Announcing our Series A Funding

Announcing our Series A Funding

Mature Voices

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.

VOICES FOR THIS USE CASE

Dylandylan
MaleYoungChinese
Best for MandarinUse voice
Franciscofrancisco
MaleYoungEuropean
Best for EnglishUse voice
Atharvaatharva
MaleYoungIndian
Best for HindiUse voice
Nikolainikolai
MaleYoungRussian
Best for RussianUse voice
Jakubjakub
MaleYoungPolish
Best for PolishUse voice
Maksimmaksim
MaleYoungRussian
Best for RussianUse voice

Eighty six voices

Mature covers 37 percent of the catalog and, like young, is a genuinely tagged attribute rather than an inferred one. The underlying data used both middle aged and old, which have been combined here into a single value.

Long form is the natural fit

Twenty voices carry the narrative tag, and there is substantial overlap with this group. Audiobooks and documentary work conventionally use mature voices, and the practical test is an extended passage rather than a sentence, since long form is where consistency becomes audible.

Age cannot be set

There is no age parameter, so a voice cannot be aged up after generation. Casting is the only route. Because this group is smaller than the young group, narrowing by accent and language leaves a manageable set to audition in most cases.

Availability in Indic languages

A substantial share of mature voices carry an Indic recommendation, including Aadya in Odia and Abir. For documentary and long form work in regional Indian languages, the combination of a mature voice and an Indic recommendation is unusually well covered here.

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

Explore Voice Similar to

Mature Voices

Young Voices

One hundred and forty six voices are tagged young, 63 percent of the catalog, across every accent and all twelve recommended languages. Unlike tone, age is a real tagged field rather than an inference.

Deep Voices

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.

Authoritative Voices

Slowing down reads as more authoritative than speeding up. A setting near 0.9 does more than any voice choice, and short declarative sentences carry more weight than qualified ones.

Male Voices

One hundred and twenty male voices across every accent group and all twelve recommended languages. Most carry an Indic recommendation, which is unusual in catalogs that treat Indian languages as an afterthought.

AI Voices for Audiobook Narration

A novel is thousands of requests stitched together, and the joins are where narration falls apart. These voices hold consistent across a full book and cover nine Indic languages that most vendors do not.

Professional Voices

Corporate narration needs consistent terminology more than it needs a particular tone. Pronunciation dictionaries fix brand and product names once, so every module in a library matches the first one.

Soothing Voices

Only two voices carry the meditative tag, so this set is drawn wider and chosen by ear. Pace matters more than casting: around 0.7 suits most soothing material.

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.

Calm Voices

Calm comes from pace more than from voice. Speed runs down to 0.5, and pauses come from punctuation rather than a parameter, so the script does as much work as the casting.

Female Voices

One hundred and one female voices, spanning six accent groups and all twelve recommended languages. At that scale accent and language usually constrain casting more than gender does.

Young Voices

One hundred and forty six voices are tagged young, 63 percent of the catalog, across every accent and all twelve recommended languages. Unlike tone, age is a real tagged field rather than an inference.

Deep Voices

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.

Authoritative Voices

Slowing down reads as more authoritative than speeding up. A setting near 0.9 does more than any voice choice, and short declarative sentences carry more weight than qualified ones.

Male Voices

One hundred and twenty male voices across every accent group and all twelve recommended languages. Most carry an Indic recommendation, which is unusual in catalogs that treat Indian languages as an afterthought.

AI Voices for Audiobook Narration

A novel is thousands of requests stitched together, and the joins are where narration falls apart. These voices hold consistent across a full book and cover nine Indic languages that most vendors do not.

Professional Voices

Corporate narration needs consistent terminology more than it needs a particular tone. Pronunciation dictionaries fix brand and product names once, so every module in a library matches the first one.

Soothing Voices

Only two voices carry the meditative tag, so this set is drawn wider and chosen by ear. Pace matters more than casting: around 0.7 suits most soothing material.

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

Eighty six of two hundred and thirty two. Aadya and Amaira in Hindi and Odia, Albus, Andrea and Alec in English. Age cannot be adjusted through the API, so it is a casting decision rather than a parameter. Mature voices are the usual choice for long form narration and documentary work.