Merge pull request #12 from dbccccccc/v2

v2

Author: dbccccccc

.env

@@ -0,0 +1,6 @@
+HOST=localhost
+PORT=7000
+VERIFY_SSL=true
+MAX_QUEUE_SIZE=100
+RATE_LIMIT_REQUESTS=30
+RATE_LIMIT_WINDOW=60 

.env.example

@@ -8,4 +8,8 @@ VERIFY_SSL=true
 # Queue configuration
 MAX_QUEUE_SIZE=100
 RATE_LIMIT_REQUESTS=30
-RATE_LIMIT_WINDOW=60 
+RATE_LIMIT_WINDOW=60
+
+# Flask configuration
+FLASK_ENV=production
+FLASK_APP=app.py 

.github/workflows/docker-build.yml

@@ -36,6 +36,8 @@ jobs:
             type=raw,value=latest,enable=${{ !github.event.release.prerelease }}
           labels: |
             org.opencontainers.image.source=${{ github.repositoryUrl }}
+            org.opencontainers.image.description=TTS API server compatible with OpenAI's TTS API format
+            org.opencontainers.image.licenses=MIT
 
       - name: Build and push
         id: build-and-push

Dockerfile

@@ -1,30 +1,43 @@
 # Use Python 3.13 slim image as base
 FROM python:3.13-slim
 
-# Set working directory to root
-WORKDIR /
+# Install Redis
+RUN apt-get update && \
+    apt-get install -y redis-server && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists/*
+
+# Set working directory
+WORKDIR /app
 
 # Copy requirements first to leverage Docker cache
-COPY requirements.txt .
+COPY flask_app/requirements.txt .
 
 # Install dependencies
 RUN pip install --no-cache-dir -r requirements.txt
 
-# Copy all application directories and files
-COPY main.py .
-COPY server/ server/
-COPY utils/ utils/
-COPY static/ static/
-COPY voices/ voices/
+# Copy Flask application
+COPY flask_app/ .
+
+# Create directory for Redis data
+RUN mkdir -p /data/redis
 
 # Set default environment variables
 ENV HOST=0.0.0.0 \
     PORT=7000 \
     VERIFY_SSL=true \
-    MAX_QUEUE_SIZE=100
+    MAX_QUEUE_SIZE=100 \
+    FLASK_ENV=production \
+    FLASK_APP=app.py \
+    CELERY_BROKER_URL=redis://localhost:6379/0 \
+    CELERY_RESULT_BACKEND=redis://localhost:6379/0
+
+# Expose ports for Flask and Redis
+EXPOSE 7000 6379
 
-# Expose port 7000
-EXPOSE 7000
+# Copy the startup script
+COPY docker-entrypoint.sh /usr/local/bin/
+RUN chmod +x /usr/local/bin/docker-entrypoint.sh
 
-# Command to run the application
-CMD ["python", "main.py"] 
+# Set the entrypoint
+ENTRYPOINT ["docker-entrypoint.sh"] 

README.md

@@ -5,102 +5,141 @@
 [![GitHub Stars](https://img.shields.io/github/stars/dbccccccc/ttsfm?style=social)](https://github.com/dbccccccc/ttsfm)
 
 > ⚠️ **Disclaimer**  
-> This project is for learning and testing purposes only. For production environments, please use [OpenAI's official TTS service](https://platform.openai.com/docs/guides/audio).
+> This project is for learning & testing purposes only. For production use, please use the [official OpenAI TTS service](https://platform.openai.com/docs/guides/audio).
+
+> ⚠️ **Development Notice**  
+> The v2 branch is currently under active development and is not recommended for production use. Please use the latest stable release version instead.
 
 English | [中文](README_CN.md)
 
-## 🌟 Project Introduction
+## 🌟 Project Overview
 
-TTSFM is a reverse-engineered API server that is fully compatible with OpenAI's Text-to-Speech (TTS) interface.
+TTSFM is a API server that's fully compatible with OpenAI's Text-to-Speech (TTS) API format.
 
-> 🎮 Try it now: [Official Demo Site](https://ttsapi.site/)
+> 🎮 Try it now: [Official Demo](https://ttsapi.site/)
 
 ## 🏗️ Project Structure
 
 ```text
 ttsfm/
-├── main.py              # Application entry
-├── server/              # Core services
-│   ├── api.py           # OpenAI-compatible API
-│   └── handlers.py      # Request handlers
-├── utils/               # Utility modules
-│   └── config.py        # Configuration management
-├── static/              # Frontend resources
-│   ├── index.html       # English interface
-│   ├── index_zh.html    # Chinese interface
-│   ├── script.js        # Frontend JavaScript
-│   └── styles.css       # Frontend styles
-├── pressure_test.py     # Stress testing script
-├── Dockerfile          # Docker configuration
+├── app.py              # Main Flask application
+├── celery_worker.py    # Celery configuration and tasks
 ├── requirements.txt    # Python dependencies
-└── .env.example       # Environment variables template
+├── static/            # Frontend resources
+│   ├── index.html     # English interface
+│   ├── index_zh.html  # Chinese interface
+│   ├── script.js      # Frontend JavaScript
+│   └── styles.css     # Frontend styles
+├── voices/            # Voice samples
+├── Dockerfile         # Docker configuration
+├── docker-entrypoint.sh # Docker startup script
+├── .env.example       # Environment variables template
+├── .env              # Environment variables
+├── .gitignore        # Git ignore rules
+├── LICENSE           # MIT License
+├── README.md         # English documentation
+├── README_CN.md      # Chinese documentation
+├── test_api.py       # API test suite
+├── test_queue.py     # Queue test suite
+└── .github/          # GitHub workflows
 ```
 
