Inflect-Nano-v2

Inflect-Nano-v2 Complete local text-to-waveform speech synthesis under 4M parameters. Fixed-voice English TTS with deterministic seeds, long-text handling, and CPU or CUDA inference. **A note from Owen** I built and funded Inflect v2 independently, spending more than US$500 on training and evaluation. If this release finds a real audience, I would like to continue the project with a broader v3. If the model is useful to you, leaving a like on Hugging Face genuinely helps more people discover it. 3,966,721 deployable parameters · 15.97 MB FP32 · 24 kHz mono output Inflect v2 uses one public API across two sizes: **Micro prioritizes quality below 10M parameters; Nano prioritizes footprint below 4M.** Explore this model card | Start here | Technical detail | | --- | --- | | Listen | Architecture | | Evaluation | Controls and long text | | Choose Micro or Nano | Data and adaptation | | Run locally | Exports and quantization | | Package map | Evaluation and raw protocol | | Limitations | Deployment guide | Listen These are held-out text generations, not reconstructions of training audio. Each transcript is shown exactly as passed to the public frontend. | Test | Exact transcript | Generated audio | | --- | --- | --- | | **Conversational** | It wasn't until later that I realized what had actually happened. | | | **Punctuation** | First, close the window; second, turn off the lamp; finally, lock the door. | | | **Numbers** | The package weighs twelve point six kilograms and arrived on July twenty-first. | | | **Names and places** | Gwendolyn photographed the eucalyptus trees outside Ljubljana. | | | **Technical** | The system runs on three core components that all have to stay in sync. | | Evaluation 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. | Community preference ↑ | UTM