Translates English and Mandarin WAV speech with local Whisper and Piper, using Claude for text translation.
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๐ก Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Inspect callable tools, capabilities, and parameters exposed to AI agents by Live Translate MCP.
translate_fileTranslate a WAV audio file. Pass an absolute path โ the server transcribes it, translates the text via Claude, synthesises speech, saves `<name>_translated.wav` next to the original, and plays it automatically.
translate_speechTranslate raw audio passed as a base64-encoded WAV string. Returns the transcription, translation, and synthesised audio as base64 WAV โ useful for programmatic workflows.
health_checkVerify that all dependencies (Whisper model cache, Piper voice files, `espeak-ng`) are present and ready before making a translation request.
The waxberry-dev/live-translate-mcp MCP server adds English-to-Mandarin and Mandarin-to-English speech translation tools to compatible MCP clients. It accepts WAV audio, identifies whether the speech is English or Mandarin, transcribes it with Whisper, sends the resulting text to Claude for translation, and converts the translated text back into speech with Piper TTS.
Audio processing stays on the local machine for speech recognition and synthesis. The translated text is sent to the Anthropic API; the source audio itself is not uploaded by these processing stages. The project is intended for Claude Desktop and Claude Code workflows where an agent needs to listen to a recording and return spoken output.
For file-based requests, translate_file receives an absolute WAV path. The server performs the full pipeline, writes a translated WAV file beside the source using a _translated.wav suffix, and plays the result automatically. The tool returns the original transcription, the translated text, and the output path.
For applications that already hold audio in memory, translate_speech accepts a base64-encoded WAV string. Its response contains the transcription, translation, and generated speech encoded as base64 WAV data, avoiding the need to manage an input or output file through the tool.
Whisper model data and Piper voice files are downloaded on first use and cached under ~/.live-translate/ by default. The supported speech languages are English and Mandarin Chinese, with language detection handled automatically.
Run the waxberry-dev/live-translate-mcp MCP server with Node.js 18 or newer:
Set ANTHROPIC_API_KEY in the server environment. This credential is required because Claude performs the text translation. Install espeak-ng separately so Piper can phonemise text; macOS users can install it with Homebrew, while Linux users can use their distribution package manager.
Piper voice files normally use ~/.live-translate/voices. Set PIPER_VOICE_DIR when the voice files are stored elsewhere. The Whisper and Piper model downloads occur automatically on the first translation request.
In Claude Desktop, register the server with the npx command and pass the API key through the MCP server's env configuration. Claude Code can add the same server through its MCP configuration, with the API key available in the process environment or MCP environment settings.
translate_file translates an absolute-path WAV recording, saves a sibling translated WAV file, and starts playback.translate_speech handles base64-encoded WAV input and returns base64-encoded synthesized WAV output alongside the text results.health_check checks whether the Whisper cache, Piper voice files, and espeak-ng are available before translation.The waxberry-dev/live-translate-mcp MCP server is suitable for meeting clips, recorded conversations, and programmatic audio workflows. It does not provide a general offline translation pipeline: Claude remains necessary for the translation step, even though transcription and speech synthesis are local.
Input audio is documented as WAV, and file requests require an absolute path. Translation is limited to English and Mandarin Chinese. Audio processing is partly offline rather than fully offline because translated text is sent to Claude through the Anthropic API. The first request may need to download approximately 150 MB of Whisper model data and approximately 200 MB of Piper voice models, which are then cached locally.
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