Voice Library
Every voice in the catalog, playable right here without signing in. 244 of them across 22 languages and 27 accents. Filter by accent, gender or use case, hear the one you want, then take its ID straight to the API.
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Voices for different usecases

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.

AI News Anchor Voices
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.

AI Voices for Documentary Voice Over
Documentary narration works by staying out of the way. The voice carries information the picture cannot, without competing with it. These six are drawn from the 163 narration-tagged voices, chosen for an even, unhurried delivery.

AI Voices for E-Learning
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.

For Explainer & Product Demo Videos
Explainer scripts get rewritten after the first cut. Generating line by line means a late change costs one line rather than a re-record, and audio arrives in about a second at 44.1 kHz.

AI Voices for IVR & Telephony
IVR voices need to survive a compressed phone line, not just sound good in a browser. These output ulaw and alaw directly, generate in roughly 200 milliseconds, and handle Hindi and English in the same prompt.

AI Voices for Voice Agents
Voice agents live or die on the pause before a reply. Synthesis starts in about 200 milliseconds and runs at 3.3 times real time, which leaves the budget where it usually belongs: your model.

AI Voices for Customer Support Automation
Support calls reach people who are already inconvenienced, often on a poor line. These voices favour clarity over character, hold steady across renders, and move between Hindi and English the way callers actually do.

AI Voices for Voice Chatbots
Web chat is unusual because the visitor reads and listens at once. These voices suit an unhurried delivery, begin playing in about 200 milliseconds over WebSocket, and cover twelve languages from one integration.

AI Voices for Gaming & Character Voices
Pre-rendering every branch means shipping every branch. WebSocket streaming lets dialogue generate during play instead, so a conversation tree costs what players actually hear rather than what they might.

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.

AI Voices for Announcements & Public Transport
Announcements are heard in reverberant halls and repeated thousands of times. Pronunciation dictionaries fix station and place names permanently, ulaw and alaw feed public address hardware directly, and one voice carries a full multilingual chain.

AI Voices for Podcast Generation
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.

AI Voices for Accessibility & Screen Readers
Experienced screen reader users often run well above normal speed. Speed adjusts from 0.5 to 2.0, and nine Indic languages are covered, where assistive audio is thin across the whole industry.

AI Voices for Meditation & Wellness
Guided audio lives on pacing rather than voice character. Speed goes down to 0.5, pauses come from how you write the script, and nine Indic languages are available for regional wellness content.

AI Voices for YouTube Voiceover
Short form rewards pace, and speed adjusts up to 2.0. Scripts change after the edit, so generating line by line means retiming a section costs one line rather than the whole track.

AI Voices for Ads & Commercials
Commercial reads are short, fast, and worth testing in variants. A thirty second spot is one or two requests, which makes producing four versions cheaper than booking one session.

AI Voices for Live Narration & Streaming
Generation runs at 3.3 times real time, so audio renders faster than it plays and the buffer stays ahead. First audio arrives in about 200 milliseconds over SSE or WebSocket.

AI Voices for Batch Voiceover Pipelines
At volume the cost driver is duplication, not generation. Hashing text and skipping what already exists removes most of it, since catalog and template work repeats the same phrases constantly.

AI Voices for Anime and Animation
Twenty three voices carry the character and animation tag, the group with the widest delivery in the catalog. These six are drawn from it, mixing American, Indian and Japanese accents for dubbing and original work alike.

AI Voices for Movie Trailers
Trailer narration is the most compressed writing in film advertising: eight or nine lines carrying an entire premise. These six are drawn from the narration pool, chosen for weight and an unhurried delivery.

Synthetic Voices
A synthetic voice is speech produced by a model rather than recorded by a person. These six are a cross-section of the catalog: different accents, different languages, all generated the moment you press play.

AI Character Voice Generator
Twenty three voices carry the character and animation tag, the group with the widest delivery in the catalog. This is the parent page for that work; the gaming and anime pages sit underneath it.

AI Voices for TikTok and Short-Form Video
Short-form rewards pace and rewrites. These six suit a fast read, and because a line regenerates in about a second you can change the script after the edit without rebooking anything.

AI Voices for Horror and Halloween
Horror narration works through restraint rather than effects. These six are drawn from the narration pool for weight and an unhurried delivery, which is what carries a story rather than a jump scare.

