MAI Transcribe-1.5 - Microsoft's MAI speech-to-text model

What is MAI Transcribe-1.5?

MAI-Transcribe-1.5 is a speech-to-text model developed by Microsoft's AI team. It supports 43 languages and has context-aware keyword biasing capabilities. The model achieved the industry's lowest word error rate (WER 4.86%) in the FLEURS benchmark test and is designed for enterprise-level production scenarios such as video captioning, conference transcription, and call analysis.

Main features of MAI Transcribe-1.5

• High-precision transcription of 43 languagesIt covers 43 languages including English, Chinese, Japanese, Hindi, and Arabic, and supports automatic language recognition.
• Keyword/Entity BiasIt can inject up to 200 domain-specific terms (such as personal names, product names, and medical terms), and uses contextual intelligence to determine whether to apply bias rather than forcibly matching.
• Robustness in noisy environmentsOptimized for real-world background noise and audio quality variations to maintain high accuracy.
• Long audio high-speed processingOne hour of audio can be transcribed in about 15 minutes, which is up to 5 times faster than the previous generation.
• Industry scenario adaptationIt has built-in ability to understand terminology in fields such as healthcare, customer service, and finance, and is ready to use out of the box.

Technical Principles of MAI Transcribe-1.5

• Multilingual unified modelingThe model is jointly trained on massive amounts of speech data in 43 languages, covering mainstream languages, including low-resource languages such as Assamese, Gujarati, and Kannada. It achieves cross-language transfer through shared representation learning, ensuring stability under different accents and dialects.
• Context-aware keyword bias mechanismUnlike traditional forced substitution, MAI-Transcribe-1.5 incorporates user-provided domain-specific vocabulary as soft cues into the decoding process. The model combines acoustic features and semantic context to dynamically determine when to activate bias strategies. On the FLEURS multilingual benchmark, it can further reduce WER by 30% while avoiding false positives on common vocabulary.
• Long audio segmentation and streaming optimizationFor long audio recordings such as conferences and podcasts, the model employs an improved segmentation and caching mechanism to reduce redundant computations and memory usage, significantly reducing end-to-end latency while maintaining semantic coherence across segments.

How to use MAI Transcribe-1.5

• Azure Speech SDKIntegrate the SDK into the application and call it. MAI-Transcribe-1.5 Model endpoints support WAV/MP3/FLAC formats (maximum single file size 300 MB or 2 hours).
• REST API: Send audio streams or files directly via HTTP requests to obtain transcription results in JSON format.
• MAI PlaygroundUpload your audio to the interactive sandbox on the Microsoft Mai playground website (https://playground.microsoft.ai/) to experience the effects instantly.
• Microsoft FoundryAccess via Azure Speech service, billed at $0.36/hour for audio, no model deployment required.

The core advantages of MAI Transcribe-1.5

• Industry-leading accuracyFLEURS 43 languages have an average WER of 4.86%, lower than Elevenlabs Scribe v2 (5.53%), OpenAI Transcribe (5.73%) and Google Gemini Flash Lite (5.63%).
• Language coverage doubledCompared to the 25 languages in v1, 18 new languages have been added, making it more suitable for global products.
• Zero error in domain vocabularyBy using keyword bias, we can accurately transcribe internal technical terms, abbreviations, and drug names of enterprises.
• Balancing cost and speedWith a model priced at $0.36/hour and 5x faster long audio processing, it offers exceptional value for money.

MAI Transcribe-1.5 project address

• Project official websitehttps://microsoft.ai/models/mai-transcribe-1-5/
• Technical Papers: https://microsoft.ai/pdf/MAI-Transcribe-1.5-Model-Card.PDF

Comparison of MAI Transcribe-1.5 with similar competing products

Application scenarios of MAI Transcribe-1.5

• Video subtitles and content localizationIt automatically generates high-precision subtitles in 43 languages for global video platforms, reducing localization costs.
• Conference and Interview TranscriptionQuickly convert multilingual meeting recordings into searchable text; one hour of audio can be converted into approximately 15 minutes.
• Customer service call analysisIt accurately identifies professional terms such as drug names and product models, supporting intelligent quality inspection and sentiment analysis.
• Medical oral historyAutomatically transcribes anatomical and pharmaceutical terms from doctor's rounds and surgical records, improving medical record entry efficiency.
• Accessibility toolsProvides real-time speech-to-text service for hearing-impaired individuals, supporting clear recognition even in noisy environments.