Canto/README.md

Qwen3-TTS WebUI

⚠️ Notice: This project is largely AI-generated and is currently in an unstable state. Stable releases will be published in the Releases section.

Unofficial text-to-speech web application based on Qwen3-TTS, supporting custom voice, voice design, and voice cloning with an intuitive interface.

This is an unofficial project. For the official Qwen3-TTS repository, please visit QwenLM/Qwen3-TTS.

中文文档

Features

• Custom Voice: Predefined speaker voices
• Voice Design: Create voices from natural language descriptions
• Voice Cloning: Clone voices from uploaded audio
• Audiobook Generation: Upload EPUB files and generate multi-character audiobooks with LLM-powered character extraction and voice assignment
• Dual Backend Support: Switch between local model and Aliyun TTS API
• Multi-language Support: English, 简体中文, 繁體中文, 日本語, 한국어
• JWT auth, async tasks, voice cache, dark mode

Interface Preview

Desktop - Light Mode

Desktop - Dark Mode

Mobile

Audiobook Generation

Tech Stack

Backend: FastAPI + SQLAlchemy + PyTorch + JWT

• Direct PyTorch inference with Qwen3-TTS models
• Async task processing with batch optimization
• Local model support + Aliyun API integration

Frontend: React 19 + TypeScript + Vite + Tailwind + Shadcn/ui

Docker Deployment

Pre-built images are available on Docker Hub: bdim404/qwen3-tts-backend, bdim404/qwen3-tts-frontend

Prerequisites: Docker, Docker Compose, NVIDIA GPU + NVIDIA Container Toolkit

git clone https://github.com/bdim404/Qwen3-TTS-WebUI.git
cd Qwen3-TTS-webUI

# Download models to docker/models/ (see Installation > Download Models below)
mkdir -p docker/models docker/data

# Configure
cp docker/.env.example docker/.env
# Edit docker/.env and set SECRET_KEY

cd docker

# Pull pre-built images
docker compose pull

# Start (CPU only)
docker compose up -d

# Start (with GPU)
docker compose -f docker-compose.yml -f docker-compose.gpu.yml up -d

Access the application at http://localhost. Default credentials: admin / admin123456

Installation

Prerequisites

• Python 3.9+ with CUDA support (for local model inference)
• Node.js 18+ (for frontend)
• Git

1. Clone Repository

git clone https://github.com/bdim404/Qwen3-TTS-WebUI.git
cd Qwen3-TTS-webUI

2. Download Models

Important: Models are NOT automatically downloaded. You need to manually download them first.

For more details, visit the official repository: Qwen3-TTS Models

Navigate to the models directory:

# Docker deployment
mkdir -p docker/models && cd docker/models

# Local deployment
cd qwen3-tts-backend && mkdir -p Qwen && cd Qwen

Option 1: Download through ModelScope (Recommended for users in Mainland China)

pip install -U modelscope

modelscope download --model Qwen/Qwen3-TTS-Tokenizer-12Hz --local_dir ./Qwen3-TTS-Tokenizer-12Hz
modelscope download --model Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --local_dir ./Qwen3-TTS-12Hz-1.7B-CustomVoice
modelscope download --model Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign --local_dir ./Qwen3-TTS-12Hz-1.7B-VoiceDesign
modelscope download --model Qwen/Qwen3-TTS-12Hz-1.7B-Base --local_dir ./Qwen3-TTS-12Hz-1.7B-Base

Optional 0.6B models (smaller, faster):

modelscope download --model Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice --local_dir ./Qwen3-TTS-12Hz-0.6B-CustomVoice
modelscope download --model Qwen/Qwen3-TTS-12Hz-0.6B-Base --local_dir ./Qwen3-TTS-12Hz-0.6B-Base

Option 2: Download through Hugging Face

pip install -U "huggingface_hub[cli]"

hf download Qwen/Qwen3-TTS-Tokenizer-12Hz --local-dir ./Qwen3-TTS-Tokenizer-12Hz
hf download Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice --local-dir ./Qwen3-TTS-12Hz-1.7B-CustomVoice
hf download Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign --local-dir ./Qwen3-TTS-12Hz-1.7B-VoiceDesign
hf download Qwen/Qwen3-TTS-12Hz-1.7B-Base --local-dir ./Qwen3-TTS-12Hz-1.7B-Base

Optional 0.6B models (smaller, faster):

hf download Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice --local-dir ./Qwen3-TTS-12Hz-0.6B-CustomVoice
hf download Qwen/Qwen3-TTS-12Hz-0.6B-Base --local-dir ./Qwen3-TTS-12Hz-0.6B-Base

Final directory structure:

