Article URL: https://huggingface.co/owensong/Inflect-Micro-v2 Comments URL: https://news.ycombinator.com/item?id=49053375 Points: 37 # Comments: 3

Complete local text-to-waveform speech synthesis under 10M parameters. Fixed-voice English TTS with deterministic seeds, long-text handling, and CPU or CUDA inference. I built and funded Inflect v2 independently. If this release finds a real audience, I would like to continue the project with a broader v3, which might include things like more langauges, voices, and stability improvements. If the model is useful to you, leaving a like on Hugging Face genuinely helps more people discover it. Inflect v2 uses one public API across two sizes: Micro prioritizes quality below 10M parameters; Nano prioritizes footprint below 4M. These are held-out text generations, not reconstructions of training audio. Each transcript is shown exactly as passed to the public frontend. No single metric captures TTS quality. Inflect v2 reports human preference, predicted naturalness, multi-ASR intelligibility, complete footprint, and runtime separately rather than compressing them into one unverifiable score. The headline row always refers to Inflect-Micro-v2. Detailed competitor results and protocol boundaries are kept visible below. Comparison set. Results include KittenTTS Nano, Piper Low, and Supertonic 3, established compact or local TTS baselines with larger deployable weight footprints than both Inflect releases. Weight sizes are compared at package level, and no single metric is treated as proof of overall superiority. Inflect-Micro-v2 recorded a 66.2% preference rate (21 wins · 10 losses · 3 ties) in the final anonymous community study. Systems were hidden, left/right order was randomized, and ties count as half a win. This is descriptive community evidence, not formal MOS.