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README.md

@@ -13,96 +13,99 @@ python_version: 3.10.12
 
 # 🎙️ Modal Transcriber MCP
 
-一个功能强大的音频转录系统，集成了 Gradio UI、FastMCP Tools 和 Modal 云计算，支持智能说话人识别。
+A powerful audio transcription system integrating Gradio UI, FastMCP Tools, and Modal cloud computing with intelligent speaker identification.
 
-## ✨ 主要功能
+## ✨ Key Features
 
-- **🎵 多平台音频下载**：支持 Apple Podcasts、小宇宙等播客平台
-- **🚀 高性能转录**：基于 OpenAI Whisper，支持多种模型（turbo, large-v3等）
-- **🎤 智能说话人识别**：使用 pyannote.audio 进行说话人分离和embedding聚类
-- **⚡ 分布式处理**：支持大文件并发切片处理，显著提升处理速度
-- **🔧 FastMCP 工具**：提供完整的 MCP (Model Context Protocol) 工具集成
-- **☁️ Modal 部署**：支持本地和云端双模式部署
+- **🎵 Multi-platform Audio Download**: Support for Apple Podcasts, XiaoYuZhou, and other podcast platforms
+- **🚀 High-performance Transcription**: Based on OpenAI Whisper with multiple model support (turbo, large-v3, etc.)
+- **🎤 Intelligent Speaker Identification**: Using pyannote.audio for speaker separation and embedding clustering
+- **⚡ Distributed Processing**: Support for large file concurrent chunk processing, significantly improving processing speed
+- **🔧 FastMCP Tools**: Complete MCP (Model Context Protocol) tool integration
+- **☁️ Modal Deployment**: Support for both local and cloud deployment modes
 
-## 🎯 核心优势
+## 🎯 Core Advantages
 
-### 🧠 智能音频分割
-- **静音检测分割**：自动识别音频中的静音段落进行智能切分
-- **Fallback机制**：长音频自动降级为时间分割，确保处理效率
-- **并发处理**：多chunk同时处理，大幅提升转录速度
+### 🧠 Intelligent Audio Segmentation
+- **Silence Detection Segmentation**: Automatically identify silent segments in audio for intelligent chunking
+- **Fallback Mechanism**: Long audio automatically degrades to time-based segmentation, ensuring processing efficiency
+- **Concurrent Processing**: Multiple chunks processed simultaneously, dramatically improving transcription speed
 
-### 🎤 高级说话人识别
-- **Embedding聚类**：使用深度学习embedding进行说话人一致性识别
-- **跨chunk统一**：解决分布式处理中说话人标签不一致问题
-- **质量过滤**：自动过滤低质量片段，提升输出准确性
+### 🎤 Advanced Speaker Identification
+- **Embedding Clustering**: Using deep learning embeddings for speaker consistency identification
+- **Cross-chunk Unification**: Solving speaker label inconsistency issues in distributed processing
+- **Quality Filtering**: Automatically filter low-quality segments to improve output accuracy
 
-### 🔧 开发者友好
-- **MCP协议支持**：完整的工具调用接口
-- **REST API**：标准化的API接口
-- **Gradio UI**：直观的Web界面
-- **测试覆盖**：29个单元测试和集成测试
+### 🔧 Developer Friendly
+- **MCP Protocol Support**: Complete tool invocation interface
+- **REST API**: Standardized API interface
+- **Gradio UI**: Intuitive web interface
+- **Test Coverage**: 29 unit tests and integration tests
 
-## 🚀 快速开始
+## 🚀 Quick Start
 
-### 本地运行
+### Local Setup
 
-1. **克隆仓库**
+1. **Clone Repository**
 ```bash
 git clone https://huggingface.co/spaces/Agents-MCP-Hackathon/ModalTranscriberMCP
 cd ModalTranscriberMCP
 ```
 
-2. **安装依赖**
+2. **Install Dependencies**
 ```bash
 pip install -r requirements.txt
 ```
 
-3. **配置 Hugging Face Token**（可选，用于说话人识别）
+3. **Configure Hugging Face Token** (Optional, for speaker identification)
 ```bash
-# 创建 .env 文件
+# Create .env file
 echo "HF_TOKEN=your_huggingface_token_here" > .env
 ```
 
-4. **启动应用**
+4. **Start Application**
 ```bash
 python app.py
 ```
 
-### 使用说明
+### Usage Instructions
 
-1. **上传音频文件** 或 **输入播客URL**
-2. **选择转录选项**：
-   - 模型大小：turbo (推荐) / large-v3
-   - 输出格式：SRT / TXT
-   - 是否启用说话人识别
-3. **开始转录**，系统会自动处理并生成结果
+1. **Upload audio file** or **Input podcast URL**
+2. **Select transcription options**:
+   - Model size: turbo (recommended) / large-v3
+   - Output format: SRT / TXT
+   - Enable speaker identification
+3. **Start transcription**, the system will automatically process and generate results
 
-## 🛠️ 技术架构
+## 🛠️ Technical Architecture
 
-- **前端**：Gradio 4.44.0
-- **后端**：FastAPI + FastMCP
-- **转录引擎**：OpenAI Whisper
-- **说话人识别**：pyannote.audio
-- **云计算**：Modal.com
-- **音频处理**：FFmpeg
+- **Frontend**: Gradio 4.44.0
+- **Backend**: FastAPI + FastMCP
+- **Transcription Engine**: OpenAI Whisper
+- **Speaker Identification**: pyannote.audio
+- **Cloud Computing**: Modal.com
+- **Audio Processing**: FFmpeg
 
-## 📊 性能指标
+## 📊 Performance Metrics
 
-- **处理速度**：支持30倍实时速度转录
-- **并发能力**：最多10个chunks同时处理
-- **准确率**：中文准确率>95%
-- **支持格式**：MP3, WAV, M4A, FLAC等
+- **Processing Speed**: Support for 30x real-time transcription speed
+- **Concurrency**: Up to 10 chunks processed simultaneously
+- **Accuracy**: Chinese accuracy >95%
+- **Supported Formats**: MP3, WAV, M4A, FLAC, etc.
 
-## 🤝 贡献
+## 🤝 Contributing
 
-欢迎提交 Issue 和 Pull Request！
+Issues and Pull Requests are welcome!
 
-## 📜 许可证
+## 📜 License
 
 MIT License
 
-## 🔗 相关链接
+## 🔗 Related Links
 
-- **项目文档**：详见仓库中的 `docs/` 目录
-- **测试覆盖**：29个测试用例确保功能稳定性
-- **Modal部署**：支持云端高性能处理 
+- **Project Documentation**: See `docs/` directory in the repository
+- **Test Coverage**: 29 test cases ensuring functional stability
+- **Modal Deployment**: Support for cloud high-performance processing
+
+---
+*Last updated: 2025-06-11*