All 48 Dependencies
Every package fish-speech depends on, ranked by repo health score.
Fish Speech (Fish Audio S2) is an open-source, state-of-the-art multilingual text-to-speech system built around a Dual-Autoregressive (Dual-AR) transformer architecture: a large "slow" AR model predicts the primary semantic codebook while a much smaller "fast" AR head reconstructs the residual RVQ audio codec tokens at each step, trading a bit of architectural complexity for major gains in inference speed and audio fidelity. The model was trained on a large multilingual audio corpus and post-trained with Group Relative Policy Optimization (GRPO) reinforcement learning, using the model suite itself as a reward model to align generations with human preference across semantic accuracy, instruction adherence, and timbre similarity.
Beyond raw synthesis quality, Fish Speech supports fine-grained inline control of prosody and emotion through free-form natural-language tags (not a fixed preset list), native multi-speaker generation via per-speaker conditioning tokens, multi-turn dialogue-aware generation, and rapid voice cloning from short reference clips. Because the Dual-AR architecture is structurally similar to a standard decoder-only LLM, the project can be served through SGLang with continuous batching, paged KV cache, and RadixAttention prefix caching for low-latency, high-throughput deployment.
The repository ships a command-line inference tool, a Gradio WebUI, and a production-style API server (Kui/uvicorn, msgpack request format, bearer-token auth) with Docker Compose files for both CUDA and ROCm targets, plus training and LoRA fine-tuning scripts built on PyTorch Lightning and Hydra. The codebase itself is released under the custom Fish Audio Research License, which permits self-hosted use for research and non-commercial purposes but requires a separate paid license for any commercial deployment.