AI Sports Announcer Voices
Commentary is the one narration job that happens while the thing it describes is still happening. These six carry pace and projection, and generation runs fast enough to keep up with live play.

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.

AI News Anchor Voices
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.

AI Voices for Documentary Voice Over
Documentary narration works by staying out of the way. The voice carries information the picture cannot, without competing with it. These six are drawn from the 163 narration-tagged voices, chosen for an even, unhurried delivery.

AI Voices for E-Learning
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.

For Explainer & Product Demo Videos
Explainer scripts get rewritten after the first cut. Generating line by line means a late change costs one line rather than a re-record, and audio arrives in about a second at 44.1 kHz.

AI Voices for IVR & Telephony
IVR voices need to survive a compressed phone line, not just sound good in a browser. These output ulaw and alaw directly, generate in roughly 200 milliseconds, and handle Hindi and English in the same prompt.

AI Voices for Voice Agents
Voice agents live or die on the pause before a reply. Synthesis starts in about 200 milliseconds and runs at 3.3 times real time, which leaves the budget where it usually belongs: your model.

AI Voices for Customer Support Automation
Support calls reach people who are already inconvenienced, often on a poor line. These voices favour clarity over character, hold steady across renders, and move between Hindi and English the way callers actually do.

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.

AI News Anchor Voices
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.

AI Voices for Documentary Voice Over
Documentary narration works by staying out of the way. The voice carries information the picture cannot, without competing with it. These six are drawn from the 163 narration-tagged voices, chosen for an even, unhurried delivery.

AI Voices for E-Learning
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.

For Explainer & Product Demo Videos
Explainer scripts get rewritten after the first cut. Generating line by line means a late change costs one line rather than a re-record, and audio arrives in about a second at 44.1 kHz.

AI Voices for IVR & Telephony
IVR voices need to survive a compressed phone line, not just sound good in a browser. These output ulaw and alaw directly, generate in roughly 200 milliseconds, and handle Hindi and English in the same prompt.
Voices with different accents

German AI Voices
Seven voices carry a German accent, and six of them are here. Markus, Max and Ben are male, Petra, Hanna and Lea female. All sit in the European language family, which covers thirteen languages in total.

Russian AI Voices
Seven voices carry a Russian accent, and six are here. Anastasia, Olga and Ekaterina are female, Maksim, Andrei and Nikolai male. They sit in the European language family alongside twelve other languages.

Indian English AI Voices
One hundred and four voices carry an Indian accent, more than twice any other group. They handle English words inside Devanagari sentences without changing register, which is how Indian English is actually spoken.

Multilingual & Code-Switching AI Voices
Seventy two voices cover eleven Indic languages plus English, so one voice serves a Hindi caller and a Tamil caller without re-casting. Hindi and English can alternate inside a single sentence.

British English AI Voices
Seven British voices, listed in full rather than selected. Isla, Julia and Poppy are female, Alistair, Edward and Noah male. Enough for most projects, small enough to audition in five minutes.

American English AI Voices
Forty three American voices, the deepest English set in the catalog. Enough range to cast for tone across a campaign rather than reusing the same voice because nothing else fits.

Australian English AI Voices
Five Australian voices, and all five are here. Chloe, Nyah and Sienna are female, Cooper and Flynn male. A small set, but enough to cast a project without repeating a voice.

Canadian English AI Voices
Three Canadian voices: Erica, Alec and William. That is the whole group. If you need more range in a North American register, the forty three American voices sound close.

AI Accent Generator
Twenty six accents run through the catalog, from Indian and American at the deep end to Korean and Vietnamese with a single voice each. This page is the entry point; each accent has its own page underneath.

German AI Voices
Seven voices carry a German accent, and six of them are here. Markus, Max and Ben are male, Petra, Hanna and Lea female. All sit in the European language family, which covers thirteen languages in total.

Russian AI Voices
Seven voices carry a Russian accent, and six are here. Anastasia, Olga and Ekaterina are female, Maksim, Andrei and Nikolai male. They sit in the European language family alongside twelve other languages.

Indian English AI Voices
One hundred and four voices carry an Indian accent, more than twice any other group. They handle English words inside Devanagari sentences without changing register, which is how Indian English is actually spoken.

