# Indian Language Text to Speech: 2026 Coverage

> Reference table of Indian-language text-to-speech coverage in 2026: 30 languages with script, speaker count and voices available, from Hindi and Bengali to Santali, Bodo, Tulu and Manipuri.

Source: https://edesy.in/blog/indian-language-text-to-speech-coverage-2026

**Short answer:** around **thirty Indian languages** have usable text to speech in 2026 — from
Hindi and Bengali down to Santali, Bodo, Tulu and Manipuri. The table below lists each with its
script, speaker count and the number of voices available.

Most of India's several hundred remaining languages have **nothing**, and the reason is not
technical difficulty. It is the availability of digitised, transcribed speech.

## The Coverage Table

Every language below has a working generator page you can try. Voice counts are the distinct
voices available per language.

| Language | Native | Speakers | Script | Voices | Region |
|---|---|---|---|---|---|
| [Hindi](https://edesy.in/ai-voice-assistant/voice-generator/hindi) | हिन्दी | 600M+ | Devanagari | 12 | North India |
| [Bengali](https://edesy.in/ai-voice-assistant/voice-generator/bengali) | বাংলা | 270M+ | Bengali | 8 | West Bengal & Bangladesh |
| [Punjabi](https://edesy.in/ai-voice-assistant/voice-generator/punjabi) | ਪੰਜਾਬੀ | 113M+ | Gurmukhi | 7 | Punjab & global |
| [Marathi](https://edesy.in/ai-voice-assistant/voice-generator/marathi) | मराठी | 83M+ | Devanagari | 8 | Maharashtra |
| [Telugu](https://edesy.in/ai-voice-assistant/voice-generator/telugu) | తెలుగు | 82M+ | Telugu | 8 | Andhra Pradesh & Telangana |
| [Tamil](https://edesy.in/ai-voice-assistant/voice-generator/tamil) | தமிழ் | 75M+ | Tamil | 8 | Tamil Nadu & global |
| [Urdu](https://edesy.in/ai-voice-assistant/voice-generator/urdu) | اردو | 70M+ | Perso-Arabic (Nastaliq) | 7 | India & Pakistan |
| [Gujarati](https://edesy.in/ai-voice-assistant/voice-generator/gujarati) | ગુજરાતી | 55M+ | Gujarati | 7 | Gujarat |
| [Bhojpuri](https://edesy.in/ai-voice-assistant/voice-generator/bhojpuri) | भोजपुरी | 50M+ | Devanagari | 5 | Bihar, UP & diaspora |
| [Kannada](https://edesy.in/ai-voice-assistant/voice-generator/kannada) | ಕನ್ನಡ | 44M+ | Kannada | 7 | Karnataka |
| [Odia](https://edesy.in/ai-voice-assistant/voice-generator/odia) | ଓଡ଼ିଆ | 40M+ | Odia | 6 | Odisha |
| [Malayalam](https://edesy.in/ai-voice-assistant/voice-generator/malayalam) | മലയാളം | 38M+ | Malayalam | 7 | Kerala |
| [Awadhi](https://edesy.in/ai-voice-assistant/voice-generator/awadhi) | अवधी | 38M+ | Devanagari | 3 | Uttar Pradesh |
| [Maithili](https://edesy.in/ai-voice-assistant/voice-generator/maithili) | मैथिली | 35M+ | Devanagari / Tirhuta | 4 | Bihar & Nepal |
| [Nepali](https://edesy.in/ai-voice-assistant/voice-generator/nepali) | नेपाली | 32M+ | Devanagari | 5 | Nepal & India |
| [Sindhi](https://edesy.in/ai-voice-assistant/voice-generator/sindhi) | سنڌي | 30M+ | Perso-Arabic / Devanagari | 4 | India & Pakistan |
| [Rajasthani](https://edesy.in/ai-voice-assistant/voice-generator/rajasthani) | राजस्थानी | 25M+ | Devanagari | 4 | Rajasthan |
| [Marwari](https://edesy.in/ai-voice-assistant/voice-generator/marwari) | मारवाड़ी | 20M+ | Devanagari | 4 | Rajasthan |
| [Chhattisgarhi](https://edesy.in/ai-voice-assistant/voice-generator/chhattisgarhi) | छत्तीसगढ़ी | 18M+ | Devanagari | 3 | Chhattisgarh |
| [Assamese](https://edesy.in/ai-voice-assistant/voice-generator/assamese) | অসমীয়া | 15M+ | Eastern Nagari | 5 | Assam |
| [Haryanvi](https://edesy.in/ai-voice-assistant/voice-generator/haryanvi) | हरियाणवी | 10M+ | Devanagari | 4 | Haryana |
| [Santali](https://edesy.in/ai-voice-assistant/voice-generator/santali) | ᱥᱟᱱᱛᱟᱲᱤ | 7M+ | **Ol Chiki** | 4 | Jharkhand, WB, Odisha |
| [Kashmiri](https://edesy.in/ai-voice-assistant/voice-generator/kashmiri) | کٲشُر | 7M+ | Perso-Arabic / Devanagari | 4 | Jammu & Kashmir |
| [Dogri](https://edesy.in/ai-voice-assistant/voice-generator/dogri) | डोगरी | 2.6M+ | Devanagari | 3 | Jammu & Himachal |
| [Garhwali](https://edesy.in/ai-voice-assistant/voice-generator/garhwali) | गढ़वळि | 2.5M+ | Devanagari | 3 | Uttarakhand |
| [Konkani](https://edesy.in/ai-voice-assistant/voice-generator/konkani) | कोंकणी | 2.5M+ | Devanagari | 3 | Goa & Konkan coast |
| [Kumaoni](https://edesy.in/ai-voice-assistant/voice-generator/kumaoni) | कुमाऊँनी | 2M+ | Devanagari | 3 | Kumaon, Uttarakhand |
| [Tulu](https://edesy.in/ai-voice-assistant/voice-generator/tulu) | ತುಳು | 2M+ | Kannada / Tigalari | 3 | Coastal Karnataka & Kerala |
| [Manipuri](https://edesy.in/ai-voice-assistant/voice-generator/manipuri) | ꯃꯩꯇꯩꯂꯣꯟ | 1.8M+ | **Meetei Mayek** | 3 | Manipur |
| [Bodo](https://edesy.in/ai-voice-assistant/voice-generator/bodo) | बड़ो | 1.5M+ | Devanagari | 3 | Assam |

