Fish Audio: S2.1 Pro

Fish Audio: S2.1 Pro is a text-to-speech model from Fish Audio, listed on api14 under the speech category. It is described by its provider as production-oriented, which places it in the part of the Fish Audio lineup aimed at deployed voice applications rather than experimentation. If you already route other models through api14, this one sits alongside them as a speech generation option from the same provider.

The model takes text as its only input and returns speech as its only output. It is built for multilingual voice applications, expressive narration, and dialogue synthesis, and it exposes open-ended natural-language controls for speaking style. That means you can describe the delivery you want in plain language rather than selecting from a fixed set of presets. No context window figure is published for this model, and the maximum output is listed as zero in the model metadata, so treat the practical length of any single generation as something to confirm against the live API rather than assume from the listing.

The use cases follow directly from those traits. Multilingual voice applications are the clearest fit, since the model is intended for more than one language. Expressive narration suits audiobooks, explainer videos, and any content where a flat read would sound wrong. Dialogue synthesis covers multi-speaker scripts, conversational agents, and interactive characters. The natural-language style controls are useful when you want to direct tone or delivery per request without maintaining a library of preset voices.

You call it through api14's OpenAI-compatible API using the model id "fish-audio/s2.1-pro". Because the interface follows the OpenAI format, existing clients and SDKs generally work with a change of base URL and model id. The model metadata lists no supported parameters, so do not assume that sampling controls such as temperature or top_p will be accepted; check the live endpoint before relying on them. The output is audio, so your client needs to handle a speech response rather than text.

There is no published context length, and the maximum output is recorded as zero, so very long scripts may need to be split into smaller requests. If your task is text generation rather than speech, a text model will fit better. If you need a speech model with documented parameter controls, compare against other speech models on api14 before committing.