Fish Audio Open-Sources S2: Fine-Grained Control Meets Production Streaming

Fish Audio has unveiled S2, an innovative text-to-speech model that allows for detailed prosody and emotion control using natural-language tags, offering flexibility in expression at the word level. Trained on over 10 million hours of audio across 50 languages, S2 employs a unique dual-autoregressive architecture and reinforcement learning alignment, achieving impressive results on benchmarks such as the Audio Turing Test and EmergentTTS-Eval. The model's design integrates the same systems for data curation and reinforcement learning rewards, addressing distribution mismatches that affect other TTS systems. S2's architecture is structurally similar to standard autoregressive large language models, enabling it to utilize existing LLM serving optimizations efficiently. The release includes model weights, fine-tuning resources, and a streaming inference engine, available on platforms like GitHub and HuggingFace, marking a significant advancement in open-source text-to-speech technology.

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