech Question-Answering corpus of over 1.5 million training samples and used it for supervised fine-tuning of Qwen-Omni model variants at multiple scales. A tailored evaluation framework with diverse tasks and metrics is designed to assess Arabic speech capabilities under limited resource constraints.
- 1.5M-sample Arabic speech question-answering corpus for instruction tuning
- Supervised fine-tuning of Qwen-Omni variants at different scales
- Evaluation framework with diverse tasks and tailored metrics
- Targets underrepresented Arabic language in multilingual speech-LLMs
- Vendors
- Alibaba
- Products
- Nuha-Speech
- Organizations
- arXiv
- AI models
- Qwen-Omni
From the original article
1 min read at arxiv.orgAs Speech Large Language Models (speech-LLMs) become increasingly multilingual, Arabic remains significantly underrepresented, highlighting the need for dedicated infrastructure to train and evaluate Arabic speech-LLMs. To address this gap, we introduce Nuha-Speech, a comprehensive initiative to develop general-purpose Arabic speech-LLMs spanning dataset construction, model training, and systematic evaluation.
By Yingzhi Wang · arxiv.org
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