AI voice catalog

Find your voice, then match it to the model and language your application needs.

Definition

The voice catalog is the library of voices available to your synthesis requests. It contains 900+ voices, with model and language compatibility recorded per voice. You select one by its voice ID in each request. For anything the catalog does not cover, a cloned voice slots in by ID the same way.

Breadth

A large catalog means you match voice to use case instead of forcing one voice everywhere. A warm narrator for an audiobook, a crisp voice for IVR, a distinct set for a game cast, selected from the same library. On Simba 3.5 every voice in the catalog speaks all 48 languages under one model id, and a voice keeps the accent of the recording it was made from. The older Simba 3.2 supports English and Simba 3.0 supports 6 languages across 7 locales.

Consistency

Reuse a voice ID to keep the selected speaker identity across chapters, episodes, and course modules. Keep your model and delivery settings consistent, and evaluate the output: synthesis is not a promise of byte-identical audio or zero variation.

Beyond the catalog

When you need a voice that is not in the library, clone one and reference its ID exactly like a catalog voice. See the voice cloning page and the model and language guide.

FAQ

Frequently asked questions

How many voices are in the catalog?
The catalog contains 900+ voices. Each voice has its own model and language compatibility; the total does not mean every voice works with every model. Select a compatible voice by its ID in the synthesis request.
How do I choose a voice?
Browse by language and style, check the voice's supported models, listen to a sample, and pass its voice ID to the API. For a custom or branded voice beyond the catalog, clone one and use its ID the same way.
Are voices consistent across requests?
Reusing a voice ID keeps the selected speaker identity consistent. Delivery can vary with the input, model, and SSML settings; it is not a guarantee of identical audio bytes. Test representative text before producing a long-form project.

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Find your voice, then match it to the model and language your application needs.

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