Launching Fish Audio S1: A Frontier Text-to-Speech Audio Foundation Model

Fish Audio S1 is a pioneering text-to-speech audio foundation model that supports open-domain emotion, tone, and special effect markers, developed using over 2 million hours of audio training with online reinforcement learning from human feedback (RLHF). Available in two variants, the full-featured S1 (4B) and the resource-efficient S1-mini (0.5B), both models boast impressive performance metrics, with S1 achieving a 0.8% word error rate (WER) and a 0.4% character error rate (CER) on the Seed TTS Eval. S1 ranks highest in naturalness, intelligibility, and similarity on HuggingFace TTS-Arena-V2, offering voice-actor-level control with emotion markers and global multilingual capabilities across several languages. The model's Qwen3 architecture and efficient real-time performance make it suitable for interactive applications, with affordable pricing that supports high-volume or budget-sensitive workloads. Additionally, it enables zero-shot and few-shot voice cloning without phoneme dependency, making it accessible for diverse text-to-speech needs.

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