-
securityA
license-
qualityEnables recording audio from a microphone and transcribing it using OpenAI's Whisper model. Works as both a standalone MCP server and a Goose AI agent extension.
Last updated -
6
MIT License
Enables containerized deployment of the transcription service, making it portable and providing a consistent runtime environment.
Provides audio file processing capabilities, allowing the transcription service to handle various audio formats like .wav, .mp3, .ogg, and .m4a.
Integrates with OpenAI's Whisper models to provide high-quality, multi-language audio transcription with options for different model sizes.
Offers a web-based user interface for uploading audio files, selecting model parameters, previewing transcriptions, and downloading results.
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 <input_audio> <output_json>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)
🔧 Installation
Clone the repo
git clone https://github.com/ShreyasTembhare/MCP---Audio-Transcriber.git cd MCP---Audio-TranscriberCreate a
.envASSEMBLYAI_API_KEY=your_assemblyai_api_key_hereEnsure
.gitignorecontains:.envInstall Python dependencies
pip install --upgrade pip pip install -r requirements.txtInstall ffmpeg
Ubuntu/Debian:
sudo apt update && sudo apt install ffmpeg -yWindows: download from https://ffmpeg.org and add its
bin/to your PATH
Usage
1. CLI Transcription
python app.py <input_audio> <output_json>
<input_audio>: any file or URL supported by AssemblyAI<output_json>: 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
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/
├── app.py # CLI entrypoint (AssemblyMCP only)
├── mcp.py # ModelContextProtocol + AssemblyMCP
├── streamlit_app.py # Streamlit interface
├── requirements.txt # assemblyai, python-dotenv, streamlit, etc.
├── Dockerfile # builds the container
├── .gitignore # ignores .env, __pycache__, etc.
├── LICENSE # MIT license
└── data/ # sample input and output
├── input.ogg
└── output.json
This server cannot be installed
local-only server
The server can only run on the client's local machine because it depends on local resources.
A portable, Dockerized Python tool that implements Model Context Protocol for audio transcription using Whisper models, featuring both CLI and web UI interfaces for converting audio files to JSON transcriptions.
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