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MCP Audio Transcriber

MCP Audio Transcriber

Public
ShreyasTembhare/MCP---Audio-Transcriber

Dockerized Python tool implementing the Model Context Protocol (MCP) via AssemblyAI's API to transcribe audio files or URLs into structured JSON with CLI and Streamlit web UI support.

python
0 tools
May 29, 2025
Updated Jun 4, 2025

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MCP Audio Transcriber

A Dockerized Python tool that implements the Model Context Protocol (MCP) via AssemblyAI's API. Upload or point to an audio file, and receive a structured JSON transcription.

Features

  • AssemblyMCP: a concrete MCP implementation that uses AssemblyAI's REST API
  • Command-line interface (app.py):
    python app.py
  • Streamlit web UI (streamlit_app.py):
    • Upload local files or paste URLs
    • Click Transcribe
    • Preview transcript and download JSON
  • Docker support for environment consistency and portability

Prerequisites

  • Python 3.10+
  • An AssemblyAI API key
  • ffmpeg (for local decoding, if using local files)
  • (Optional) Docker Desktop / Engine
  • (Optional) Streamlit (pip install streamlit)

šŸ”§ Installation

  1. Clone the repo

    git clone https://github.com/ShreyasTembhare/MCP---Audio-Transcriber.git cd MCP---Audio-Transcriber
  2. Create a .env

    ASSEMBLYAI_API_KEY=your_assemblyai_api_key_here
    
  3. Ensure .gitignore contains:

    .env
  4. Install Python dependencies

    pip install --upgrade pip pip install -r requirements.txt
  5. Install ffmpeg

    • Ubuntu/Debian: sudo apt update && sudo apt install ffmpeg -y
    • Windows: download from https://ffmpeg.org and add its bin/ to your PATH

Usage

1. CLI Transcription

python app.py
  • ``: any file or URL supported by AssemblyAI
  • ``: path for the generated JSON

Example:

python app.py data/input.ogg data/output.json cat data/output.json

2. Streamlit Web UI

streamlit run streamlit_app.py
  • Open http://localhost:8501
  • Upload or enter an audio URL
  • Click Transcribe
  • Download the JSON result

3. Docker

Build the image:

docker build -t mcp-transcriber .

Run it (mounting your data/ folder):

docker run --rm \ -e ASSEMBLYAI_API_KEY="$ASSEMBLYAI_API_KEY" \ -v "$(pwd)/data:/data" \ mcp-transcriber:latest \ /data/input.ogg /data/output.json

Then inspect:

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

Windows PowerShell:

docker run --rm ` -e ASSEMBLYAI_API_KEY=$env:ASSEMBLYAI_API_KEY ` -v "${PWD}\data:/data" ` mcp-transcriber:latest ` /data/input.ogg /data/output.json

Project Structure

MCP-Audio-Transcriber/
ā”œā”€ā”€ app.py               # CLI entrypoint (AssemblyMCP only)
ā”œā”€ā”€ mcp.py               # ModelContextProtocol + AssemblyMCP
ā”œā”€ā”€ streamlit_app.py     # Streamlit interface
ā”œā”€ā”€ requirements.txt     # assemblyai, python-dotenv, streamlit, etc.
ā”œā”€ā”€ Dockerfile           # builds the container
ā”œā”€ā”€ .gitignore           # ignores .env, __pycache__, etc.
ā”œā”€ā”€ LICENSE              # MIT license
└── data/                # sample input and output
    ā”œā”€ā”€ input.ogg
    └── output.json
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