Transcripts people read
A high-accuracy model that returns punctuated, capitalised Azerbaijani — ready for supervisors, auditors and customers.
- Call recordings, interviews, meetings
- Speaker separation
- Editable, exportable transcripts
Azerbaijani speech recognition
Speech-to-text that actually speaks Azerbaijani
Global speech engines often return confident text in a neighbouring language when given Azerbaijani audio. Our speech recognition is built for Azerbaijani from the ground up, trained on real phone-line recordings, and runs on your own infrastructure.
Request a pilot on your dataThe problem
Why this matters
Wrong language
Confident, and wrong
Generic engines produce fluent text that isn’t Azerbaijani — and nobody notices until it reaches a report.
Studio audio
Trained on the wrong speech
Read-aloud training data breaks on noise, interruptions and phone-line quality.
AZ + RU
Code-switching
Real conversations mix Azerbaijani and Russian mid-sentence.
Per hour
Metered and off-premises
Cloud transcription bills by the hour and keeps its own copy of your recordings.
What you get
Capabilities
Transcripts machines read
A compact model for volume: transcribe every call, index an archive, or feed analytics and QA.
- Full coverage instead of sampling
- Runs on modest hardware
- Batch and streaming
Integrations
Connect to telephony, contact-centre platforms and meeting tools, or use the API directly.
- Call analytics and QA scoring
- Subtitles for training and media
- Search across recordings
Products
The Allmaz Lab products behind this capability
Adventa is an authorised partner of Allmaz Lab — we deploy, integrate and support these products.
Chinar
Speech recognition built for Azerbaijani, in two sizes: one for transcripts people read, one for volume.
- 87% word accuracy on clear Azerbaijani speech
- Chinar-F ≈ 50× smaller for full-archive coverage
- 4–7× faster than benchmarked cloud services
- Trained on real call-centre audio
How it works
From input to outcome
- 1 Test on a sample of your real recordings
- 2 Choose the model for readability or volume
- 3 Deploy on your infrastructure
- 4 Connect telephony, archives or apps
- 5 Monitor accuracy and improve on your audio
Deployment & trust
Yours to control
Audio never leaves your building: no third-party retention and no per-hour metering. Our team researches and builds Azerbaijani speech recognition, and we measure accuracy on your own recordings before you commit.
- Hosting
- On-prem
- Languages
- AZ · RU · EN
- Data egress
- None
Who it’s for
Made for the people who use it
Contact centres
Transcribe and score every call instead of a small sample.
Compliance & audit
Punctuated transcripts fit for a compliance record, produced in-house.
Media & public sector
Interviews, hearings and meetings transcribed in Azerbaijani.
Product teams
Voice interfaces and assistants that understand Azerbaijani speakers.
In oil, gas & energy
Where it applies on the asset
Shift handovers and radio logs
Searchable transcripts of operational communications.
HSE investigations
Interview transcripts in Azerbaijani, kept on your servers.
Customer hotlines
Quality and complaint monitoring for energy and utility service lines.
FAQ
Questions
Can we test it on our own recordings?
Yes. A pilot runs on a sample of your real audio so you can judge the Azerbaijani output before any commitment.
Does it handle mixed Azerbaijani and Russian?
Yes. Code-switching is common in real conversations and is part of what the models are built for.
What about noisy phone calls?
The models are trained on genuine call-centre recordings — background noise, interruptions and phone-line quality included.
Does it run on our servers?
Yes. Both models deploy on your infrastructure; the compact model runs comfortably on modest hardware.
Related
Speech & contact-centre AI
Azerbaijani speech recognition and 100% call analytics: every conversation transcribed, scored and searchable.
On-prem LLM · Azerbaijan
A large language model built for Azerbaijani, deployed on your own servers — including air-gapped networks.
AI strategy
Not sure where to start? A fixed-scope discovery sprint maps your use cases first.
See it on your own data
A pilot runs on your documents, calls or records — measured against criteria we agree up front.