Best Text to Speech for Indian Languages: Open-Source and Cloud

Searching for the best text to speech for Indian languages usually starts with Hindi and quickly gets harder. Almost every tool speaks Hindi. Far fewer speak Odia, Assamese, Punjabi or Konkani, and fewer still sound natural in them. If your customers are spread across India, coverage is the first filter, and quality on a real phone line is the second.

This guide looks at text to speech models for Indian languages from both cloud vendors and the open-source world, compares their coverage, and gives you a simple way to pick one for voice agents, apps or content.

India has 22 scheduled languages, and TTS coverage is uneven

The Eighth Schedule of the Constitution lists 22 languages. Most cloud TTS products focus on the largest ones: Hindi, Bengali, Marathi, Telugu, Tamil, Gujarati, Kannada, Malayalam and Punjabi, plus Indian English. Coverage of Odia, Assamese, Urdu and the smaller scheduled languages varies a lot. Open-source research models, especially from AI4Bharat at IIT Madras, have pushed coverage further.

Two practical points before comparing tools:

• “Supported” is not the same as “good”. A language can be listed with one stiff voice. Listen before you plan around it.
• Indian English is its own language for TTS. Callers notice a US or UK accent. Pick an Indian English voice for English calls.

Cloud TTS options for Indian languages

Sarvam AI

Sarvam’s Bulbul v3 covers 11 languages: Hindi, Bengali, Tamil, Telugu, Gujarati, Kannada, Malayalam, Marathi, Punjabi, Odia and Indian English, with 30+ voices according to its docs (Sarvam Bulbul docs). Sarvam has said it plans to extend to all 22 scheduled languages. It is built in India and focused on Indian speech, which shows in how it handles numbers and code-mixed text.

Google Cloud TTS

Google covers most major Indian languages with several voice families, including Chirp 3 HD voices. A good option for multi-language production if you are already on Google Cloud.

Microsoft Azure

Azure offers neural voices for a range of Indian languages and is often the default for enterprises with Microsoft agreements. Check its voice gallery for your exact languages.

ElevenLabs, Gemini, Smallest AI and others

ElevenLabs is strong on expressive voices and covers Hindi and some other Indian languages in its multilingual models. Gemini TTS offers expressive and custom-designed voices. Smallest AI focuses on fast, low-cost Indian-language voices with native 8 kHz telephone output. Coverage per language differs, so check each vendor’s current list.

Open-source text to speech models for Indian languages

AI4Bharat Indic Parler-TTS

The model card describes it as able to speak 20 Indic languages plus English. You control the voice with a text description (“a calm female voice, moderate pace, clear audio”) rather than picking a preset speaker. Its coverage is the main reason people choose it.

IndicF5

A newer open model for Indian languages that clones a voice from a short reference clip and its transcript. Coverage is narrower (around 11 languages). Check the official AI4Bharat release and licence before commercial use.

Veena

Veena, from Maya Research, is an Apache 2.0 model built on a 3B-parameter backbone. Its model card lists Hindi and English today, with other languages planned.

Open models avoid per-character fees and keep text on your servers, but you need hardware and someone to run it. Speed depends heavily on your GPU or CPU.

Coverage comparison

This table reflects public documentation at the time of writing. Vendors add languages often, so verify before you decide.

How to choose: three questions

1. Where will the voice be heard?

Phone calls need streaming output, quick first audio and voices that stay clear at 8 kHz. Apps need moderate speed and good quality on phone speakers. Content (videos, audiobooks, e-learning) can use slower, more expressive batch synthesis.

2. Which languages do you really need?

List your customers’ languages by volume. If 90% of calls are Hindi, Tamil and English, choose the best voice for those three; do not pick a weaker vendor just because it lists 22 languages. If you serve the North-East or need Konkani or Santali, open models may be your only option.

3. Can the text leave your servers?

Text sent to TTS often contains names, amounts and account details. For hospitals, banks and government services, self-hosting may be the deciding factor under the DPDP Act.

A short test plan

1. Write a 15-line script in each target language with greetings, numbers, dates, names and a few English words.
2. Generate it with two or three candidate voices per language.
3. Play the audio over a real phone call for voice agents, or on a phone speaker for apps.
4. Ask native speakers from different regions to rate clarity and naturalness.
5. For live use, measure time to first audio. Our TTS latency benchmark shows why this matters.

For Hindi specifically, our guide to Hindi text to speech AI goes deeper on voices, numbers and Hinglish.

Language-by-language notes

• Hindi: the most voices and the most competition. Choose on naturalness and English-word handling.
• Tamil and Malayalam: long words and frequent English borrowings. Test pronunciation of names and long compounds.
• Bengali and Marathi: well covered by Indian-focused vendors; compare at least two voices.
• Telugu and Kannada: good coverage, but voice choice is smaller than Hindi. Check regional naturalness.
• Punjabi and Odia: available from Indian-focused vendors and open models; fewer voices overall.
• Assamese, Urdu and smaller scheduled languages: open models are often the main option. Plan more time for testing with native speakers.
• Indian English: use an Indian English voice for Indian callers, even on English-only calls.

How VEXYL fits

VEXYL-TTS is our open-source (Apache 2.0) self-hosted text-to-speech server. It covers 22 Indian languages, from Hindi, Tamil and Bengali to Assamese, Konkani, Bodo, Dogri, Maithili, Manipuri, Santali, Sindhi and Indian English, with 44+ voices. It is built on AI4Bharat’s Indic Parler-TTS and offers WebSocket streaming and a batch API, so you can run it on your own server with no per-character fees.

For phone agents, the VEXYL Voice Gateway lets you mix providers: for example, Sarvam for Hindi and Tamil, Google for broad coverage, and VEXYL-TTS for a language the clouds do not cover or for calls where text must stay on-prem. You can set a voice per language and per call, and the gateway falls back to another configured provider if one fails mid-call. In the gateway the self-hosted VEXYL-TTS runs in batch mode, so expect a slightly longer pause than with streaming cloud voices. Every setting is in the text-to-speech docs. If you are weighing on-prem against hosted overall, see our guide to a self-hosted AI voice agent.

FAQ

Which is the best text to speech for Indian languages?

For the major languages on phone calls, Sarvam and the big clouds are strong options; test them. For the widest coverage or on-prem needs, Indic Parler-TTS based servers such as VEXYL-TTS.

Which TTS supports all 22 Indian scheduled languages?

Open-source models based on AI4Bharat’s work come closest. VEXYL-TTS lists 22 Indian languages. Most cloud vendors cover a subset.

Is there free text to speech for Indian languages?

Yes. Open models such as Indic Parler-TTS and Veena, and self-hosted servers like VEXYL-TTS, are free to use; you pay only for hardware.

Does Microsoft have text to speech in Indian languages?

Yes. Azure offers neural voices for several Indian languages. Check its current voice list for your languages.

Can one voice speak several Indian languages?

Some multilingual voices can. In most production setups, teams choose the best voice per language instead.

Try the languages you need

Run VEXYL-TTS on your own server, or plug it into the free Developer edition of the gateway alongside a cloud voice and compare them on real calls. For a multi-language rollout, contact the VEXYL team.