hub-translation

# Translation Nodes > Translate text between languages using neural machine translation ## Available Translation Methods > **Note:** Dedicated translation nodes are not yet in node-hub. Use LLM-based translation with dora-qwen or dora-qwen2-5-vl. | Method | Install | Description | |--------|---------|-------------| | dora-qwen | `pip install dora-qwen` | LLM-based translation | | dora-transformers | `pip install dora-transformers` | Huggingface models | ## LLM-Based Translation with dora-qwen Use Qwen LLM for translation tasks. ### YAML Configuration ```yaml - id: translate build: pip install dora-qwen path: dora-qwen inputs: text: whisper/text outputs: - text env: SYSTEM_PROMPT: "You are a translator. Translate the following text from English to Chinese. Output only the translation." ``` ### Supported Languages Qwen supports translation between most major language pairs including: - English, Chinese, Japanese, Korean - German, French, Spanish, Portuguese - And many more ## dora-transformers (Custom Model) Use Huggingface translation models via dora-transformers. ### YAML Configuration ```yaml - id: translate build: pip install dora-transformers path: dora-transformers inputs: text: input/text outputs: - text env: MODEL: Helsinki-NLP/opus-mt-en-zh TASK: translation ``` ### Features - Use any Huggingface translation model - Support for Opus-MT, M2M100, NLLB models - Offline inference supported ## Real-Time Translation Pipeline ```yaml nodes: # Microphone - id: microphone build: pip install dora-microphone path: dora-microphone inputs: tick: dora/timer/millis/100 outputs: - audio # VAD - id: vad build: pip install dora-vad path: dora-vad inputs: audio: microphone/audio outputs: - audio # Speech to text - id: whisper build: pip install dora-distil-whisper path: dora-distil-whisper inputs: input: vad/audio outputs: - text env: TARGET_LANGUAGE: english # Translation (using LLM) - id: translate build: pip install dora-qwen path: dora-qwen inputs: text: whisper/text outputs: - text env: SYSTEM_PROMPT: "Translate to Chinese:" # TTS for translated text - id: tts build: pip install dora-kokoro-tts path: dora-kokoro-tts inputs: text: translate/text outputs: - audio # Speaker - id: speaker build: pip install dora-pyaudio path: dora-pyaudio inputs: audio: tts/audio # Visualization - id: rerun build: pip install dora-rerun path: dora-rerun inputs: original: source: whisper/text metadata: primitive: "text" translated: source: translate/text metadata: primitive: "text" ``` ## Multi-Language Translation Use multiple dora-qwen instances with different translation prompts: ```yaml nodes: - id: input build: pip install terminal-input path: terminal-input outputs: - text # English to Chinese - id: en_to_zh build: pip install dora-qwen path: dora-qwen inputs: text: input/text outputs: - text env: SYSTEM_PROMPT: "Translate the following text to Chinese. Output only the translation." # English to Japanese - id: en_to_ja build: pip install dora-qwen path: dora-qwen inputs: text: input/text outputs: - text env: SYSTEM_PROMPT: "Translate the following text to Japanese. Output only the translation." # English to Spanish - id: en_to_es build: pip install dora-qwen path: dora-qwen inputs: text: input/text outputs: - text env: SYSTEM_PROMPT: "Translate the following text to Spanish. Output only the translation." - id: rerun build: pip install dora-rerun path: dora-rerun inputs: original: input/text chinese: en_to_zh/text japanese: en_to_ja/text spanish: en_to_es/text ``` ## Text Data Format ### Input/Output ```python import pyarrow as pa # Send text for translation text = "Hello, how are you?" node.send_output("text", pa.array([text])) # Receive translated text translated = event["value"][0].as_py() ``` ## Related Skills - **hub-audio** - Speech-to-text for voice translation - **hub-llm** - Language models - **hub-visualization** - Display original and translated text