Text to speech

Text to speech built for the way Indians actually write

Paste a script in Devanagari, in Roman-script Hinglish, or in any of 32 other languages, and get a natural WAV back. No recording needed — preset voices work on the free tier.
Languages
34, including 12 Indian language options
Input
Typed or pasted text, native script or Roman-script Hinglish
Output
WAV — playable, croppable, downloadable, shareable
Free allowance
3 min and 3,000 characters per month

How neural text to speech works

A neural text-to-speech system does two jobs. First it works out how the sentence should be said: which syllables carry stress, where the pauses fall, whether the pitch rises at the end. Then it renders that plan as a waveform. The second job is largely solved — most tools produce clean audio. The first is where they diverge, and where a model trained on the language you are generating beats a better-sounding model that was not.

This is why “which model sounds best” is the wrong question for Indian languages. A system that has never heard Hindi prosody will place stress like an English sentence and be immediately identifiable as wrong, no matter how clean its output is.

The preprocessing that decides whether it sounds right

Before any audio is generated, the text goes through steps that matter more than model choice for real scripts:

  • Script normalisation. English words inside Indian-language text are converted to the local script. Skip this and models trained without Latin-script audio drop those words entirely rather than mispronouncing them — a silent failure that is easy to ship by accident.
  • Sentence-boundary chunking. Long text is split before generation, on sentence boundaries including the Devanagari danda (।), then stitched. Splitting at a fixed character count is what makes long narration break mid-clause.
  • Language routing. Hindi, Hinglish and English are three separate paths, not one path with a flag. Picking the wrong one is the single most common cause of odd output, which is why they are three visible choices in the interface.

When to use text to speech and when to clone

Use a preset voice when the voice does not need to be yours: product explainers, IVR-style announcements, accessibility readbacks, subtitle audio, quick drafts before you commit to a final take, and anything where a consistent neutral narrator is the point.

Use voice cloning when the voice is part of the brand: a personal channel, a course whose students know your voice, a podcast, or a series where episode 40 has to sound like episode 1.

The longer comparison, including the licensing and consent differences between the two, is in voice cloning vs text to speech.

Frequently asked questions

What is text to speech?

Text to speech converts written text into spoken audio using a synthetic voice. Modern neural systems predict how a sentence should sound — where stress and pauses fall — rather than stitching together recorded fragments, which is why they sound continuous rather than robotic.

Is text to speech the same as voice cloning?

No. Text to speech answers 'read this out'. Voice cloning answers 'read this out in this specific person's voice'. Cloning is text to speech plus a reference recording that conditions the voice.

Does Mayantar do Hindi text to speech?

Yes, from Devanagari text, and Hinglish from Roman-script text as a separate option. Ten more Indian languages are supported alongside them.

Can I use text to speech for free?

Yes — 3 minutes and 3,000 characters a month with preset voices, no card required. Output is watermarked on the free tier.

How long a document can I convert?

Long text is split on sentence boundaries and stitched back into one continuous file, so chapter-length scripts work. The practical ceiling is your plan's monthly character allowance: 3,000 on free up to 800,000 on Premium Plus.

Keep reading

Paste a script and hear it

Free tier, preset voices, no card. If it does not sound right, you have lost two minutes.