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.

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.

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.

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.

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.

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.

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.

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.

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.

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.