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.
Free to start!
German AI Voices
Female Voices
Warm Voices
AI Voices for Podcast Generation
British English AI Voices
AI Voices for Documentary Voice Over
Calm Voices
Australian English AI Voices
AI Voices for Gaming & Character Voices
Professional Voices
AI Narrator Voices
Cheerful Voices
AI Voices for Customer Support Automation
Authoritative Voices
AI Voices for Meditation & Wellness
Gravelly AI Voices
AI Voices for YouTube Voiceover
Indian English AI Voices
AI Voices for Movie Trailers
Friendly 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.
1500+ voices to explore
Browse our full voice library and find the right tone, accent, and personality for your agent.
Model Specifications
Streaming transports
Output formats
Speed range
Sample rate
Latency
One hundred and one voices
Female voices make up 44 percent of the catalog, spanning Indian, American, British, Australian, Canadian and Latin American accents and all twelve recommended languages. At that scale accent and language are usually the real constraint on casting rather than gender.
What can be adjusted
Speed, from 0.5 to 2.0, is the only delivery control. There is no pitch, tone or emotion parameter, so a voice cannot be shifted after generation. Casting is therefore the decision that matters, and it is worth auditioning several rather than choosing from a description.
Setting it in the API
Gender is not a parameter. You select a voice by ID and its gender comes with it, so changing an assistant to a female voice means changing voice_id to one of these. If you are using a third party product rather than the API, the setting lives in that product.
Language coverage
Female voices appear in both language families. Those in the Indic group cover eleven Indic languages plus English; those in the European group cover ten European languages. No voice spans both, so a multilingual product may need one voice per family rather than one overall.
Similar to
Female Voices
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.
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.
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.
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.
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.
