Node Trans
Real-time audio transcription and translation app. Captures audio from microphone, system audio, or both. Supports two transcription modes: Soniox (cloud, built-in translation) and Local Whisper (fully offline).
Runs as a web app (browser) or desktop app (Electron). Supports macOS, Windows, and Linux.
Features
- Real-time speech-to-text with live partial results
- Automatic translation (cloud via Soniox, or local via Ollama / LibreTranslate)
- Speaker diarization — identifies who is speaking (
Speaker 1,Speaker 2, ...) - Session history with speaker renaming and Markdown export
- Always-on-top overlay window (Electron)
- Bilingual UI (English / Vietnamese)
Quick Start
npm install
npm run start # web app — open http://localhost:3000For Electron:
npm run electron:devSee DEV-BUILD.md for build instructions and troubleshooting.
Hardware Requirements
Only relevant for Local Whisper mode. Soniox only needs an internet connection.
macOS
| Config | RAM | Usable features | | ----------- | ------------------- | -------------------------------------------------------------- | | Minimum | 8 GB | Whisper tiny/base + Ollama gemma3:1b, no diarization | | Recommended | Apple Silicon 16 GB | Whisper medium/large-v3-turbo + Ollama gemma3:4b + diarization | | Optimal | Apple Silicon 32 GB | All features with large models, very smooth |
Apple Silicon recommended (M1+): unified memory allows CPU and GPU to share RAM — Whisper (Metal), Ollama (Metal), and diarization (MPS) are all hardware-accelerated, 4–6× faster than Intel at the same model size.
Windows
| Config | RAM | GPU | Usable features | | ----------- | ----- | --------------------------------- | ------------------------------------------------------- | | Minimum | 16 GB | None | Whisper tiny/base + small Ollama (slow), no diarization | | Recommended | 16 GB | NVIDIA 8 GB VRAM (RTX 3060+) | Whisper medium + Ollama gemma3:4b + diarization | | Optimal | 32 GB | NVIDIA 12 GB+ VRAM (RTX 4070 Ti+) | All features comfortably |
AMD GPUs are not supported. Without an NVIDIA GPU, diarization cannot run in real-time. Install CUDA Toolkit for GPU acceleration.
Linux
| Config | RAM | GPU | Usable features | | ----------- | ----- | --------------------------------- | ------------------------------------------------------- | | Minimum | 16 GB | None | Whisper tiny/base + small Ollama (slow), no diarization | | Recommended | 16 GB | NVIDIA 8 GB VRAM (RTX 3060+) | Whisper medium + Ollama gemma3:4b + diarization | | Optimal | 32 GB | NVIDIA 12 GB+ VRAM (RTX 4070 Ti+) | All features comfortably |
Requires PulseAudio or PipeWire (with PulseAudio compatibility) for audio device management. Most modern distros (Ubuntu 22.04+, Fedora, etc.) include one of these by default. Install ffmpeg via your package manager (sudo apt install ffmpeg).
Disk space (Local Whisper)
| Component | Size | | ----------------------------------- | ------------- | | Whisper base model | ~150 MB | | Whisper large-v3-turbo model | ~1.6 GB | | Ollama + gemma3:4b model | ~3.5 GB | | Python venv for diarization (torch) | ~4–6 GB | | pyannote diarization model | ~1 GB | | Total (full setup) | ~10–12 GB |
Soniox (Cloud)
Requires a free API key from soniox.com.
Configure in Settings → Engine → Soniox API Key, or via .env:
SONIOX_API_KEY=your_api_key_hereSoniox handles transcription, translation, and speaker diarization out of the box — no additional setup needed.
Local Whisper (Offline)
No API key required. All processing happens on-device.
1. Build whisper.cpp (one-time):
npm run setup:whisper2. Download a model:
cd node_modules/nodejs-whisper/cpp/whisper.cpp
bash models/download-ggml-model.sh base # or: tiny, small, medium, large-v3-turbo3. Select in app: Settings → Engine → Local Whisper → choose your model.
4. Translation (optional): configure Ollama or LibreTranslate in Settings → Engine.
Whisper Models
| Model | Size | RAM | Speed | Quality |
| ---------------- | ------- | ----- | --------- | --------- |
| tiny | ~75 MB | ~1 GB | Very fast | Low |
| base | ~150 MB | ~1 GB | Fast | Medium |
| small | ~500 MB | ~2 GB | Medium | Good |
| medium | ~1.5 GB | ~5 GB | Slow | High |
| large-v3-turbo | ~1.6 GB | ~4 GB | Medium | Very high |
| large | ~3 GB | ~8 GB | Slowest | Best |
Recommended: large-v3-turbo on Apple Silicon, base on low-end hardware.
Speaker Diarization (Local Whisper)
Identifies individual speakers using pyannote-audio. Requires Python 3.10–3.12 and a free HuggingFace token.
1. Install Python dependencies:
npm run setup:diarizeOr via the UI: Settings → Engine → Setup Diarization.
2. Create a HuggingFace READ token at huggingface.co/settings/tokens, then accept terms for:
3. Enter the token in Settings → Engine → HuggingFace Token.On first use, the app downloads ~1 GB of models. Subsequent starts are ready in ~10–15 seconds.
System Audio Capture
System audio capture (from Zoom, Meet, YouTube, etc.) works natively — no virtual audio drivers needed. Pre-built binaries are included in the repo.
- macOS: ScreenCaptureKit (requires Screen Recording permission, macOS 13+)
- Windows: WASAPI loopback (no special permissions needed)
- Linux: PulseAudio / PipeWire monitor sources (via ffmpeg)
To rebuild from source: npm run setup:audiocap (requires Swift on macOS, .NET 8 SDK for Windows cross-compile)
Documentation
| File | Contents | | ------------------------------------ | -------------------------------------------------- | | DEV-BUILD.md | Development setup, build commands, troubleshooting | | ARCHITECTURE.md | System architecture, data flow, component overview |
Tech Stack
| Layer | Technology | | ------------------- | ------------------------------------------------- | | Frontend | React 19, Vite, Tailwind CSS v4, Socket.IO Client | | Backend | Node.js, Express 5, Socket.IO | | Desktop | Electron | | Audio capture (mic) | ffmpeg (avfoundation / dshow / pulse) | | Audio capture (sys) | audiocap (macOS / Windows), ffmpeg+pulse (Linux) | | STT (cloud) | Soniox API | | STT (local) | nodejs-whisper (whisper.cpp) | | Speaker diarization | pyannote-audio 3.1 (Python) | | Translation (local) | Ollama / LibreTranslate | | Database | SQLite (better-sqlite3) |