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Agents-MCP-Hackathon/ModalTranscriberMCP

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App README

🎙️ Modal Transcriber MCP

A powerful audio transcription system integrating Gradio UI, FastMCP Tools, and Modal cloud computing with intelligent speaker identification.

✨ Key Features

  • —🎵 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

🎯 Core Advantages

🧠 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

🎤 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

🔧 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

🚀 Quick Start

Local Setup

  1. 1.Clone Repository
bash
git clone https://huggingface.co/spaces/Agents-MCP-Hackathon/ModalTranscriberMCP
cd ModalTranscriberMCP
  1. 1.Install Dependencies
bash
pip install -r requirements.txt
  1. 1.Configure Hugging Face Token (Optional, for speaker identification)
bash
# Create .env file
echo "HF_TOKEN=your_huggingface_token_here" > .env
  1. 1.Start Application
bash
python app.py

Usage Instructions

  1. 1.Upload audio file or Input podcast URL
  2. 2.Select transcription options:
  3. 3.Model size: turbo (recommended) / large-v3
  4. 4.Output format: SRT / TXT
  5. 5.Enable speaker identification
  6. 6.Start transcription, the system will automatically process and generate results

🛠️ Technical Architecture

  • —Frontend: Gradio 4.44.0
  • —Backend: FastAPI + FastMCP
  • —Transcription Engine: OpenAI Whisper
  • —Speaker Identification: pyannote.audio
  • —Cloud Computing: Modal.com
  • —Audio Processing: FFmpeg

📊 Performance Metrics

  • —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.

🤝 Contributing

Issues and Pull Requests are welcome!

📜 License

MIT License

🔗 Related Links

  • —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