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🎉 Announcement

We are excited to announce that we have rebranded to OpenAudio — introducing a revolutionary new series of advanced Text-to-Speech models that builds upon the foundation of Fish-Speech.

We are proud to release OpenAudio-S1 as the first model in this series, delivering significant improvements in quality, performance, and capabilities.

OpenAudio-S1 comes in two versions: OpenAudio-S1 and OpenAudio-S1-mini. Both models are now available on Fish Audio Playground (for OpenAudio-S1) and Hugging Face (for OpenAudio-S1-mini).

Visit the OpenAudio website for blog & tech report.

Highlights ✨

Excellent TTS quality

We use Seed TTS Eval Metrics to evaluate the model performance, and the results show that OpenAudio S1 achieves 0.008 WER and 0.004 CER on English text, which is significantly better than previous models. (English, auto eval, based on OpenAI gpt-4o-transcribe, speaker distance using Revai/pyannote-wespeaker-voxceleb-resnet34-LM)

Model	Word Error Rate (WER)	Character Error Rate (CER)	Speaker Distance
S1	0.008	0.004	0.332
S1-mini	0.011	0.005	0.380

Best Model in TTS-Arena2 🏆

OpenAudio S1 has achieved the #1 ranking on TTS-Arena2, the benchmark for text-to-speech evaluation:

Speech Control

OpenAudio S1 supports a variety of emotional, tone, and special markers to enhance speech synthesis:

• Basic emotions:

(angry) (sad) (excited) (surprised) (satisfied) (delighted) 
(scared) (worried) (upset) (nervous) (frustrated) (depressed)
(empathetic) (embarrassed) (disgusted) (moved) (proud) (relaxed)
(grateful) (confident) (interested) (curious) (confused) (joyful)

• Advanced emotions:

(disdainful) (unhappy) (anxious) (hysterical) (indifferent) 
(impatient) (guilty) (scornful) (panicked) (furious) (reluctant)
(keen) (disapproving) (negative) (denying) (astonished) (serious)
(sarcastic) (conciliative) (comforting) (sincere) (sneering)
(hesitating) (yielding) (painful) (awkward) (amused)

• Tone markers:

(in a hurry tone) (shouting) (screaming) (whispering) (soft tone)

• Special audio effects:

(laughing) (chuckling) (sobbing) (crying loudly) (sighing) (panting)
(groaning) (crowd laughing) (background laughter) (audience laughing)

You can also use Ha,ha,ha to control, there's many other cases waiting to be explored by yourself.

(Support for English, Chinese and Japanese now, and more languages is coming soon!)

Two Type of Models

Model	Size	Availability	Features
S1	4B parameters	Avaliable on fish.audio	Full-featured flagship model
S1-mini	0.5B parameters	Avaliable on huggingface hf space	Distilled version with core capabilities

Both S1 and S1-mini incorporate online Reinforcement Learning from Human Feedback (RLHF).

Features

1. Zero-shot & Few-shot TTS: Input a 10 to 30-second vocal sample to generate high-quality TTS output. For detailed guidelines, see Voice Cloning Best Practices.

2. Multilingual & Cross-lingual Support: Simply copy and paste multilingual text into the input box—no need to worry about the language. Currently supports English, Japanese, Korean, Chinese, French, German, Arabic, and Spanish.

3. No Phoneme Dependency: The model has strong generalization capabilities and does not rely on phonemes for TTS. It can handle text in any language script.

4. Highly Accurate: Achieves a low CER (Character Error Rate) of around 0.4% and WER (Word Error Rate) of around 0.8% for Seed-TTS Eval.

5. Fast: Accelerated by torch compile, the real-time factor is approximately 1:7 on an Nvidia RTX 4090 GPU.

6. WebUI Inference: Features an easy-to-use, Gradio-based web UI compatible with Chrome, Firefox, Edge, and other browsers.

7. GUI Inference: Offers a PyQt6 graphical interface that works seamlessly with the API server. Supports Linux, Windows, and macOS. See GUI.

8. Deploy-Friendly: Easily set up an inference server with native support for Linux, Windows (MacOS comming soon), minimizing speed loss.

Media & Demos

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Documents

• Build Envrionment
• Inference

Credits

• VITS2 (daniilrobnikov)
• Bert-VITS2
• GPT VITS
• MQTTS
• GPT Fast
• GPT-SoVITS
• Qwen3

Tech Report (V1.4)

@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},
}