Ask a global speech service to transcribe an Azerbaijani call and you usually get back fluent, confident text — in Turkish. Chinar is built for Azerbaijani from the ground up, in three models: Chinar-F for every call at volume, Chinar-L for transcripts people read, Chinar-P for sites without a GPU.
Ask a global speech service for an Azerbaijani transcript and it returns fluent Turkish. It looks like a working transcript — and unless someone on your team reads Azerbaijani, nobody notices until the text reaches a report.
Two major 2026 speech services tested head to head on the same audio — one commercial, one open — produced Azerbaijani output that was effectively unusable. They do not support the language at all.
Many engines learn from clean, read-aloud recordings and fall apart on a real conversation: background noise, interruptions, two people talking at once, ordinary phone-line quality.
Transcribing every call through a cloud service means paying by the hour and handing customer conversations to a third party that keeps its own copy.
Not a multilingual model with Azerbaijani bolted on, and not Turkish with an accent. The language is the design — which is the difference between a transcript you can act on and one that merely looks finished.
A compact model trained on real Azerbaijani call-centre recordings at phone quality, and our most accurate model on real calls: 23% fewer word errors than a commercial cloud speech service on the same call recordings. Cheap per hour on CPU or GPU, so every call gets transcribed rather than a sample.
Writes punctuation, capitals and numbers as digits, ready to put in front of a supervisor, an auditor or a customer. 93–94% word accuracy on clear Azerbaijani speech, measured against human-checked transcripts. Runs on GPU.
A compact model that needs no GPU: 26× faster than real time on four CPU cores, with punctuation and capitals. On real calls it is on a par with a commercial cloud speech service — on a server you already own.
Trained on genuine call-centre recordings — background noise, interruptions, people talking over each other, ordinary phone-line quality.
Before you talk to us, test hosted ASR models on your own Azerbaijani audio at chinar.allmaz.az: upload a recording, read the transcript, edit and export it. Free, with no card and no sales call.
Chinar runs on your own infrastructure. Customer conversations never leave your building: no per-hour metering, no third-party retention, no questions about where the audio went.
| Chinar-F | Chinar-L | Chinar-P | |
|---|---|---|---|
| Built for | Every call at volume: QA, search, analytics | Documents people read: minutes, reports, subtitles | Sites without a GPU: branches, closed networks |
| Accuracy on phone calls | Highest — 23% fewer errors than a commercial cloud service | High, close to Chinar-F | On a par with a commercial cloud service |
| Readable text | Basic | Best — punctuation, capitals, numbers as digits | Punctuation and capitals |
| Hardware | CPU or GPU | GPU | CPU or GPU — no GPU needed |
| Cost at volume | Lowest | Medium | Low |
Transcribe every conversation instead of a sample, and read the result in Azerbaijani without translating it first.
Punctuated transcripts fit to sit in a compliance record, produced without sending the recording to a third party.
Run Chinar-F across an archive to power search, analytics and quality monitoring at a cost per hour that makes full coverage affordable.
Interviews and meetings transcribed in Azerbaijani, on infrastructure you control.
In head-to-head testing on the same audio, two major 2026 speech services — one commercial, one open — produced Azerbaijani output that was effectively unusable. Chinar handled the same recordings correctly. That gap is not a tuning difference: those services do not support the language at all.
Chinar is trained on genuine call-centre recordings, with the noise, the interruptions and the phone-line quality that come with them. Many engines are trained on read speech and fall apart on a real conversation.
Measured against the cloud services we benchmarked, so a day of recordings is processed in minutes rather than left in a queue.
Chinar-F, Chinar-L and Chinar-P are all trained on Azerbaijani phone audio, so you pick the size for the job rather than the language support: F for volume, L for documents people read, P for sites without a GPU. None of them bills per minute — the cost does not grow with call volume.
The çinar is the oriental plane tree — the broad, long-lived tree that shades courtyards and village squares across Azerbaijan, old enough that people meet under the same one their grandparents did. It is a local thing, deeply rooted, and it outlasts whatever is planted beside it. The name fits a speech model built for this language rather than borrowed from the one next door.
Chinar runs on your own infrastructure, so customer conversations never leave your building: no audio goes to a third party, nothing is retained outside your network, and no per-hour meter runs against a cloud account. The 93–94% figure is word accuracy for Chinar-L, our GPU model, on clear Azerbaijani speech, measured against human-checked transcripts — noisier audio scores lower, and we would rather state the condition than quote a number without it.
Allmaz is the AI product studio of Smart Solutions, which built and operates Azerbaijan's unified public procurement portal — established by presidential decree and delivered as one of the country's first public-private partnerships in digital government.
Request a demo and we will run Chinar on your own Azerbaijani recordings — Chinar-F for the volume, Chinar-L for the transcript people read, Chinar-P where there is no GPU.