Open models are enabling real-time voice cloning, wake-word detection, speech recognition, avatar generation, and visual search on local hardware. CPU-only and consumer GPU execution are lowering the cost of interactive on-premise assistants.
Kitten-TTS 2 brings 1.58-bit ternary quantization to a 1.7 billion parameter model that runs in real time on CPU. Self-hosting becomes realistic without GPUs, shifting TCO from VRAM to software execution quality. For organizations under data sovereig...
A 305-million-parameter speech encoder, frozen or augmented with low-rank adapters, takes on Saudi and Gulf Arabic speech recognition. The frozen system is statistically comparable to Cohere Transcribe Arabic on CER and 3.4 times faster on CPU; the a...
KittenML has released code and weights for Kitten-TTS 2, a TTS model with 1.58-bit ternary quantization and 1.7 billion parameters. It runs in real time on CPU, performs zero-shot voice cloning from under five seconds of audio, supports more than 20 ...
An Apache-2.0 open source project offers an on-device voice front-end for local assistants: five models for text-based wake word, voiceprint, voice separation and personal VAD. The Rust engine runs in the browser, Python and Android; models take 7.5 ...
A local test with EmbeddingGemma2 shows semantic image search on a consumer GPU, indexing 3,003 screenshots in 510 seconds with a 330 MB Q8 model and 768-dimensional vectors. But retrieval quality depends heavily on query wording and corpus character...
The open source project Openspline adds an avatar layer to existing voice AI pipelines: from an image, it generates a talking face with synchronized lips, executed locally on a GPU. The lite model exceeds 90 FPS on an NVIDIA RTX 4090 and integrates w...