Docker deployment (docker/models/):

Qwen3-TTS-webUI/
└── docker/
    └── models/
        ├── Qwen3-TTS-Tokenizer-12Hz/
        ├── Qwen3-TTS-12Hz-1.7B-CustomVoice/
        ├── Qwen3-TTS-12Hz-1.7B-VoiceDesign/
        └── Qwen3-TTS-12Hz-1.7B-Base/

Local deployment (qwen3-tts-backend/Qwen/):

Qwen3-TTS-webUI/
└── qwen3-tts-backend/
    └── Qwen/
        ├── Qwen3-TTS-Tokenizer-12Hz/
        ├── Qwen3-TTS-12Hz-1.7B-CustomVoice/
        ├── Qwen3-TTS-12Hz-1.7B-VoiceDesign/
        └── Qwen3-TTS-12Hz-1.7B-Base/

3. Backend Setup

cd qwen3-tts-backend

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Install Qwen3-TTS
pip install qwen-tts

# Create configuration file
cp .env.example .env

# Edit .env file
# For local model: Set MODEL_BASE_PATH=./Qwen
# For Aliyun API only: Set DEFAULT_BACKEND=aliyun
nano .env  # or use your preferred editor

Important Backend Configuration (.env):

MODEL_DEVICE=cuda:0              # Use GPU (or cpu for CPU-only)
MODEL_BASE_PATH=./Qwen           # Path to your downloaded models
DEFAULT_BACKEND=local            # Use 'local' for local models, 'aliyun' for API
DATABASE_URL=sqlite:///./qwen_tts.db
SECRET_KEY=your-secret-key-here  # Change this!

Start the backend server:

# Using uvicorn directly
uvicorn main:app --host 0.0.0.0 --port 8000 --reload

# Or using conda (if you prefer)
conda run -n qwen3-tts uvicorn main:app --host 0.0.0.0 --port 8000 --reload

Verify backend is running:

curl http://127.0.0.1:8000/health

4. Frontend Setup

cd qwen3-tts-frontend

# Install dependencies
npm install

# Create configuration file
cp .env.example .env

# Start development server
npm run dev

5. Access the Application

Open your browser and visit: http://localhost:5173

Default Credentials:

• Username: admin
• Password: admin123456
• IMPORTANT: Change the password immediately after first login!

Production Build

For production deployment:

# Backend: Use gunicorn or similar WSGI server
cd qwen3-tts-backend
gunicorn main:app -w 4 -k uvicorn.workers.UvicornWorker -b 0.0.0.0:8000

# Frontend: Build static files
cd qwen3-tts-frontend
npm run build
# Serve the 'dist' folder with nginx or another web server

Configuration

Backend Configuration

Backend .env key settings:

SECRET_KEY=your-secret-key
MODEL_DEVICE=cuda:0
MODEL_BASE_PATH=../Qwen
DATABASE_URL=sqlite:///./qwen_tts.db

DEFAULT_BACKEND=local

ALIYUN_REGION=beijing
ALIYUN_MODEL_FLASH=qwen3-tts-flash-realtime
ALIYUN_MODEL_VC=qwen3-tts-vc-realtime-2026-01-15
ALIYUN_MODEL_VD=qwen3-tts-vd-realtime-2026-01-15

Backend Options:

• DEFAULT_BACKEND: Default TTS backend, options: local or aliyun
• Local Mode: Uses local Qwen3-TTS model (requires MODEL_BASE_PATH configuration)
• Aliyun Mode: Uses Aliyun TTS API (requires users to configure their API keys in settings)

Aliyun Configuration:

• Users need to add their Aliyun API keys in the web interface settings page
• API keys are encrypted and stored securely in the database
• Superuser can enable/disable local model access for all users
• To obtain an Aliyun API key, visit the Aliyun Console

Usage

Switching Between Backends

1. Log in to the web interface
2. Navigate to Settings page
3. Configure your preferred backend:
   • Local Model: Select "本地模型" (requires local model to be enabled by superuser)
   • Aliyun API: Select "阿里云" and add your API key
4. The selected backend will be used for all TTS operations by default
5. You can also specify a different backend per request using the backend parameter in the API

Managing Aliyun API Key

1. In Settings page, find the "阿里云 API 密钥" section
2. Enter your Aliyun API key
3. Click "更新密钥" to save and validate
4. The system will verify the key before saving
5. You can delete the key anytime using the delete button

Acknowledgments

This project is built upon the excellent work of the official Qwen3-TTS repository by the Qwen Team at Alibaba Cloud. Special thanks to the Qwen Team for open-sourcing such a powerful text-to-speech model.

License

Apache-2.0 license