Multilingual & Code-Switching AI Voices
Seventy two voices cover eleven Indic languages plus English, so one voice serves a Hindi caller and a Tamil caller without re-casting. Hindi and English can alternate inside a single sentence.

British English AI Voices
Seven British voices, listed in full rather than selected. Isla, Julia and Poppy are female, Alistair, Edward and Noah male. Enough for most projects, small enough to audition in five minutes.

American English AI Voices
Forty three American voices, the deepest English set in the catalog. Enough range to cast for tone across a campaign rather than reusing the same voice because nothing else fits.

Australian English AI Voices
Five Australian voices, and all five are here. Chloe, Nyah and Sienna are female, Cooper and Flynn male. A small set, but enough to cast a project without repeating a voice.

Canadian English AI Voices
Three Canadian voices: Erica, Alec and William. That is the whole group. If you need more range in a North American register, the forty three American voices sound close.

German AI Voices
Seven voices carry a German accent, and six of them are here. Markus, Max and Ben are male, Petra, Hanna and Lea female. All sit in the European language family, which covers thirteen languages in total.

Russian AI Voices
Seven voices carry a Russian accent, and six are here. Anastasia, Olga and Ekaterina are female, Maksim, Andrei and Nikolai male. They sit in the European language family alongside twelve other languages.

Indian English AI Voices
One hundred and four voices carry an Indian accent, more than twice any other group. They handle English words inside Devanagari sentences without changing register, which is how Indian English is actually spoken.

Multilingual & Code-Switching AI Voices
Seventy two voices cover eleven Indic languages plus English, so one voice serves a Hindi caller and a Tamil caller without re-casting. Hindi and English can alternate inside a single sentence.

British English AI Voices
Seven British voices, listed in full rather than selected. Isla, Julia and Poppy are female, Alistair, Edward and Noah male. Enough for most projects, small enough to audition in five minutes.

American English AI Voices
Forty three American voices, the deepest English set in the catalog. Enough range to cast for tone across a campaign rather than reusing the same voice because nothing else fits.
Voices with different styles

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.

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.

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.

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.

Conversational Voices
One hundred and ninety five voices carry the conversational tag, 84 percent of the catalog. Streaming is what makes them feel live: audio begins playing before the sentence has finished rendering.

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.

Energetic Voices
Speed is the reliable lever here, not casting. Settings between 1.2 and 1.4 lift almost any voice in the catalog, though longer sentences start to blur past roughly 1.5.

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.

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.

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.

Expressive Voices
There is no emotion parameter, so range comes from the voice and from how the line is written. Fourteen voices carry the character tag, and those have the widest delivery available.

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.

Friendly Voices
For Indian customer facing work the deciding factor is not tone but language. These voices switch between Hindi and English mid sentence, which reads as considerably more natural than either alone.

Serious Voices
For compliance and legal reads the goal is that exact wording lands, not that the voice sounds grave. A speed near 0.9 improves intelligibility more than any casting choice.

Cheerful Voices
Only six voices in the catalog show cheerful or positive markers, the thinnest tone signal available. Speed slightly above 1.0 lifts almost any voice, which is the more dependable route.

Sultry AI Voices
There is no tone field in the catalog, so these six were chosen by listening rather than returned by a filter. What they share is an unhurried delivery and a lower placement, which is most of what the word describes.

Gravelly AI Voices
A gravelly voice is one with audible texture, a roughness in the lower register. Nothing in the catalog records that, so these six were chosen by listening. What they share is weight and an unhurried delivery.

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.

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.

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.

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.

Conversational Voices
One hundred and ninety five voices carry the conversational tag, 84 percent of the catalog. Streaming is what makes them feel live: audio begins playing before the sentence has finished rendering.

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.

Energetic Voices
Speed is the reliable lever here, not casting. Settings between 1.2 and 1.4 lift almost any voice in the catalog, though longer sentences start to blur past roughly 1.5.

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.

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.

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.

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.

Conversational Voices
One hundred and ninety five voices carry the conversational tag, 84 percent of the catalog. Streaming is what makes them feel live: audio begins playing before the sentence has finished rendering.

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.
Build the future of voice agent orchestration
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San Francisco, CA 94104
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Build the future of voice agent orchestration
311 California Street, Suite 320
San Francisco, CA 94104
Build the future of voice agent orchestration
311 California Street, Suite 320
San Francisco, CA 94104
Documentation
Resources
Initiatives