Runs text-to-speech with instant voice cloning fully on-device, from phones to GPUs. Built on small LLM backbones (120M-360M params) plus a 50Hz neural codec; clones a voice from ~3 seconds of audio across English, Spanish, German, and French.
Turns clinical text into structured, de-identified clinical signals—entity extraction and PII de-identification—that run entirely on local hardware. Provides 1,000+ specialized medical NER models, multilingual support, Apple MLX acceleration, and Apache‑2.0 licensing.
Detects motion from Wi‑Fi channel state information (CSI) on cheap ESP32 boards and integrates natively with Home Assistant; offers an optional on‑device ML detector that requires no calibration.
Contains training, evaluation, and deployment code plus checkpoints for humanoid whole-body controllers (Decoupled WBC and GEAR‑SONIC). Includes C++ inference, VR teleoperation, data pipelines (Bones‑SEED) and Hugging Face checkpoints for research-to-robot workflows.
Delivers multilingual, on-device text-to-speech via ONNX Runtime with prebuilt ONNX assets and cross-platform SDKs (Python, Node, mobile); targets low-latency, privacy-preserving TTS with ready demos and 31-language support in v3.
Combines a vector store, Cypher-style graph queries, and on-device LLM inference in one Rust engine, with a graph neural network that reranks results and adapts to query patterns in under a millisecond. Services ship as self-contained .rvf containers.
Runs text-to-video, image-to-video, text-to-image, and image editing inference with acceleration, offloading, quantization, and distributed execution for large visual generation models.
Generates real-time, infinite-length portrait video from one reference image on a 12GB GPU. Combines implicit facial signals and 3D keypoints with step-distilled diffusion and autoregressive micro-chunk streaming for low-latency live use.
Converts images (and other conditions) into high-fidelity, fully textured 3D assets using a 4B-parameter generative model and a field‑free sparse voxel format (O‑Voxel). Handles arbitrary topology, PBR materials, and near real-time mesh/voxel conversions; requires Linux and an NVIDIA GPU with >=24GB memory.
Provides a plug-and-play inference engine that lets language models programmatically inspect, decompose, and recursively call themselves to handle very long contexts; supports local and cloud REPL sandboxes, multiple LLM backends, and trajectory logging/visualization.
Provides a DiT-based audio–video foundation model plus an official Python inference and LoRA trainer. Ships multiple production-ready pipelines (text/image/audio→video), checkpoints, and performance optimizations (FP8, distilled pipelines) for high-fidelity synchronized audio–video generation.
Enables parallel speculative decoding by using a lightweight block-diffusion draft model to produce multi-token drafts for faster, high-quality generation. Integrates with vLLM, SGLang and Transformers backends and ships draft models on Hugging Face.