Speech to Text API for Indian Languages: How to Choose
Picking a speech to text API for Indian languages looks easy on paper. Every vendor lists Hindi, Tamil, Malayalam and a dozen more, and every vendor says it is accurate. Then you put it on a phone line, a caller in Kochi switches between Malayalam and English mid-sentence, the line drops to 8 kHz, and the transcript turns to noise.
This guide is for teams building voice agents, call analytics or voice-enabled apps who need Indian language speech recognition that works outside a demo. We cover what to test, the main types of providers, how pricing actually adds up, and how to avoid locking yourself into one vendor.
What makes Indian language speech to text hard
Indian language ASR has a few problems that English-first systems rarely face at the same time:
- Code-mixing. Real callers say “mera order cancel karna hai” or mix English numbers into Tamil sentences. A model trained only on clean Hindi or clean Tamil will stumble.
- Narrowband phone audio. Telephone audio is usually 8 kHz G.711. Models tuned on 16 kHz app recordings can lose accuracy on calls, and upsampling does not bring the missing frequencies back.
- Accent and dialect spread. Hindi in Patna and Hindi in Pune are not the same input. Kannada and Telugu vary by district too.
- Numbers, names and addresses. The words that matter most in business calls (amounts, dates, PIN codes, locality names) are the ones generic models get wrong most often.
- Short replies. On calls, many answers are one word: “haan”, “illa”, “ok”. Some recognisers drop very short first utterances entirely.
So the question is not “does it support Bengali?” but “does it transcribe a noisy, code-mixed Bengali phone call quickly enough to answer in real time?”
Streaming vs batch: decide this first
STT APIs come in two shapes, and the choice matters more than most accuracy tables.
| Mode | How it works | Best for | Trade-off |
|---|---|---|---|
| Streaming (WebSocket) | Audio is sent as it arrives; partial text comes back while the person is still talking; the provider decides when the turn ended | Live voice agents, IVR replacement, real-time agent assist | Needs a persistent connection per call; more moving parts |
| Batch (REST) | You detect the end of speech yourself, then upload the whole utterance or file | Call recordings, voicemail, analytics, simple reminder or survey bots | Adds a noticeable pause on every turn in a live conversation |
If you are building a conversational agent, start with streaming providers. Batch recognition is fine where the caller says little, such as a yes/no confirmation call, and it is often cheaper. For a deeper look at where the milliseconds go, see our guide on reducing latency in real-time speech recognition.
The main options in 2026
Broadly, you will choose between four groups. Feature sets change often, so treat this as a starting map and verify current language lists and pricing on each vendor’s site.
India-focused API providers
Companies such as Sarvam AI build models specifically for Indian languages. As of 2026, Sarvam’s own product page says its Saaras models cover 22 Indian languages with code-mixing and language detection support. Providers in this group usually handle Hinglish and regional accents better than global defaults, and they offer streaming endpoints suited to phone calls.
Global cloud providers
Google Cloud Speech-to-Text supports many Indian languages, offers telephony-tuned models, and lets you choose regional endpoints. It is a natural fit if you already run on Google Cloud and need enterprise contracts. Accuracy on code-mixed speech varies by language, so test it on your audio rather than relying on a feature list.
Fast general-purpose models
Deepgram, Groq-hosted Whisper, OpenAI transcription models and Gemini can all transcribe Indian languages to some degree. They are strong for English-first or international callers and can be very cheap per minute. Many of them work best in batch mode for Indian languages.
Open-source and self-hosted Indic ASR
Open models have improved a lot. AI4Bharat’s IndicConformer 600M multilingual model is released under the MIT licence and its model card says it targets all 22 scheduled Indian languages. Self-hosting means no per-minute fees and audio that never leaves your servers, at the cost of running and monitoring your own inference service. Our open-source VEXYL-STT server packages self-hosted recognition for 14 Indian languages with WebSocket streaming and a batch REST API, and runs on CPU. We explain the set-up in this VEXYL-STT walkthrough.
A practical evaluation checklist
Vendor accuracy claims are measured on vendor-chosen data. The only benchmark that matters is your own audio. Here is a test plan that takes a few days, not months:
- Collect 50 to 100 real clips per language. Use actual call recordings (with consent and masking) or record staff reading typical customer lines over a phone, not a studio mic.
