AI voiceover in Indian languages: voice cloning, text-to-speech and pronunciation fixes

Narration is where most AI videos lose viewers. Images can be stylised and still look good; a flat, robotic or mispronouncing voice makes people click away in seconds. This is especially true in Indian languages, where many tools still sound like they were built for English first.

Our studio voices videos in 30 Indian and global languages, and our channel Bible Tales got 1,22,541 views and 3,128 watch hours in 7 days (Oct 11–17, 2026) on AI narration alone. Here's what we learned about getting it right.

Text-to-speech vs voice cloning

For a channel, consistency matters more than variety. Viewers come back for a voice they recognise. We use one master voice per channel and per language.

Local vs cloud voices

Local (free, on your GPU)

• IndicF5, an open text-to-speech model for Indian languages from AI4Bharat, does zero-shot voice cloning: give it a reference clip and it speaks new text in that voice.
• F5-TTS is the general-purpose model IndicF5 builds on.
• These run on an NVIDIA GPU. In our studio, voice and image models load one at a time and are cleared from GPU memory after each batch, so both fit on a 16 GB card.

Cloud (no GPU needed)

• Gemini TTS and OpenAI TTS both offer ready-made voices through an API. Our studio has test buttons for each so you can hear a sample before committing.
• Language coverage and quality differ by provider and language and change often, so test your language rather than trusting a list.

You can mix them: write scripts with a cloud model, narrate locally, or the reverse. I compare costs and trade-offs in free local AI vs paid APIs.

Recording a good reference sample for cloning

The clone can only be as good as the sample.

1. Quiet room, no fan or echo. A phone in a cupboard full of clothes beats a fancy mic in a bare room.
2. Natural, steady pace in the tone you want for narration, not a reading voice.
3. A short, clean clip with no music or background noise. Trim breaths and silences at the start and end.
4. Same language as the narration where possible; accent carries over from the sample.
5. Keep the original file and reuse it for every video so the narrator never changes.

Fixing pronunciation (the part that matters most)

Every language has names, places and borrowed words that models stumble over. Fixing them at the source works better than re-recording.

1. Phonetic respelling

If a name comes out wrong, respell it in the script the way it sounds. Keep the correct spelling for subtitles and the respelt version for the voice. A good pipeline holds both.

2. A pronunciation list

Keep a simple list of "written form → spoken form" pairs for your channel and apply it automatically before every voice generation. It grows with each episode and stops the same mistake coming back.

3. Numbers, dates and abbreviations

Write them out the way they should be spoken. "1,22,541" may be read in an unexpected way; spell it out in your narration script.

4. Punctuation for pacing

Commas and full stops control pauses. Split long sentences. If a line sounds rushed, add a pause rather than asking for a slower voice overall.

5. Listen back, automatically

Run speech-to-text on every generated file and compare it with the script. Lines where the transcript doesn't match are your mispronunciations and skipped words. This is how our studio catches problems without a person listening to every second.

Loudness, music and subtitles

• Loudness: keep narration at a consistent level across scenes. Our studio has pacing and vocal loudness controls for this.
• Music: one continuous bed with ducking, so it dips automatically under the voice.
• Subtitles: time them to the actual audio using voice-activity detection, not to the script. Use fonts that properly support your script so letters don't break apart. We render Indic scripts with HarfBuzz and Noto fonts.

One video, many languages

Once the pipeline works for one language, you can produce others from the same story. In our studio each language gets its own script, voice files, subtitles and master video in a separate folder, so a translation never overwrites the original, and "Batch all languages" produces every track one after another.

Two tips:

• Translate the script, then re-check it in the target language. Don't voice a raw machine translation.
• Use a native-sounding reference voice for each language rather than one voice speaking all of them.

Consent, disclosure and licences

• Consent: clone only your own voice or a voice whose owner has given clear, written permission. Never clone celebrities, politicians or anyone who hasn't agreed. We only do voice cloning for client setups with the speaker's written consent.
• YouTube disclosure: YouTube's page lists cloning one's own voice to create voice overs or dubs as something you don't need to disclose. Making a real person appear to say something they didn't say does need disclosure (source).
• Licences: some popular open voice models are released for non-commercial use only, and some free online voices have no commercial licence. For monetized or client work, check the model card or use a commercially licensed option. Our client setups use models licensed for commercial use.

A quick quality checklist

Before you publish, check:

• Names and places sound right (respelling list applied)
• Numbers and dates are spoken naturally
• No skipped or repeated words (speech-to-text check passed)
• Even loudness across scenes, music ducked under the voice
• Subtitles timed to the audio, with no broken characters
• Voice used with consent and a licence that allows your use

Getting narration right is a big part of what we teach in the weekend workshop: you leave with a voiceover in your language and a pronunciation list you can keep using. To see where voice fits in the whole pipeline, read How I got 1.22 lakh views in 7 days.