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.
A Specialized Alternative to Multilingual Speech APIs
Many global speech services claim multilingual support, but often fail to distinguish Azerbaijani from related languages. In practice, these services frequently return fluent, confident text in Turkish, which can appear as a working transcript to those unfamiliar with the language. Even head-to-head tests of major 2026 commercial and open-source speech services revealed that some outputs are effectively unusable because they lack genuine support for the Azerbaijani language. Allmaz addresses this gap with a dedicated, on-premises automatic speech recognition system built specifically for Azerbaijani rather than adapted from another language. By focusing on the unique linguistic nuances of the region and deploying directly on your own infrastructure, we ensure high accuracy and total data sovereignty for local businesses, removing the risks associated with third-party cloud retention.
Advantages of a Dedicated Azerbaijani Model
Eliminates 'false fluency' where Azerbaijani speech is incorrectly transcribed as Turkish
Guarantees data sovereignty as recordings never leave your internal network
Removes financial unpredictability by eliminating per-hour metering and third-party retention
Superior performance on real-world audio, trained on call-center recordings rather than clean read speech
High-velocity processing, performing four to seven times faster than benchmarked cloud services
Flexible deployment options with models optimized for either human readability or machine analytics
Tailored Models for Every Use Case
Chinar-L for Human Readability
Designed for documents, summaries, and compliance records. It provides punctuated and capitalized transcripts with 87% word accuracy on clear Azerbaijani speech.
Chinar-F for Machine Analytics
A lightweight model roughly 50x smaller than the large version, ideal for archiving, search, and quality monitoring across every call.
On-Premises Deployment
Runs entirely on your own infrastructure, removing the risks associated with third-party cloud retention.
Real-World Audio Training
Trained on actual call-center recordings, meaning the system handles background noise, interruptions, and phone-line quality.
Implementing Local Speech Recognition
Frequently Asked Questions
How does this differ from global multilingual APIs?
Many global services lack true Azerbaijani support and may return Turkish text instead. Our models are built specifically for Azerbaijani rather than adapted from related languages, avoiding the common error of producing fluent but incorrect Turkish transcripts.
Can it handle noisy phone recordings?
Yes. Unlike models trained on read speech, our system is trained on genuine call-center recordings, meaning it is designed to handle background noise, interruptions, overlapping speech, and standard phone-line quality.
What are the hardware requirements for the different models?
Chinar-F is specifically designed to be roughly 50 times smaller than Chinar-L, allowing it to run comfortably on hardware where a large model will not, significantly reducing the cost per hour for high-volume tasks.
Is my data secure during transcription?
Absolutely. Because the system runs on your own infrastructure, recordings never leave your network, there is no third-party data retention, and you are not subject to per-hour cloud metering.
How accurate is the transcription for human-read documents?
The Chinar-L model, which provides the punctuation and capitalization necessary for human-readable documents, scores 87% word accuracy on clear Azerbaijani speech when measured against reference transcripts from a commercial speech system.
Ready for Accurate Azerbaijani Transcription?
Move your speech processing in-house with a model built for your language and your infrastructure.
Request a demo