ygelfand/echolocal
Turn an Echo Dot (2nd gen) into a fully local Home Assistant voice satellite, multi-room media player, lux sensor, Bluetooth proxy, and more. No Amazon, no cloud.
What it solves
EchoLocal transforms a 2nd-generation Amazon Echo Dot (biscuit, RS03QR) into a local, privacy-focused voice satellite for Home Assistant. It replaces Amazon's cloud services with a local Go-based implementation, allowing the device to function without relying on external servers.
How it works
The project consists of a daemon (echod) that runs directly on the Echo Dot hardware. This daemon manages the hardware, handles wake word detection, and implements an ESPHome native API server. This allows Home Assistant to discover and interact with the device as if it were a native ESPHome device. A companion CLI tool (echoctl) is used for installation and provisioning.
Who it’s for
Users who own a 2nd-generation Echo Dot and want to integrate it into a local Home Assistant setup for voice control, while maintaining privacy and removing Amazon's cloud dependency.
Highlights
- Local Wake Words: 100% on-device detection using openWakeWord and microWakeWord models, including "stop" detection.
- Home Assistant Integration: Acts as a native
media_player and voice satellite, supporting media playback with ducking and white-noise generation.
- Bluetooth Proxy: Forwards BLE advertisements to Home Assistant and acts as an iBeacon for device positioning systems.
- Hardware Utilization: Enables the use of the onboard LED ring (with custom animations) and the previously unused ambient light (Lux) sensor.
Related
- Project
kesai-labs/py123dpy123d is a Python library that unifies many autonomous‑driving datasets (Argoverse 2, nuScenes, Waymo, nuPlan, etc.) by converting them to a fast, zero‑copy Apache Arrow format and exposing a single API for cameras, lidar, maps, and labels. It includes CLI tools for downloading and converting data, Hydra‑based pipelines, and an interactive 3‑D viewer. Install via `pip install py123d` (with optional extras for each dataset) and use the provided commands to fetch, convert, and visualise data.
- Project
Qpu523/VPSCI-DatasetVPSCI is a large, high‑resolution dataset of vehicle‑pedestrian near‑miss interactions generated by a three‑stage reinforcement‑learning pipeline in the CARLA simulator. It contains ~198 k episodes across eight intersection scenarios, with CSV files logged at 20 Hz and supporting videos. Human studies show the generated interactions are indistinguishable from real‑world footage, making the dataset valuable for autonomous‑driving safety research.
- Project
NeLy-EPFL/flygymFlyGym is a Python library that provides a fast, physics‑based digital twin of an adult fruit fly. It simulates detailed biomechanics, compound‑eye vision, olfaction, leg adhesion, and offers a hierarchical brain‑spinal‑cord control interface, enabling researchers to develop and test embodied sensorimotor controllers.
- Project
lucidrains/q-transformerQ‑Transformer is a PyTorch library that implements the autoregressive Q‑function approach for scalable offline reinforcement learning, targeting robotic tasks that combine video observations and language instructions. It provides a vision‑transformer model, data‑collection agent, replay‑memory dataset, and Q‑learning trainer, all installable via `pip install q-transformer`.