An alternative to a multilingual speech API
An alternative to a multilingual speech API: a local, on-prem alternative for Azerbaijani business — see how Chinar compares.
Native Azerbaijani Automatic Speech Recognition
Global speech services often struggle with Azerbaijani, frequently returning fluent but incorrect Turkish text that can mislead those unfamiliar with the language. In head-to-head tests of major 2026 speech services, both commercial and open-source options produced effectively unusable output because they lacked genuine support for the language. Allmaz solves this by providing a specialized ASR solution built specifically for Azerbaijani from the ground up, rather than adapting a model from a related language. Our solution is designed for enterprise-grade deployment on your own infrastructure, ensuring that no call audio ever leaves your building. By eliminating third-party data retention and per-minute cloud billing, businesses can achieve full call coverage. This enables comprehensive quality assurance on every single interaction, deep archive search, and robust evidence for compliance and audit without the cost scaling linearly with call volume.
Advantages of Localized Speech Processing
Native Azerbaijani training ensures accuracy where multilingual APIs often default to Turkish
On-premise deployment guarantees data sovereignty and eliminates third-party audio retention
Predictable cost structure by removing per-minute cloud billing and scaling with your own hardware
Superior performance on real-world phone audio, including background noise and overlapping speech
High-speed processing, performing four to seven times faster than benchmarked cloud services
Comprehensive QA capabilities by transcribing every call rather than relying on small samples
Optimized Models for Every Infrastructure
Chinar-F: High-Volume Production
A compact transducer trained on real Azerbaijani call-center recordings. It is the most accurate model for real calls, making 23% fewer word errors than a commercial cloud speech service and scoring 91% accuracy on clear read speech.
Chinar-L: Document-Ready Transcripts
Optimized for interviews and meetings, this GPU-based model handles punctuation, capitalization, and digits. It achieves 93–94% word accuracy on clear Azerbaijani speech, measured against human-checked transcripts for professional documentation.
Chinar-P: Minimal Infrastructure
A compact model requiring no GPU that runs 26× faster than real-time on four CPU cores. It provides punctuation and capitalization, performing on a par with a commercial cloud speech service.
Real-World Robustness
Unlike models trained on read speech, our system is trained on genuine phone audio, making it resilient to interruptions, background noise, and standard phone-line quality.
Deploying Local Speech Recognition
Frequently Asked Questions
How does this differ from global multilingual APIs?
Many global services do not truly support Azerbaijani and may return confident Turkish text instead. Our models are built specifically for Azerbaijani and run locally on your hardware to ensure accuracy and privacy.
Can I test the technology before deploying?
Yes, visitors can test hosted ASR models for free at chinar.allmaz.az. You can upload a recording and get an editable Azerbaijani transcript without needing an account or credit card.
What is the accuracy on real phone calls?
Our Chinar-F model is specifically trained on real call-center recordings and makes 23% fewer word errors on such audio compared to benchmarked commercial cloud speech services.
Does it support other languages or mixed speech?
The current models are optimized for Azerbaijani. However, support for Russian and mixed Azerbaijani–Russian speech is currently in development.
What hardware is required for these models?
Depending on the model, you can run on CPU or GPU. Chinar-P is designed for minimal infrastructure and requires no GPU, while Chinar-L is optimized for GPU environments.
Ready for Accurate Azerbaijani Transcription?
Move beyond generic cloud APIs and secure your data with a local, high-performance speech solution.
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