AI Narrator Voices

AI Narrator Voices

Narration is the one job where a voice has to hold up for hours rather than seconds. 163 of the 244 voices here carry the narration tag, the largest group in the catalog, across English, Hindi and nine other Indic languages.

Narrator ai voice

VOICES FOR THIS USE CASE

Ahanahan
MaleYoungIndian
Best for HindiUse voice
Ameliaamelia
FemaleYoungAmerican
Best for EnglishUse voice
Amritamrit
MaleYoungIndian
Best for HindiUse voice
Blakeblake
MaleYoungAmerican
Best for EnglishUse voice
Cressidacressida
FemaleYoungBritish
Best for EnglishUse voice
Elowenelowen
FemaleYoungBritish
Best for EnglishUse voice

What narration asks of a voice

A voice that is pleasant across thirty seconds is not necessarily tolerable across nine hours. Narration rewards consistency over character: an even pace, predictable emphasis, and no mannerism that becomes noticeable on the fortieth repetition. That is a different casting problem from advertising or dialogue, and it is why the narration tag exists separately.

Consistency across a long script

Requests cap at 250 characters, so a chapter is assembled from many calls rather than one. Keeping voice, speed and sample rate fixed across the whole run removes most variation. Generate a full page and listen across the joins before committing to a book, because that is where drift becomes audible.

The largest group in the catalog

163 voices carry the narration tag, roughly two thirds of the catalog. That breadth means you can cast for register rather than availability, which is rarely true of the more specialised tags. It spans every accent group and all 31 languages the model supports.

Regional language narration

Nine Indic languages carry recommended voices. Narration in Tamil, Telugu, Kannada, Marathi, Gujarati, Punjabi, Odia and Bengali is thinly served across the industry, and 144 voices here carry an Indic recommendation. For audio publishing aimed at Indian readers, that depth is unusual.

SPECIFICATION

Sample rate

44.1 kHz native, resampled to 8 kHz for telephony

Latency

~200 ms to first byte (p50, warm region)

Output formats

ulaw, alaw, PCM 16-bit, WAV, MP3

Streaming transports

WebSocket, HTTP chunked transfer

Speed range

0.5× – 2.0×, set per request

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News delivery is judged on clarity before character. These six come from voices carrying both the narration and educational tags, the combination that suits reading information aloud rather than performing it.

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Podcast production is segment based already, which suits generation in short calls. Intros and ad reads generate once and cache, and pairing contrasting voices produces a two-hander without booking two people.

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Course audio has to sound the same in module ten as in module one. These voices hold consistent across sessions, handle technical vocabulary through pronunciation dictionaries, and cover nine Indic languages alongside English.

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Eighty six voices are tagged mature, and twenty also carry the narrative tag. That overlap is the usual starting point for audiobooks and documentary work, where consistency shows over hours rather than seconds.

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Worth saying plainly: nothing in the catalog records pitch, so these six were chosen by ear rather than filtered. There is no pitch control either, so depth is a casting decision.

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Warm Voices

There is no tone field in the catalog, so these were chosen by listening rather than filtered. Warmth is a property of the voice, not a setting, which makes casting the decision that counts.

FAQs

163 of 244 voices carry the narration tag, the largest group in the catalog. They span every accent group and all 31 supported languages, so casting is usually decided by register rather than by what happens to be available.