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 Dedicated Alternative to Multilingual Speech APIs
Many global speech services attempt to handle Azerbaijani by adapting models from related languages. This often results in transcripts that appear fluent and confident but are actually written in Turkish, which can mislead anyone not fluent in both languages. In head-to-head tests of major 2026 speech services, both commercial and open models produced output that was effectively unusable because they lacked genuine support for the Azerbaijani language. Chinar provides a local, on-premise alternative specifically built for Azerbaijani rather than adapted from elsewhere. By utilizing models trained on genuine call-centre recordings—complete with background noise, interruptions, and overlapping speech—it ensures high accuracy and total data sovereignty. This approach allows businesses to move away from unreliable cloud APIs and implement a solution that truly understands the linguistic nuances of Azerbaijani speech.
Advantages of Localized Speech Recognition
Native Azerbaijani architecture built from the ground up rather than adapted from related languages
Complete data sovereignty with on-premise deployment, ensuring no audio ever leaves your building
Elimination of per-minute cloud billing, making full call coverage and 100% QA affordable
Robust handling of mixed Azerbaijani–Russian speech for diverse communication environments
High-performance processing speeds, running four to seven times faster than benchmarked cloud services
Enhanced security and compliance through the total absence of third-party data retention
Production-Ready Model Options
Chinar-F: Volume & Accuracy
A compact transducer trained on real Azerbaijani call-centre recordings. It is the most accurate model for real calls, making 23% fewer word errors than commercial cloud services on the same recordings, and scores 91% word accuracy on clear read speech.
Chinar-L: Document Quality
Optimized for interviews and meetings where words end up in summaries or compliance records. It writes punctuation, capitals, and numbers as digits, achieving 93–94% word accuracy on clear Azerbaijani speech.
Chinar-P: Minimal Infrastructure
A compact model designed for sites without a GPU. It runs 26× faster than real time on four CPU cores and performs on par with commercial cloud services for call-centre recordings.
Advanced Transcription
All three models are production-ready and include automatic punctuation, capitalization, and speaker diarisation to accurately label who said what.
Implementing Localized ASR
Frequently Asked Questions
How does Chinar differ from global multilingual APIs?
Global services often mistake Azerbaijani for Turkish or fail to support it entirely. Chinar is built specifically for Azerbaijani and trained on genuine call-centre recordings—including background noise and interruptions—rather than just read speech.
Do I need expensive hardware to run these models?
Not necessarily. While Chinar-L requires a GPU for document-grade transcription, Chinar-P is designed for minimal infrastructure and can run efficiently on four CPU cores without a GPU.
Is it possible to test the technology before deployment?
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.
How does the cost compare to cloud-based speech services?
Because the models run on your own infrastructure, there is no per-minute cloud billing. This means your operational costs do not grow as your call volume increases, making full archive transcription affordable.
Can the models handle conversations involving multiple languages?
Yes, Chinar is specifically designed to handle mixed Azerbaijani–Russian speech, ensuring accurate transcription even when speakers switch between the two languages.
Ready for Accurate Azerbaijani Transcription?
Move your speech processing on-premise for better accuracy, security, and cost-efficiency.
Request a demo