Fish Speech is a production-grade multilingual text-to-speech system with a dual-autoregressive transformer architecture (4B parameter slow AR for temporal semantics, 400M fast AR for residual codebooks). It supports real-time voice cloning from short reference audio (10-30s), multi-speaker multi-turn generation, and fine-grained prosody/emotion control via natural language tags. The system is deployed through multiple interfaces: a Gradio web UI, a React-based modern web UI, a production ASGI API server (Kui) with streaming support, and command-line tools for inference and batch processing. Its architecture is modular: a TTSInferenceEngine orchestrates reference loading, LLM inference, and VQ decoding; an RVQ audio codec (10 codebooks, ~21 Hz frame rate) compresses audio; and the dual-AR model is structurally isomorphic to standard LLMs, enabling reuse of inference optimizations like paged KV cache and RadixAttention. The repository also includes training infrastructure (Lightning + Hydra) with LoRA fine-tuning, protobuf-based dataset pipelines, and a comprehensive i18n framework. While primarily a deployable application, it retains strong research character with published papers, benchmark results, and custom model implementations.