model · Sarvam AI
Speech Recognition & Transcription with Sarvam AI
Speech Recognition & Transcription built on Sarvam AI, chosen where it genuinely fits, and swapped where it does not.
- Category
- model
- Vendor
- Sarvam AI
- Alternatives we also use
- 6
Why Sarvam AI for this
Code-mixing is normal in Indian speech and a general model often mishandles it. We test specifically for the switch points.
Sarvam AI is strongest at Indian language coverage and speech quality that general models do not match. For speech recognition & transcription that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: narrower scope than a general frontier model. We pair it rather than replace with it. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. Every engagement opens with a measurement: the cycle time, the cost per transaction, or the error rate we are being asked to move.
You own the code, the models where they are open-weight, and the documentation to run it without us.
The honest assessment
- What it is
- Indian-language models and speech stack, built for Indian accents and code-mixed speech.
- Strongest at
- Indian language coverage and speech quality that general models do not match
- Trade-off
- narrower scope than a general frontier model. We pair it rather than replace with it
- Category
- model
We are not a reseller for Sarvam AI and hold no commission on this choice. Where a different option fits your workload better, the recommendation will say so. That is the entire value of asking us.
What is included
- Domain vocabulary tuning for your terminology
- Speaker diarisation, who said what
- Indian language and accent handling, including code-mixing
- Timestamped output linked to the audio
- Word error rate measured on your own recordings
- Integration with your EMR, CRM or case system
Questions
How accurate is it for Indian accents?
Good and improving, but the honest answer depends on audio quality, accent and domain. We benchmark word error rate on your own recordings before you commit.
Can it separate speakers?
Yes, speaker diarisation labels who said what, which is essential for clinical, legal and contact-centre records.
Does the audio leave our environment?
Only if you allow it. We can deploy fully on-premise where confidentiality or regulation requires it.
Alternatives for speech recognition & transcription
Same capability, different stack. Each page states its own trade-off.
What else we build on Sarvam AI
Building with Sarvam AI?
Bring us the workload and we will tell you whether this is the right stack for it.
Or email bd@dtrasglobal.com · call +91 74118 77878