Pipi is a smartphone robot that can talk, remember things, take photos, and drive around when mounted on an Octobot.

• macOS/Apple Silicon or Linux x86_64 for local Qwen3-TTS speech synthesis.
• Linux uses Vulkan for native STT/TTS acceleration.
• The default local model set needs about 8-10 GB of unified memory at runtime.
• Node.js 22+, CMake, pkg-config, C/C++ build tools, and tar.
• Xcode command line tools, Xcode Metal Toolchain, and Opus are required for the macOS Qwen3-TTS worker. Rust is only needed for the optional Rust/MLX worker (QWEN3_TTS_WORKER=rust).

macOS native build prerequisites:

brew install cmake pkg-config opus
xcodebuild -downloadComponent MetalToolchain

Ubuntu 26.04 native build prerequisites:

sudo apt install -y build-essential cmake pkg-config git curl tar \
  mesa-vulkan-drivers vulkan-tools libvulkan-dev glslc libshaderc-dev spirv-tools

For AMD Strix Halo/Radeon 8060S, install ROCm 7.2.2 from AMD's noble repository if you want HIP/ROCm experiments. The default Linux STT build uses Vulkan.

Fast path after cloning:

./setup.sh
npm run dev

setup.sh installs npm dependencies, initializes submodules, and compiles native STT/TTS workers for the active platform.

Manual equivalent:

npm ci --ignore-scripts
git submodule update --init --recursive
npm run build:native
npm run dev

Open:

http://localhost:8010

For phone access, expose port 8010 over HTTPS, for example with ngrok.

Pipi runs local LLM, STT, and TTS models. Missing default models are downloaded automatically on startup.

• LLM default: Gemma 4 26B A4B MoE Q4 via llama.cpp.

  • Model: ggml-org/gemma-4-26B-A4B-it-GGUF
  • Downloaded into: ~/models/gemma-4-26b-a4b-it
  • Pipi also downloads a pinned llama.cpp release into ~/.cache/pibot/llama.cpp.
  • Linux downloads the pinned Vulkan-enabled llama.cpp release.
  • Override the llama.cpp server binary with LLAMA_CPP_BINARY_PATH=/path/to/llama-server.
  • Use LOCAL_LLM=gemma12b npm run dev for Gemma 4 12B IT Q4 from unsloth/gemma-4-12b-it-GGUF, downloaded into ~/models/gemma-4-12b-it.

• STT default: native parakeet.cpp GGUF worker with whisper.cpp GGML Silero VAD.

  • Build with npm run build:stt-parakeet-cpp.
  • Uses Metal on Apple platforms and Vulkan on Linux by default.
  • Prebuilt STT worker archives are published from the parakeet-cpp-stt-v* GitHub release workflow for Linux x64 Vulkan, macOS arm64 Metal, and Windows x64 Vulkan.
  • Model: mudler/parakeet-cpp-gguf/tdt-0.6b-v3-q8_0.gguf.
  • Downloaded into: ~/models/parakeet-cpp-gguf/tdt-0.6b-v3-q8_0.gguf.
  • VAD model: ggml-org/whisper-vad/ggml-silero-v6.2.0.bin.
  • Downloaded into: ~/models/whisper-vad/ggml-silero-v6.2.0.bin.
  • Override with PARAKEET_CPP_MODEL_PATH/PARAKEET_CPP_MODEL_FILE and SILERO_VAD_GGML_MODEL_PATH/SILERO_VAD_GGML_MODEL_FILE.

• TTS default: native C++/GGML Qwen3-TTS worker (QWEN3_TTS_WORKER=cpp).

  • Uses Metal on macOS and Vulkan on Linux/Windows.
  • Built with npm run build:tts-cpp (also part of npm run build:native and ./setup.sh).
  • Model: badlogicgames/qwen3-tts-0.6b-q8_0-gguf (Q8_0 GGUF + tokenizer).
  • Downloaded into: ~/models/qwen3-tts-0.6b-q8_0-gguf.
  • Override with QWEN3_TTS_CPP_WORKER_PATH / QWEN3_TTS_CPP_MODEL_PATH / QWEN3_TTS_CPP_MODEL_REPO.
  • Uses x-vector voice cloning.

• TTS alternative: Rust Qwen3-TTS 0.6B Base 6-bit MLX (QWEN3_TTS_WORKER=rust).

  • Better voice cloning (ICL), but requires Rust + Apple MLX; build with npm run build:tts-rust.
  • Model: mlx-community/Qwen3-TTS-12Hz-0.6B-Base-6bit, downloaded into ~/models/qwen3-tts-12hz-0.6b-base-6bit.

• Select the TTS engine with QWEN3_TTS_WORKER (cpp default, rust, python, or disabled).

./setup.sh              # install deps, initialize submodules, build native workers
npm run dev             # start the development server
npm run build:native    # build STT and TTS native workers
npm run build:stt-parakeet-cpp # build the native parakeet.cpp STT worker
npm run build:tts-cpp   # build only the native C++/Metal Qwen3-TTS worker (default)
npm run build:tts-rust  # build only the optional Rust/MLX Qwen3-TTS worker
npm run check           # format/lint/typecheck/build client
npm run bench:stt       # benchmark STT worker
npm run bench:tts       # benchmark TTS worker
npm run bench:llm       # benchmark local LLM server