Neighbouring-region languages with coverage: [Sinhala](https://edesy.in/ai-voice-assistant/voice-generator/sinhala)
(සිංහල, 17M+, Sri Lanka), [Burmese](https://edesy.in/ai-voice-assistant/voice-generator/burmese) (မြန်မာ, 33M+),
[Khmer](https://edesy.in/ai-voice-assistant/voice-generator/khmer) (ខ្មែរ, 16M+).

## Speaker Count Does Not Predict Support

The most useful thing this table shows is how weakly the two correlate.

| Language | Speakers | Voices |
|---|---|---|
| Bhojpuri | 50M+ | 5 |
| Awadhi | 38M+ | 3 |
| Sinhala | 17M+ | 5 |
| Santali | 7M+ | 4 |

**Awadhi has over five times Sinhala's speakers and fewer voices.** Support tracks the size of
the digitised, transcribed, licensable corpus — which depends on publishing, broadcasting,
government output and academic research — not on how many people speak a language at home.

Languages that are primarily *spoken* rather than *published* are systematically
under-represented, regardless of size. That is the structural reason a language like Bhojpuri,
with 50 million speakers and a film industry, has thinner tooling than languages a fraction of
its size.

## The Four Things That Make a Language Hard

Reading down the table, the difficult cases share identifiable properties:

**1. A script with little digital text.** Santali's **Ol Chiki** and Manipuri's **Meetei Mayek**
have their own Unicode blocks and comparatively little material in them. A model trained broadly
on Indian text gets almost nothing for free.

**2. No well-resourced relative to borrow from.** Haryanvi and Bhojpuri can lean on Hindi;
they are Indo-Aryan and closely related. **Santali is Munda** and **Manipuri is Tibeto-Burman** —
neither has a large, well-resourced neighbour to transfer from. This is usually the single
biggest factor.

**3. Dual-script usage that splits the corpus.** **Sindhi** (Perso-Arabic and Devanagari),
**Kashmiri** (same split), **Manipuri** (Meetei Mayek and Bengali script) and **Maithili**
(Devanagari and Tirhuta) each have their available material divided between two writing systems,
effectively halving the data for either.

**4. Writing conventions that complicate processing.** Not an Indian example, but the clearest
one regionally: **Burmese is written without spaces between words**, so the system must segment
the text itself before it can pronounce it.

## What Still Has Nothing

Worth stating plainly, because a coverage table can imply more completeness than exists. India
has several hundred languages and the great majority have **no text to speech at all**, including:

- **Kutchi** — closely related to Sindhi, distinct, unsupported
- **Magahi** — tens of millions of speakers in Bihar and Jharkhand
- Most **Tibeto-Burman languages of the Northeast** beyond Manipuri and Bodo — Mizo, Khasi, Ao,
  Angami and dozens of others
- Most **Austroasiatic languages** beyond Santali — Ho, Mundari, Kurukh
- **Tibetan varieties** spoken in India
- The **Andamanese** languages, several of which are critically endangered

For endangered languages the ordering is worth being honest about: **documentation and recording
must come before synthesis.** There is no model without a corpus, and building the corpus is
linguistic and community work, not engineering work.

