Whisper Telegram

Transcribe and speak — two-way voice for Claude via Telegram

CI PyPI Downloads Python License: MIT MCP Ko-fi

An MCP server that gives Claude two-way voice capabilities via Telegram: transcribe incoming voice messages with Whisper, and reply with synthesized speech. Works with Claude Desktop, Claude Code, and any MCP-compatible client.

What It Does

• Transcribe local audio files -- OGG, WAV, MP3, FLAC, and more
• Transcribe Telegram voice messages -- pass a file_id, get text back
• Speak text as voice notes -- synthesise speech and send back as OGG (plays as a voice note in Telegram)
• Two transcription backends -- local faster-whisper (free, private) or OpenAI Whisper API (cloud)
• Auto mode -- tries local first, falls back to OpenAI if it fails
• Language detection -- automatic or specify an ISO-639-1 code
• Word-level timestamps -- optional fine-grained timing

Prerequisites

Feature	Requirement
Transcription (local)	None — faster-whisper bundled via [local] extras
Transcription (cloud)	OPENAI_API_KEY env var
Voice replies — Kokoro (best quality)	Docker — run docker run -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-cpu:latest
Voice replies — OpenAI TTS (fallback)	OPENAI_API_KEY env var
Voice replies — macOS say (last resort)	Mac only, no setup

Kokoro requires Docker. If Docker isn't running, voice replies fall back to OpenAI TTS or macOS say automatically.

Quick Start

Set up in 30 seconds with Claude Code

The fastest way to get started — just tell Claude Code to set it up for you:

1. Add to your .mcp.json (Claude Code) or claude_desktop_config.json (Claude Desktop):

{
  "mcpServers": {
    "whisper-telegram-mcp": {
      "command": "uvx",
      "args": ["whisper-telegram-mcp"],
      "env": {
        "TELEGRAM_BOT_TOKEN": "your-bot-token-here"
      }
    }
  }
}

2. Restart Claude and say: "Set up my Telegram bot for voice transcription" — Claude will walk you through creating the bot with BotFather and configuring everything.

One command with uvx

uvx whisper-telegram-mcp

No installation needed -- uvx handles everything.

Or install with pip

pip install "whisper-telegram-mcp[all]"
whisper-telegram-mcp

Telegram Bot Setup

1. Open Telegram and message @BotFather
2. Send /newbot and follow the prompts to create a bot
3. Copy the token (looks like 1234567890:ABCdef...)
4. Add TELEGRAM_BOT_TOKEN to your MCP config env (see below)
5. Message your bot to start — it'll only respond to approved users

The Claude Telegram plugin handles access control. See its docs for pairing/allowlist setup.

Integration

Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "whisper-telegram-mcp": {
      "command": "uvx",
      "args": ["whisper-telegram-mcp"],
      "env": {
        "WHISPER_MODEL": "base",
        "WHISPER_BACKEND": "auto",
        "TELEGRAM_BOT_TOKEN": "your-bot-token-here"
      }
    }
  }
}

Claude Code

Add to your project's .mcp.json:

{
  "mcpServers": {
    "whisper-telegram-mcp": {
      "command": "uvx",
      "args": ["whisper-telegram-mcp"],
      "env": {
        "WHISPER_MODEL": "base",
        "WHISPER_BACKEND": "auto",
        "TELEGRAM_BOT_TOKEN": "your-bot-token-here"
      }
    }
  }
}

Tools

Tool	Description
transcribe_audio	Transcribe a local audio file (OGG, WAV, MP3, etc.) to text
transcribe_telegram_voice	Download and transcribe a Telegram voice message by file_id
speak_text	Convert text to speech → OGG/Opus file (plays as voice note in Telegram)
list_models	List available Whisper model sizes with speed/accuracy info
check_backends	Check which backends (local/OpenAI) are available and configured

transcribe_audio

file_path: str        # Absolute path to audio file
language: str | None  # ISO-639-1 code (e.g. "en"), None = auto-detect
word_timestamps: bool # Include word-level timestamps (default: false)

transcribe_telegram_voice

file_id: str          # Telegram voice message file_id
bot_token: str | None # Bot token (falls back to TELEGRAM_BOT_TOKEN env var)
language: str | None  # ISO-639-1 code, None = auto-detect
word_timestamps: bool # Include word-level timestamps (default: false)

speak_text

Converts text to an OGG/Opus audio file. Automatically selects the best available TTS backend.

text: str             # Text to synthesise
voice: str            # Voice name (default: "af_sky")
output_path: str|None # Optional path for output .ogg file

TTS Backends (in priority order):

Backend	Cost	Quality	Setup
Kokoro (local)	Free	Natural, high quality	Start manually (see below)
OpenAI TTS (cloud)	~$0.015/1k chars	High quality	OPENAI_API_KEY env var
macOS say (fallback)	Free	Robotic	Mac only, no setup

In auto mode (default), the server tries Kokoro first, then OpenAI, then macOS say. Configure with TTS_BACKEND env var.

