
About Munsit
Munsit: The World’s Most Accurate Arabic Speech-to-Text Model
Munsit is an advanced Arabic automatic speech recognition (ASR) model trained on 30,000 hours of diverse Arabic speech using cutting-edge weak supervision techniques. It surpasses global leaders like OpenAI’s Whisper, Meta’s SeamlessM4T, and Microsoft models in accuracy across all major Arabic speech benchmarks. Munsit specializes in recognizing multiple Arabic dialects with the lowest Word Error Rate (WER) of 26.68%, making it ideal for business, government, and developer applications in the Arabic-speaking world.
Key Features
Trained on 30,000 hours of Arabic audio with 15,000 hours of high-quality filtered samples
Superior performance across six key Arabic speech benchmarks (SADA, Common Voice, MASC, Casablanca, MGB-2)
Lowest average Word Error Rate (WER) among leading Arabic ASR models
Combines multiple ASR candidate transcriptions with perplexity-based filtering and agreement scoring
Supports multi-dialect Arabic transcription with high linguistic accuracy
Enables voice agents, virtual assistants, customer support, and real-time meeting transcription
Accurate Arabic speech-to-text and translation to unified dialects or English
High standards for data security, privacy, and compliance with sovereign AI principles
Use Cases
Building Arabic AI voice agents and conversational interfaces
Real-time transcription and actionable meeting notes across Arabic dialects
Supporting government, business, and smart city applications in the Arabic region
Speech-to-text conversion for multilingual and cross-dialect communication
Integrating high-accuracy Arabic ASR into customer support and virtual assistant workflows
Getting Started
Website: https://munsit.cntxt.tech
Munsit delivers unmatched Arabic speech recognition accuracy, enabling scalable and reliable voice AI applications that meet regional linguistic complexities and stringent data protection standards.