## Overview
**FireRedTTS3** is a unified speech generation and editing system built on **semantically enriched continuous speech representations**. It comes in two variants:
- **FireRedTTS3-Base** — zero-shot voice cloning across **24 languages** and **21 Chinese dialects**
- **FireRedTTS3-Instruct** — natural-language **voice design** and **speech editing** (semantic + acoustic) in one unified model
## Highlights ✨
* 🌍 **Multilingual — 24 Languages** — Best average WER/CER (avg 3.754%) and best average speaker similarity on MiniMax-MLS-Test (avg 84.8%), plus best-in-class cloning WER/CER (avg 3.04%) and similarity on Seed-TTS-eval (avg 78.8%). Supported languages:
`Arabic` · `Cantonese` · `Chinese` · `Czech` · `Dutch` · `English` · `Finnish` · `French` · `German` · `Greek` · `Hindi` · `Indonesian` · `Italian` · `Japanese` · `Korean` · `Polish` · `Portuguese` · `Romanian` · `Russian` · `Spanish` · `Thai` · `Turkish` · `Ukrainian` · `Vietnamese`
* 🗣️ **Multi-Dialect — 21 Chinese Dialects** — Zero-shot voice cloning across major Chinese dialect groups. Supported dialects:
`Anhui` · `Fujian` · `Gansu` · `Guizhou` · `Hebei` · `Henan` · `Hubei` · `Hunan` · `Jiangxi` · `Liaoning` · `Minnan` · `Ningxia` · `Shaanxi` · `Shandong` · `Shanghai` · `Shanxi` · `Sichuan` · `Tianjin` · `Wenzhou` · `Wu` · `Yunnan`
* 🎨 **Instruction-Controlled Voice Design** — Generate a brand-new voice from a natural-language description (gender, age, timbre, emotion, pace, accent…) with no reference audio, guided by an explicit textual plainning step before synthesis.
* ✂️ **Free-Form Speech Editing** — Semantic editing (insertion / deletion / substitution) and acoustic editing (speed / pitch / volume) driven by free-form instructions.
## News
- [2026.08.05] We release **FireRedTTS3-Base**
- [2026.08.13] We release the **FireRedTTS3-Instruct** model & code
## Roadmap
- [x] Release the FireRedTTS3-Base model
- [x] Release the FireRedTTS3-Instruct model
- [ ] Release the technical report
## Contents
- [Quick Start](#-quick-start)
- [Model](#-model)
- [Performance](#-performance)
- [Usage Disclaimer](#-usage-disclaimer-)
- [Citation](#-citation)
- [Acknowledgements](#-acknowledgements)
- [License](#-license)
## Quick Start 🚀
### Clone the repo
```sh
git clone https://github.com/FireRedTeam/FireRedTTS3.git
cd FireRedTTS3
```
### Installation with pip
```sh
pip install -r requirements.txt
```
### Model Download
Download the pretrained model from Hugging Face with the `hf` CLI:
```sh
pip install "huggingface_hub[cli]"
hf download FireRedTeam/FireRedTTS3 --local-dir pretrained_models/
```
### Configure Text Frontend
#### Language Recognition (Optional)
FireRedTTS3-Base relies on explicit language tags for best performance. However, if you don't know the exact language of the text, you can download Meta's `FastText` language-id model and let it detect the language automatically.
```sh
# Download FastText language-id model (lid.176) with:
curl -L -o fireredtts3/utils/llm_tn/models/lid.176.ftz https://dl.fbaipublicfiles.com/fasttext/supervised-models/lid.176.ftz
```
#### Text Normalization (TN)
TN converts written numbers, dates, units, currencies, acronyms, etc. into their spoken form (e.g. 19:30 → nineteen thirty). By default, FireRedTTS3 uses the `wetext` TN tool, which supports Chinese and English, other languages (e.g. Japanese, Russian) undergo only basic cleaning. For full language TN support, enable the LLM-based TN by passing `use_llm_tn=True` when initializing FireRedTTS3. It reads its config from a .env file:
```sh
cp .env.example .env
# Then fill in your values
LLM_TN_API_URL=https://api.deepseek.com/chat/completions # any OpenAI-compatible endpoint
LLM_TN_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
LLM_TN_MODEL=deepseek-v4-flash # or any model >= 30B
```
### Python API
For the best voice cloning performance, use a prompt in the desired language or dialect, since the output inherits the speaking style of the reference. For example, provide a Japanese prompt when synthesizing Japanese and a Sichuanese prompt when synthesizing Sichuanese.
```python
import torch
import torchaudio
from fireredtts3.core import FireRedTTS3
# Init model: choose the text-normalization frontend here.
# use_wetext=True -> local weText TN (zh/en only)
# use_llm_tn=True -> LLM-based TN (all languages, needs .env / API creds)
# both False -> no TN frontend built
tts = FireRedTTS3(
"pretrained_models",
use_wetext=True,
use_llm_tn=False,
)
language = None # Automatic detection if pass None
prompt_text = "
