Package Exports

@redlasha/talk-to
@redlasha/talk-to/dist/cli.js

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (@redlasha/talk-to) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

talk-to

Korean voice MCP server for Claude Code. Speak and listen in Korean.

Features

voice_listen - Microphone → Korean text (STT)
voice_speak - Text → Korean speech (TTS)
voice_converse - Speak then listen (bidirectional)

Quick Start

1. Install system dependencies

Linux:

sudo apt install sox libsox-fmt-all
pip install edge-tts

macOS:

brew install sox
pip install edge-tts

Windows:

pip install edge-tts
# sox is NOT required on Windows — audio uses PowerShell natively

2. Add to Claude Code

Linux / macOS:

claude mcp add talk-to -- npx -y @redlasha/talk-to mcp

Windows:

claude mcp add talk-to -- cmd /c npx -y @redlasha/talk-to mcp

With Groq API key (optional cloud STT fallback):

# Linux / macOS
claude mcp add --env GROQ_API_KEY=gsk_xxx talk-to -- npx -y @redlasha/talk-to mcp

# Windows
claude mcp add --env GROQ_API_KEY=gsk_xxx talk-to -- cmd /c npx -y @redlasha/talk-to mcp

Done. Start a new Claude Code session and the tools are available.

3. STT Backend (choose one)

Option A: Local Whisper (recommended, free)

# whisper.cpp server on port 2022
./whisper-server -m models/ggml-medium.bin -l ko --port 2022

GPU acceleration is strongly recommended. Without GPU, STT takes 5-10 seconds per utterance. With GPU, it drops to under 1 second. See GPU Setup below.

Option B: Groq API (cloud fallback)

# Set via claude mcp add --env, or export directly
export GROQ_API_KEY=gsk_your_key_here

CLI Commands

npx @redlasha/talk-to check   # Verify dependencies (auto-detects OS)
npx @redlasha/talk-to test    # Quick voice roundtrip test
npx @redlasha/talk-to setup   # Print .mcp.json config
npx @redlasha/talk-to help    # Show help

Korean Voices

Voice	ID
Female (default)	`ko-KR-SunHiNeural`
Male	`ko-KR-InJoonNeural`

Environment Variables

Variable	Description	Required
`GROQ_API_KEY`	Groq API key for cloud STT	No (if local Whisper)
`WHISPER_URL`	Local Whisper server URL	No (default: `http://localhost:2022`)

GPU Setup for Whisper

GPU acceleration makes the difference between unusable and real-time STT.

Setup	Latency	Experience
CPU only	5-10s per utterance	Painful, conversation breaks
NVIDIA GPU (CUDA)	< 1s	Natural conversation flow
Apple Silicon (Metal)	< 1s	Built-in, no extra setup

Linux (NVIDIA CUDA)

# 1. Install CUDA toolkit
sudo apt install nvidia-cuda-toolkit

# 2. Build whisper.cpp with CUDA
git clone https://github.com/ggerganov/whisper.cpp
cd whisper.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release

# 3. Download Korean-optimized model
./models/download-ggml-model.sh medium

# 4. Run server with GPU
./build/bin/whisper-server -m models/ggml-medium.bin -l ko --port 2022

macOS (Apple Silicon)

Metal is enabled by default. No extra setup needed:

cmake -B build
cmake --build build --config Release
./build/bin/whisper-server -m models/ggml-medium.bin -l ko --port 2022

Windows (NVIDIA CUDA)

# Install CUDA Toolkit from https://developer.nvidia.com/cuda-downloads
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release
.\build\bin\Release\whisper-server.exe -m models\ggml-medium.bin -l ko --port 2022

Model Selection

Model	Size	Accuracy	GPU VRAM
`base`	150MB	Good	~300MB
`small`	460MB	Better	~1GB
`medium`	1.5GB	Best for Korean	~2.5GB
`large-v3`	3GB	Highest accuracy	~5GB

medium is recommended for Korean - best balance of accuracy and speed.

Platform Support

Platform	Audio	edge-tts	Whisper	Status
Linux (Ubuntu/Debian)	sox (apt)	pip	whisper.cpp	Tested
macOS	sox (brew)	pip	whisper.cpp	Supported
Windows	PowerShell (built-in)	pip	whisper.cpp	Supported

License

MIT