# fish-speech **Repository Path**: weiwei20180921/fish-speech ## Basic Information - **Project Name**: fish-speech - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-12-12 - **Last Updated**: 2025-12-12 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

Fish Speech

> [!IMPORTANT] > **License Notice** > This codebase is released under **Apache License** and all model weights are released under **CC-BY-NC-SA-4.0 License**. Please refer to [LICENSE](LICENSE) for more details. > [!WARNING] > **Legal Disclaimer** > We do not hold any responsibility for any illegal usage of the codebase. Please refer to your local laws about DMCA and other related laws. ## Start Here Here are the official documents for Fish Speech, follow the instructions to get started easily. - [Installation](https://speech.fish.audio/install/) - [Finetune](https://speech.fish.audio/finetune/) - [Inference](https://speech.fish.audio/inference/) - [Samples](https://speech.fish.audio/examples) ## 🎉 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](https://fish.audio) (for **OpenAudio-S1**) and [Hugging Face](https://huggingface.co/fishaudio/openaudio-s1-mini) (for **OpenAudio-S1-mini**). Visit the [OpenAudio website](https://openaudio.com/blogs/s1) 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](https://arena.speechcolab.org/), 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](https://fish.audio/) | Full-featured flagship model | | **S1-mini** | 0.5B parameters | Avaliable on huggingface [hf space](https://huggingface.co/spaces/fishaudio/openaudio-s1-mini) | 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](https://docs.fish.audio/resources/best-practices/voice-cloning).** 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. **Deploy-Friendly:** Easily set up an inference server with native support for Linux and Windows (macOS support coming soon), minimizing performance loss. ## **Media & Demos**

### **Social Media**

### **Interactive Demos**

### **Video Showcases**

--- ## 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 (V1.4) ```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}, } ```