## Text to Speech Is Not a Voice Agent

A distinction that matters when planning anything on top of this table.

| | What it needs | Maturity in these languages |
|---|---|---|
| **Text to speech** — read a script aloud | Synthesis only | **Usable across all thirty** |
| **Speech recognition** — understand a speaker | Large transcribed audio corpus | Much thinner |
| **Conversational voice agent** | Both, plus understanding | Emerging; bilingual in practice |

In practice, services aimed at low-resource-language speakers run **bilingually**: the local
language for prompts and announcements the user hears, with a better-supported language handling
the parts that require comprehension. See [Indian-language voice AI](https://edesy.in/indian-language-voice-ai).

## Using This Table

Every language links to a generator page where you can paste text and hear the result on a free
tier. For a language you are unfamiliar with, the fastest evaluation is to have a native speaker
listen to thirty seconds and tell you whether proper nouns survive — that is where low-resource
synthesis breaks first.

Deeper guides for the harder cases:

- [Santali and the Ol Chiki script](https://edesy.in/blog/santali-voice-ai-ol-chiki-text-to-speech-2026)
- [Manipuri and the two-script problem](https://edesy.in/blog/manipuri-meetei-mayek-voice-ai-2026)
- [Sindhi across Perso-Arabic and Devanagari](https://edesy.in/blog/sindhi-text-to-speech-voice-ai-guide-2026)
- [Burmese, Zawgyi and word segmentation](https://edesy.in/blog/burmese-myanmar-ai-voice-generator-2026)
- [Haryanvi vs Hindi](https://edesy.in/blog/haryanvi-voice-ai-generator-guide-2026)
- [Bhojpuri and its diaspora](https://edesy.in/blog/bhojpuri-voice-ai-generator-guide-2026)

## Common Questions

### How many Indian languages have text to speech?
Around thirty at a usable level, listed above. India has several hundred languages in total.

### Why do big platforms skip most Indian languages?
Training data economics. The corpus does not exist at the scale their pipelines assume, and
building it is expensive relative to the addressable market.

### Are these voices recordings of real speakers?
They are synthesised, not recordings of a specific named person. For content where authenticity
is the point, a human recording is still better; synthesis wins when you have fifty items to
produce rather than one.

### Can I use these for commercial content?
There is a free tier for evaluation. Check plan terms for commercial use.

### Which language has the best quality?
Broadly, the ones with the largest corpora — Hindi, Bengali, Tamil, Telugu, Marathi. Quality
declines predictably as you move down the speaker-and-corpus scale.

### How do I request a language that is missing?
Missing languages are usually a data problem rather than a decision. If you have access to a
transcribed speech corpus in an unsupported language, that is the thing that unblocks it.

## Related Reading

- [Voice AI language adoption in India](https://edesy.in/blog/voice-ai-language-adoption-india-2026)
- [Voice AI languages supported across all platforms](https://edesy.in/blog/voice-ai-languages-supported-all-platforms-2026)
- [Multilingual voice support for global business](https://edesy.in/blog/multilingual-voice-support-global-business)
- [Code-switching in Hindi-English voice AI](https://edesy.in/blog/code-switching-voice-ai-hindi-english)
- [All supported languages](https://edesy.in/ai-voice-assistant/languages)
- [Indian-language voice AI](https://edesy.in/indian-language-voice-ai)

## Frequently Asked Questions

### How many Indian languages have working text to speech in 2026?

Roughly thirty Indian languages have usable text-to-speech today at the level of generating intelligible narration from written text. That includes all the major scheduled languages plus a number of regional varieties such as Bhojpuri, Haryanvi, Marwari, Garhwali, Kumaoni, Chhattisgarhi and Awadhi, and low-resource scheduled languages such as Santali, Bodo, Dogri and Manipuri.

### Which Indian languages still have no text to speech?

Most of India's several hundred smaller languages have nothing. Notable gaps include Kutchi, Magahi, Tibetan varieties spoken in India, most Austroasiatic and Tibeto-Burman languages of the Northeast beyond Manipuri and Bodo, and the Andamanese languages. The limiting factor is digitised, transcribed audio rather than technical difficulty.

### Does having more speakers mean better voice support?

Only loosely. Support tracks how much speech has been digitised, transcribed and licensed, not population. Bhojpuri has over 50 million speakers and thinner support than Sinhala with 17 million, because a language's corpus depends on publishing, broadcasting and research activity rather than headcount.

### What makes a language hard for text to speech?

Four things compound: a script with little digital text, no closely-related well-resourced language to transfer from, dual-script usage that splits the available corpus, and writing conventions such as the absence of word spacing. Santali hits the first two, Sindhi and Manipuri the third, Burmese the fourth.

### Can text to speech in these languages hold a conversation?

No. Text to speech reads a script you wrote. A conversational voice agent additionally needs speech recognition and language understanding, which are considerably harder and much less mature for low-resource languages. Services in these languages commonly run bilingually.