MCP Audio Transcriber

A portable, Dockerized Python tool that implements a Model Context Protocol (MCP) for audio transcription using OpenAI's Whisper models—and even ships with a Streamlit-powered web UI so you can upload an audio file and download the transcription as JSON.

🚀 Features

• Modular MCP interface (mcp.py) that defines a standard ModelContextProtocol.
• Whisper-based implementation (WhisperMCP) for high-quality, multi-language transcription.
• Command-line interface (app.py) for batch or ad-hoc transcription:

  python app.py <input_audio> <output_json> [--model MODEL_NAME]
  

• Docker support for a consistent runtime:

  docker build -t mcp-transcriber .
  docker run --rm \
    -v /full/path/to/data:/data \
    mcp-transcriber:latest \
    /data/input.wav /data/output.json
  

• Streamlit web app (streamlit_app.py) letting end users:
  • Upload any common audio file (.wav, .mp3, .ogg, .m4a)
  • Choose a Whisper model size
  • Preview the transcription live
  • Download the JSON result with one click

📦 Prerequisites

• Python 3.10+
• ffmpeg installed & on your PATH
• (Optional) Docker Engine / Docker Desktop
• (Optional) Streamlit

🔧 Installation

1. Clone the repo

   git clone https://github.com/ShreyasTembhare/MCP---Audio-Transcriber.git
   cd MCP---Audio-Transcriber
   

2. Python dependencies & FFmpeg

   pip install --upgrade pip
   pip install -r requirements.txt
   # On Ubuntu/Debian:
   sudo apt update && sudo apt install ffmpeg
   # On Windows:
   #   Download a static build from https://ffmpeg.org and add its bin/ to your PATH
   

3. (Optional) Docker

   • Install Docker Desktop
   • Enable WSL integration if using WSL2.

4. (Optional) Streamlit

   pip install streamlit
   

🎯 Usage

1. CLI Transcription

python app.py <input_audio> <output_json> [--model tiny|base|small|medium|large]

• <input_audio>: path to your audio file
• <output_json>: path where the JSON result will be saved
• --model: choose Whisper model size (default: base)

Example:

python app.py data/input.ogg data/output.json --model tiny
cat data/output.json

2. Docker

Build the image:

docker build -t mcp-transcriber .

Run it (mounting your data/ folder):

docker run --rm \
  -v "/full/path/to/your/project/data:/data" \
  mcp-transcriber:latest \
  /data/input.wav /data/output.json

Then inspect:

ls data/output.json
cat data/output.json

3. Streamlit Web UI

Launch the app:

streamlit run streamlit_app.py

• Open http://localhost:8501 in your browser
• Upload an audio file
• Select the Whisper model size
• Click Transcribe
• Preview & download the resulting JSON

📁 Project Structure

MCP-Audio-Transcriber/
├── app.py               # CLI entrypoint
├── mcp.py               # Model Context Protocol + WhisperMCP
├── requirements.txt     # Python dependencies
├── streamlit_app.py     # Streamlit interface
├── Dockerfile           # Container definition
├── .gitignore           # ignore **pycache**, venvs, etc.
├── LICENSE              # MIT license
└── data/                # sample input and output
    ├── input.ogg
    └── output.json