Fish Audio: S2 Pro

Fish Audio: S2 Pro is a text-to-speech model from Fish Audio. It sits in the speech category, and its provider release date is 29 July 2026. If you are building anything that needs to turn written text into spoken audio, this is the kind of model you would look at first, and Fish Audio is the company behind it.

The model takes text as its only input and produces speech as its only output. It is multilingual, so the same model can handle more than one language rather than forcing you to pick a single-language voice. Fish Audio describes it as suited to expressive narration and multi-speaker dialogue, and it offers natural-language controls for speaking style and emotion. That means you can steer how something is read by describing the delivery you want, rather than only selecting from fixed presets. No context length is listed for this model, so treat the text you send as the practical constraint and check the live model page for current details.

Those capabilities point to a fairly clear set of uses. Expressive narration fits audiobooks, narrated articles, explainer videos and any product where a flat robotic read would sound wrong. Multi-speaker dialogue makes it a candidate for podcast-style content, audio dramas, training scenarios and conversational demos where two or more voices need to trade lines. The natural-language style and emotion controls are useful when you want the same script delivered in different tones, for example a calm version and an excited version, without rebuilding your pipeline. Because the model is multilingual, teams shipping content in several languages can keep one integration instead of stitching together separate per-language systems.

You call it through api14's OpenAI-compatible API using the model id "fish-audio/s2-pro". Because the interface follows the OpenAI format, you can point an existing OpenAI client at api14 and send a request with that model id, with your text as the input, and receive speech back. The model lists no supported parameters, so do not assume you can pass temperature, top_p, max tokens or similar sampling controls; send the model id and your input, and rely on the natural-language style and emotion instructions in the text itself to shape the output. The recorded maximum output value is 0, which gives you no usable output ceiling to plan against, so verify actual behaviour against the live endpoint before you commit to it.

Given how little is specified here, this model is a reasonable fit when your job is text in, speech out, especially for expressive or multi-speaker work. If you need a model that also accepts audio or images as input, returns text, or exposes tunable sampling parameters, you will want to look elsewhere on api14.