Professional Voices

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.

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professional ai voices
CatalinaVerified
Latin-American · Female · Young
United States flagSpanish
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ClaudiaVerified
Castilian-Standard · Female · Young
Spanish
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AndreiVerified
Russian · Male · Young
Russia flagRussian
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JakubVerified
Polish · Male · Young
Polish
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ArghyaVerified
Indian · Male · Young
India flagHindi
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MuruganVerified
Indian · Male · Young
India flagHindi
Use voice
CatalinaVerified
Latin-American · Female · Young
United States flagSpanish
Use voice
ClaudiaVerified
Castilian-Standard · Female · Young
Spanish
Use voice
AndreiVerified
Russian · Male · Young
Russia flagRussian
Use voice
JakubVerified
Polish · Male · Young
Polish
Use voice
ArghyaVerified
Indian · Male · Young
India flagHindi
Use voice
MuruganVerified
Indian · Male · Young
India flagHindi
Use voice

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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.

1500+ voices to explore

Browse our full voice library and find the right tone, accent, and personality for your agent.

Model Specifications

Streaming transports

WebSocket, HTTP chunked transfer

WebSocket, HTTP chunked transfer

Output formats

.ulaw, .alaw, PCM 16-bit, .wav, .mp3

.ulaw, .alaw, PCM 16-bit, .wav, .mp3

Speed range

0.5× – 2.0×, set per request

0.5× – 2.0×, set per request

Sample rate

44.1 kHz native, resampled to 8 kHz for telephony

44.1 kHz native, resampled to 8 kHz for telephony

Latency

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

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

Drawn from two working pools

There is no professional tag, so this set comes from the educational and Voice Agent groups, which between them cover the contexts where a composed register matters. Twenty two voices carry one of those tags. Selection within them rests on listening rather than on any documented attribute.

Pace over character

Speed, adjustable from 0.5 to 2.0, is the only delivery control. For corporate narration a setting slightly below 1.0 reads as more measured. There is no tone parameter, so a voice cannot be made to sound more formal after the fact.

Terminology consistency

Corporate material repeats brand names, product names and acronyms constantly, and inconsistency across a library is more noticeable than any individual mispronunciation. Pronunciation dictionaries fix each term once so every generation matches, which matters more for recurring material than for one off scripts.

Consistency across a long script

Requests cap at 250 characters, so a quarterly presentation or training script is assembled from many calls. Keeping voice, speed and sample rate fixed across the whole run removes most variation, but generating a full section and listening across the joins is still worth doing before committing.

Popular voices

Frequently asked questions

Drawn from the educational and voice agent pools, since there is no corporate tag. Alec and Erica read as composed in English, Aarushi and Aditi in Hindi. For recurring material, pronunciation dictionaries are the feature that matters most, since they fix brand and product names once rather than per script.