Starting Kokoro locally:

Kokoro FastAPI is not on PyPI — start it before running the MCP server:

# Docker (simplest, recommended)
docker run -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-cpu:latest

# Apple Silicon (GPU-accelerated)
docker run -p 8880:8880 ghcr.io/remsky/kokoro-fastapi-gpu-mac:latest

# From source
git clone https://github.com/remsky/Kokoro-FastAPI && cd Kokoro-FastAPI && ./start-cpu.sh

Once running, the MCP server auto-detects it at http://127.0.0.1:8880/v1. Override with KOKORO_BASE_URL env var.

Kokoro voices (primary):

Voice	Accent	Style
af_sky	US	Female (default)
af_bella	US	Female
af_sarah	US	Female
af_nicole	US	Female
am_adam	US	Male
am_michael	US	Male
bf_emma	UK	Female
bf_isabella	UK	Female
bm_george	UK	Male
bm_lewis	UK	Male

OpenAI voices (fallback):

Voice	Style
alloy	Neutral
echo	Male
fable	Narrative
onyx	Deep male
nova	Female
shimmer	Soft female

Kokoro voice names are automatically mapped to the closest OpenAI or macOS equivalent when falling back.

Returns:

{
  "file_path": "/tmp/tmpXXX.ogg",
  "size_bytes": 16555,
  "backend": "kokoro",
  "voice": "af_sky",
  "success": true,
  "error": null
}

Send the returned file_path as a Telegram attachment and it will appear as a native voice note.

Transcription response format

All transcription tools return:

{
  "text": "Hello, this is a voice message.",
  "language": "en",
  "language_probability": 0.98,
  "duration": 3.5,
  "segments": [
    {"start": 0.0, "end": 3.5, "text": "Hello, this is a voice message."}
  ],
  "backend": "local",
  "success": true,
  "error": null
}

Configuration

All configuration is via environment variables:

Variable	Default	Description
WHISPER_BACKEND	auto	auto, local, or openai
WHISPER_MODEL	base	Whisper model size (see below)
OPENAI_API_KEY	--	Required for openai transcription and TTS backends
TELEGRAM_BOT_TOKEN	--	Required for transcribe_telegram_voice
WHISPER_LANGUAGE	auto-detect	ISO-639-1 language code
TTS_BACKEND	auto	auto, kokoro, openai, or macos
TTS_VOICE	af_sky	Default voice for speak_text (Kokoro voice name)
KOKORO_BASE_URL	http://127.0.0.1:8880/v1	Kokoro FastAPI base URL

How It Works

                         MCP Client (Claude)
                              |
                         [MCP stdio]
                              |
                    whisper-telegram-mcp
                    /         |         \
                   /          |          \
      transcribe_audio  transcribe_     speak_text
                        telegram_voice      |
              |               |          auto_tts()
              |         [Bot API DL]    /    |    \
              +--------+------+     Kokoro OpenAI macOS
                       |            (local) (cloud) (say)
                 auto_transcribe()      |
                  /           \      .ogg file
           LocalBackend    OpenAIBackend
           (faster-whisper)  (Whisper API)

1. Claude sends a tool call via MCP (stdio transport)
2. For Telegram voice messages, the file is downloaded via Bot API
3. auto_transcribe() picks the best available transcription backend
4. auto_tts() picks the best available TTS backend (Kokoro -> OpenAI -> macOS)
5. Results are returned as structured JSON

Local vs OpenAI

	Local (faster-whisper)	OpenAI API
Cost	Free	$0.006/min
Privacy	All data stays on device	Audio sent to OpenAI
Speed	~1-10s depending on model	~1-3s
Setup	Automatic (downloads model on first use)	Requires OPENAI_API_KEY
Accuracy	Excellent with base or larger	Excellent
Offline	Yes	No

Model Sizes

Model	Parameters	Speed	Accuracy	VRAM
tiny	39M	Fastest	Lowest	~1GB
base	74M	Fast	Good	~1GB
small	244M	Moderate	Better	~2GB
medium	769M	Slow	High	~5GB
large-v3	1550M	Slowest	Highest	~10GB
turbo	~800M	Fast	High	~6GB

English-only variants (tiny.en, base.en, small.en, medium.en) are slightly more accurate for English.

Privacy & Data

• Local backend (faster-whisper): Audio stays on your device. Nothing leaves your machine.
• OpenAI backend: Audio sent to OpenAI API per their data retention policy
• Temporary files: Audio downloaded from Telegram is written to /tmp and deleted immediately after transcription
• Logs: Go to stderr only — no audio content or credentials are ever logged

Development

git clone https://github.com/abid-mahdi/whisper-telegram-mcp.git
cd whisper-telegram-mcp
python3.12 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

# Run unit tests
pytest tests/ -v -m "not integration"

# Run integration tests (downloads ~150MB model on first run)
pytest tests/ -m integration -v

# Run with coverage
pytest tests/ --cov=src/whisper_telegram_mcp --cov-report=term-missing

MCP Inspector

uvx mcp dev src/whisper_telegram_mcp/server.py

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feat/amazing-feature)
3. Run tests (pytest tests/ -v -m "not integration")
4. Commit with conventional commits (feat:, fix:, docs:, etc.)
5. Open a pull request

License

MIT