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.
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
Caching is the main cost lever
In catalog and template work, repeated phrases are usually a large share of total volume. Hashing the text and skipping anything already generated removes that duplication entirely. This sits on your side, and in most bulk pipelines it saves more than any other single optimisation.
Keeping output consistent at volume
Across thousands of items, small variations become visible. Fixing the voice, speed and sample rate for an entire run is what keeps a product catalog sounding uniform. Pronunciation dictionaries handle recurring brand and product names so those do not need per item correction.
Sizing a batch job
Requests cap at 250 characters, so a job is a queue of small calls rather than a few large ones. Concurrency is set by your plan, so check that before sizing a run. Throughput is generally limited by concurrency rather than by generation speed, which runs at 3.3 times real time.
Formats and storage
Output is 44.1 kHz natively, available as PCM, MP3 or WAV. For large libraries MP3 keeps storage manageable, while WAV is worth retaining for anything that may be re-edited later. Generating both in one pass is cheaper than regenerating from scratch when a format need changes.
Similar to
AI Voices for Batch Voiceover Pipelines
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 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 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.
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.
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.
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.
