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waxberry-dev/live-translate-mcp

by Various

MCP server for local speech translation (EN ↔ 中文) via Whisper + Claude + Piper

W

MCP

waxberry-dev/live-translate-mcp

Added 15 June 2026

#anthropic #chinese #chinese-english-translator #claude #english #english-chinese #mandarin #mcp

Overview

An MCP server that performs local speech translation between English and Chinese using Whisper for speech recognition, Claude for translation, and Piper for text-to-speech. It runs entirely on the user's machine, enabling real-time voice translation without cloud dependencies.

Best for

Best for
Developers needing a local, privacy-preserving speech translation server for MCP-based workflows

Use cases

  • Real-time bilingual voice conversations
  • Local speech-to-speech translation for privacy-sensitive applications
  • Integrating live translation into MCP-compatible tools

Notes

An MCP server that performs local speech translation between English and Chinese using Whisper for speech recognition, Claude for translation, and Piper for text-to-speech. It runs entirely on the user’s machine, enabling real-time voice translation without cloud dependencies.

1 stars on GitHub. Last updated 2026-06-12.

Use cases

  • Real-time bilingual voice conversations
  • Local speech-to-speech translation for privacy-sensitive applications
  • Integrating live translation into MCP-compatible tools

Pros

  • Fully local operation preserves privacy and works offline
  • Leverages well-known open-source models (Whisper, Piper)
  • Simple MCP interface for integration with other systems

Cons

  • Very early stage with only 1 star and minimal community adoption
  • Requires local setup of multiple models and dependencies
  • Performance may be limited without a capable GPU

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Fully local operation preserves privacy and works offline
  • Leverages well-known open-source models (Whisper, Piper)
  • Simple MCP interface for integration with other systems

Cons

  • Very early stage with only 1 star and minimal community adoption
  • Requires local setup of multiple models and dependencies
  • Performance may be limited without a capable GPU

Pairs with

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