Fish Speech
**English** | [简体中文](docs/README.zh.md) | [Portuguese](docs/README.pt-BR.md) | [日本語](docs/README.ja.md) | [한국어](docs/README.ko.md) | [العربية](docs/README.ar.md)
### Fine-Grained Inline Control via Natural Language
S2 Pro brings unprecedented "soul" to speech. Using simple `[tag]` syntax, you can precisely embed emotional instructions at any position in the text.
- **15,000+ Unique Tags Supported**: Not limited to fixed presets; S2 supports **free-form text descriptions**. Try `[whisper in small voice]`, `[professional broadcast tone]`, or `[pitch up]`.
- **Rich Emotion Library**:
`[pause]` `[emphasis]` `[laughing]` `[inhale]` `[chuckle]` `[tsk]` `[singing]` `[excited]` `[laughing tone]` `[interrupting]` `[chuckling]` `[excited tone]` `[volume up]` `[echo]` `[angry]` `[low volume]` `[sigh]` `[low voice]` `[whisper]` `[screaming]` `[shouting]` `[loud]` `[surprised]` `[short pause]` `[exhale]` `[delight]` `[panting]` `[audience laughter]` `[with strong accent]` `[volume down]` `[clearing throat]` `[sad]` `[moaning]` `[shocked]`
### Innovative Dual-Autoregressive (Dual-AR) Architecture
S2 Pro adopts a master-slave Dual-AR architecture consisting of a decoder-only transformer and an RVQ audio codec (10 codebooks, ~21 Hz):
- **Slow AR (4B parameters)**: Operates along the time axis, predicting the primary semantic codebook.
- **Fast AR (400M parameters)**: Generates the remaining 9 residual codebooks at each time step, reconstructing exquisite acoustic details.
This asymmetric design achieves peak audio fidelity while significantly boosting inference speed.
### Reinforcement Learning (RL) Alignment
S2 Pro utilizes **Group Relative Policy Optimization (GRPO)** for post-training alignment. We use the same model suite for data cleaning and annotation directly as Reward Models, perfectly resolving the distribution mismatch between pre-training data and post-training objectives.
- **Multi-Dimensional Reward Signals**: Comprehensively evaluates semantic accuracy, instruction adherence, acoustic preference scoring, and timbre similarity to ensure every second of generated speech feels intuitive to humans.
### Extreme Streaming Performance (Powered by SGLang)
As the Dual-AR architecture is structurally isomorphic to standard LLMs, S2 Pro natively supports all SGLang inference acceleration features, including Continuous Batching, Paged KV Cache, CUDA Graph, and RadixAttention-based Prefix Caching.
**Performance on a single NVIDIA H200 GPU:**
- **Real-Time Factor (RTF)**: 0.195
- **Time-to-First-Audio (TTFA)**: ~100 ms
- **Extreme Throughput**: 3,000+ acoustic tokens/s while maintaining RTF < 0.5
### Robust Multilingual Support
S2 Pro supports over 80 languages without requiring phonemes or language-specific preprocessing:
- **Tier 1**: Japanese (ja), English (en), Chinese (zh)
- **Tier 2**: Korean (ko), Spanish (es), Portuguese (pt), Arabic (ar), Russian (ru), French (fr), German (de)
- **Global Coverage**: sv, it, tr, no, nl, cy, eu, ca, da, gl, ta, hu, fi, pl, et, hi, la, ur, th, vi, jw, bn, yo, xsl, cs, sw, nn, he, ms, uk, id, kk, bg, lv, my, tl, sk, ne, fa, af, el, bo, hr, ro, sn, mi, yi, am, be, km, is, az, sd, br, sq, ps, mn, ht, ml, sr, sa, te, ka, bs, pa, lt, kn, si, hy, mr, as, gu, fo, etc.
### Native Multi-Speaker Generation
Fish Audio S2 allows users to upload reference audio containing multiple speakers, and the model processes each speaker's features via the `<|speaker:i|>` token. You can then control the model's performance via speaker ID tokens, enabling a single generation to include multiple speakers. There is no longer a need to upload separate reference audio for each individual speaker.
### Multi-Turn Generation
Thanks to the expansion of the model context, our model can now leverage previous information to improve the expressiveness of subsequent generated content, thereby increasing the naturalness of the dialogue.
### Rapid Voice Cloning
Fish Audio S2 supports accurate voice cloning using short reference samples (typically 10-30 seconds). The model captures timbre, speaking style, and emotional tendencies, producing realistic and consistent cloned voices without additional fine-tuning.
For SGLang Server usage, please refer to the [SGLang-Omni README](https://github.com/sgl-project/sglang-omni/blob/main/sglang_omni/models/fishaudio_s2_pro/README.md).
---
## Credits
- [VITS2 (daniilrobnikov)](https://github.com/daniilrobnikov/vits2)
- [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2)
- [GPT VITS](https://github.com/innnky/gpt-vits)
- [MQTTS](https://github.com/b04901014/MQTTS)
- [GPT Fast](https://github.com/pytorch-labs/gpt-fast)
- [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS)
- [Qwen3](https://github.com/QwenLM/Qwen3)
## Tech Report
```bibtex
@misc{fish-speech-v1.4,
title={Fish-Speech: Leveraging Large Language Models for Advanced Multilingual Text-to-Speech Synthesis},
author={Shijia Liao and Yuxuan Wang and Tianyu Li and Yifan Cheng and Ruoyi Zhang and Rongzhi Zhou and Yijin Xing},
year={2024},
eprint={2411.01156},
archivePrefix={arXiv},
primaryClass={cs.SD},
url={https://arxiv.org/abs/2411.01156},
}
@misc{liao2026fishaudios2technical,
title={Fish Audio S2 Technical Report},
author={Shijia Liao and Yuxuan Wang and Songting Liu and Yifan Cheng and Ruoyi Zhang and Tianyu Li and Shidong Li and Yisheng Zheng and Xingwei Liu and Qingzheng Wang and Zhizhuo Zhou and Jiahua Liu and Xin Chen and Dawei Han},
year={2026},
eprint={2603.08823},
archivePrefix={arXiv},
primaryClass={cs.SD},
url={https://arxiv.org/abs/2603.08823},
}
```