Fish Audio S2 Pro Technical Report GitHub Playground Fish Audio S2 Pro is a leading text to speech (TTS) model with fine grained inline control of prosody and emotion. Trained on over 10M+ hours of audio data across 80+ languages, the system combines reinforcement learning alignment with a dual autoregressive architecture. The release includes model weights, fine tuning code, and an SGLang based streaming inference engine. Architecture S2 Pro builds on a decoder only transformer combined with an RVQ based audio codec (10 codebooks, ~21 Hz frame rate) using a Dual Autoregressive (Dual AR) architecture: Slow AR (4B parameters): Operates along the time axis and predicts the primary semantic codebook. Fast AR (400M parameters): Generates the remaining 9 residual codebooks at each time step, reconstructing fine grained acoustic detail. This asymmetric design keeps inference efficient while preserving audio fidelity. Because the Dual AR architecture is structurally isomorphic to standard autoregressive LLMs, it inherits all LLM native serving optimizations from SGLang — including continuous batching, paged KV cache, CUDA graph replay, and RadixAttention based prefix caching. Fine Grained…
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