fikrikarim/parlor
HTMLOn-device, real-time multimodal AI. Have natural voice and vision conversations with an AI that runs entirely on your machine. Powered by Gemma 4 E2B and Kokoro.
Parlor
On-device, real-time multimodal AI. Have natural voice and vision conversations with an AI that runs entirely on your machine.
Parlor uses Gemma 4 E2B for understanding speech and vision, and Kokoro for text-to-speech. You talk, show your camera, and it talks back, all locally.
https://github.com/user-attachments/assets/cb0ffb2e-f84f-48e7-872c-c5f7b5c6d51f
Research preview. This is an early experiment. Expect rough edges and bugs.
Why?
I'm self-hosting a totally free voice AI on my home server to help people learn speaking English. It has hundreds of monthly active users, and I've been thinking about how to keep it free while making it sustainable.
The obvious answer: run everything on-device, eliminating any server cost. Six months ago I needed an RTX 5090 to run just the voice models in real-time.
Google just released a super capable small model that I can run on my M3 Pro in real-time, with vision too! Sure you can't do agentic coding with this, but it is a game-changer for people learning a new language. Imagine a few years from now that people can run this locally on their phones. They can point their camera at objects and talk about them. And this model is multi-lingual, so people can always fallback to their native language if they want. This is essentially what OpenAI demoed a few years ago.
How it works
Browser (mic + camera)
│
│ WebSocket (audio PCM + JPEG frames)
▼
FastAPI server
├── Gemma 4 E2B via LiteRT-LM (GPU) → understands speech + vision
└── Kokoro TTS (MLX on Mac, ONNX on Linux) → speaks back
│
│ WebSocket (streamed audio chunks)
▼
Browser (playback + transcript)
- Voice Activity Detection in the browser (Silero VAD). Hands-free, no push-to-talk.
- Barge-in. Interrupt the AI mid-sentence by speaking.
- Sentence-level TTS streaming. Audio starts playing before the full response is generated.
Requirements
- Python 3.12+
- macOS with Apple Silicon, or Linux with a supported GPU
- ~3 GB free RAM for the model
Quick start
git clone https://github.com/fikrikarim/parlor.git
cd parlor
# Install uv if you don't have it
curl -LsSf https://astral.sh/uv/install.sh | sh
cd src
uv sync
uv run server.py
Open http://localhost:8000, grant camera and microphone access, and start talking.
Models are downloaded automatically on first run (~2.6 GB for Gemma 4 E2B, plus TTS models).
Configuration
| Variable | Default | Description |
|---|---|---|
MODEL_PATH |
auto-download from HuggingFace | Path to a local gemma-4-E2B-it.litertlm file |
PORT |
8000 |
Server port |
Performance (Apple M3 Pro)
| Stage | Time |
|---|---|
| Speech + vision understanding | ~1.8-2.2s |
| Response generation (~25 tokens) | ~0.3s |
| Text-to-speech (1-3 sentences) | ~0.3-0.7s |
| Total end-to-end | ~2.5-3.0s |
Decode speed: ~83 tokens/sec on GPU (Apple M3 Pro).
Project structure
src/
├── server.py # FastAPI WebSocket server + Gemma 4 inference
├── tts.py # Platform-aware TTS (MLX on Mac, ONNX on Linux)
├── index.html # Frontend UI (VAD, camera, audio playback)
├── pyproject.toml # Dependencies
└── benchmarks/
├── bench.py # End-to-end WebSocket benchmark
└── benchmark_tts.py # TTS backend comparison
Acknowledgments
- Gemma 4 by Google DeepMind
- LiteRT-LM by Google AI Edge
- Kokoro TTS by Hexgrad
- Silero VAD for browser voice activity detection