- Include the hard cases. Code-mixed sentences, amounts, dates, phone numbers, local place names, background traffic, a speakerphone and a cheap handset.
- Write reference transcripts by hand. Then calculate word error rate, but also a simpler “did it get the important field right?” score for names, numbers and intents.
- Measure time to final text. For streaming, note how long after the caller stops speaking the final transcript arrives. That number becomes part of every bot reply.
- Test short replies. Feed a single “yes”, “haan” or “venda” at the start of a call. If it gets swallowed, your bot will feel deaf.
- Check failure behaviour. What happens when the API times out or the socket drops? Can you fall back to a second provider?
Language-specific notes are in our separate guides to Malayalam and Tamil speech to text.
Speech to text API pricing: what you actually pay
STT pricing is usually per second or per minute of audio processed. A few details change the real bill a lot:
- What gets billed. On a voice agent, you often bill recognition only while the system is listening, not while the bot is talking. That can be well under half of the call duration.
- Billing increments. Some APIs round each request up to 15 seconds or more. Many short batch requests can cost more than the raw minutes suggest.
- Model tiers. Telephony-enhanced or premium models can cost more than the base model. Check whether that tier is on by default.
- Volume and contracts. List prices are a ceiling. At scale, most vendors negotiate.
- Self-hosting. No per-minute fee, but you pay for servers and someone’s time. It tends to win at steady high volume or where data must stay on-premises.
In practice, recognition is rarely the biggest line item in a voice agent. The language model and the voice usually cost more. In our live hospital deployment, the whole AI call (STT, LLM and TTS together) came to ₹0.83 per minute. So choose STT for accuracy and speed first, and optimise the price second.
Data residency and DPDP
Voice recordings and transcripts are personal data. Under India’s DPDP Act, you need a lawful basis, a clear purpose and sensible retention for them. When you send audio to an external API, ask where it is processed, whether it is stored, and whether it is used for training. If those answers are hard to get, a self-hosted model or a provider with an Indian region is the safer default. This is general information, not legal advice.
How VEXYL fits
The VEXYL Voice Gateway does not force one recogniser on you. It supports Sarvam (streaming and batch), Google, Deepgram, Groq, Gemini, OpenAI and the self-hosted VEXYL-STT, and you switch between them with one setting. An auto mode routes Indian languages to Sarvam and other languages to Groq, and any single call can override the default through its session metadata or a SIP header. That makes it easy to run the evaluation above on live traffic: send one number to one provider, another number to a second, and compare.
The gateway also handles the telephony details that trip up raw APIs: sending native 8 kHz phone audio, filling silence gaps from handsets that stop transmitting, holding speech said while the bot is busy, and filtering hold music or carrier announcements before they reach the bot. All provider settings are documented in the speech recognition chapter of the docs.
FAQ
Which is the best speech to text API for Indian languages?
There is no single winner for every language and use case. India-focused providers often lead on code-mixed phone audio, global clouds suit enterprise estates, and self-hosted models win on data control and cost at volume. Test two or three on 100 of your own calls before you decide.
Is there a free speech to text API for Indian languages?
Open-source models such as AI4Bharat’s IndicConformer and our VEXYL-STT server have no licence or per-minute fees. You still pay for the server you run them on. Most commercial APIs offer free trial credits rather than a permanent free tier.
Can one API handle Hinglish and other code-mixed speech?
Several can, but quality varies. Look for explicit code-mixing support in the documentation, and include mixed sentences in your test set. Some providers have a separate code-mix or transliteration mode you need to switch on.
Should I use streaming or batch STT for a voice bot?
Use streaming for anything conversational, because batch adds a pause to every turn. Batch is fine for recordings, analytics and short confirmation or survey calls where the caller only says a word or two.
How do I keep call audio inside India?
Either choose a provider with an Indian processing region and confirm its retention terms, or self-host the recogniser on your own servers. Self-hosting gives you full control over where audio and transcripts live.
Want to try several Indian language recognisers on your own phone line? Start with the free, self-hosted VEXYL Developer edition, or talk to us about a hosted set-up.