 ## 🚀 Quick Start
 
 ### System Requirements
-- Python ≥ 3.8
-- Or Docker environment
+- Docker and Docker Compose
+- or Python ≥ 3.8 with Redis
 
 ### 🐳 Docker Run (Recommended)
 
 Basic usage:
 ```bash
-docker run -p 7000:7000 dbcccc/ttsfm:latest
+docker run -p 7000:7000 -p 6379:6379 dbcccc/ttsfm:latest
 ```
 
-Custom configuration using environment variables:
+Custom configuration with environment variables:
 ```bash
 docker run -d \
   -p 7000:7000 \
+  -p 6379:6379 \
   -e HOST=0.0.0.0 \
   -e PORT=7000 \
   -e VERIFY_SSL=true \
   -e MAX_QUEUE_SIZE=100 \
   -e RATE_LIMIT_REQUESTS=30 \
   -e RATE_LIMIT_WINDOW=60 \
+  -e CELERY_BROKER_URL=redis://localhost:6379/0 \
+  -e CELERY_RESULT_BACKEND=redis://localhost:6379/0 \
   dbcccc/ttsfm:latest
 ```
 
 Available environment variables:
 - `HOST`: Server host (default: 0.0.0.0)
 - `PORT`: Server port (default: 7000)
-- `VERIFY_SSL`: Whether to verify SSL certificates (default: true)
-- `MAX_QUEUE_SIZE`: Maximum number of tasks in queue (default: 100)
-- `RATE_LIMIT_REQUESTS`: Maximum number of requests per time window (default: 30)
-- `RATE_LIMIT_WINDOW`: Time window in seconds for rate limiting (default: 60)
-
-> 💡 **Tip**  
-> MacOS users experiencing port conflicts can use alternative ports:  
-> `docker run -p 5051:7000 dbcccc/ttsfm:latest`
+- `VERIFY_SSL`: Verify SSL certificates (default: true)
+- `MAX_QUEUE_SIZE`: Maximum queue size (default: 100)
+- `RATE_LIMIT_REQUESTS`: Maximum requests per time window (default: 30)
+- `RATE_LIMIT_WINDOW`: Rate limit time window in seconds (default: 60)
+- `CELERY_BROKER_URL`: Redis broker URL (default: redis://localhost:6379/0)
+- `CELERY_RESULT_BACKEND`: Redis result backend URL (default: redis://localhost:6379/0)
 
 ### 📦 Manual Installation
 
-1. Download the latest release package from [GitHub Releases](https://github.com/dbccccccc/ttsfm/releases)
-2. Extract and enter the directory:
+1. Clone the repository:
 ```bash
-tar -zxvf ttsfm-vX.X.X.tar.gz
-cd ttsfm-vX.X.X
+git clone https://github.com/dbccccccc/ttsfm.git
+cd ttsfm
 ```
-3. Install dependencies and launch:
+
+2. Install dependencies and start:
 ```bash
+cd flask_app
 pip install -r requirements.txt
-cp .env.example .env  # Edit config as needed
-python main.py
+
+# Start Redis server
+redis-server
+
+# In a new terminal, start Celery worker
+celery -A celery_worker.celery worker --pool=solo -l info
+
+# In another terminal, start Flask application
+python app.py
 ```
 
 ## 📚 Usage Guide
 
 ### Web Interface
-Access `http://localhost:7000` to experience the interactive demo
+Visit `http://localhost:7000` for the interactive demo
 
 ### API Endpoints
 | Endpoint | Method | Description |
-|------|------|-------------|
-| `/v1/audio/speech` | POST | Text-to-Speech |
+|----------|--------|-------------|
+| `/v1/audio/speech` | POST | Text to Speech |
 | `/api/queue-size` | GET | Query task queue |
+| `/api/voice-sample/<voice>` | GET | Get voice sample |
+| `/api/version` | GET | Get API version |
 
-> 🔍 Complete API documentation is available via the web interface after local deployment
+> 🔍 Complete API documentation is available in the web interface after local deployment
+
+## 🔧 Architecture
+
+The application uses a distributed task queue architecture:
+
+1. **Flask Application**: Handles HTTP requests and serves the web interface
+2. **Celery**: Manages asynchronous task processing
+3. **Redis**: Acts as message broker and result backend
+4. **Task Queue**: Processes TTS requests asynchronously
+
+```mermaid
+graph TD
+    A[Client] -->|HTTP Request| B[Flask App]
+    B -->|Task| C[Celery]
+    C -->|Queue| D[Redis]
+    D -->|Process| E[Celery Worker]
+    E -->|Result| D
+    D -->|Response| B
+    B -->|HTTP Response| A
+```
 
 ## 🤝 Contributing
 
@@ -114,4 +153,4 @@ We welcome all forms of contributions! You can participate by:
 
 ## 📈 Project Activity
 
-[![Star History Chart](https://api.star-history.com/svg?repos=dbccccccc/ttsfm&type=Date)](https://star-history.com/#dbccccccc/ttsfm&Date)
+[![Star History Chart](https://api.star-history.com/svg?repos=dbccccccc/ttsfm&type=Date)](https://star-history.com/#dbccccccc/ttsfm&Date)