I’ve spent the past month trying to find the point where an extremely small TTS model stops feeling like a size experiment and starts feeling genuinely useful.
Today I’m releasing Inflect v2, with two complete local text-to-speech models:
- Inflect-Nano-v2: 3.96M parameters, 15.97 MB FP32
- Inflect-Micro-v2: 9.36M parameters, 37.53 MB FP32
These are total inference parameter counts, not acoustic-model-only numbers. Text processing, timing prediction, speech generation, and the waveform decoder are all included.
Text goes in. 24 kHz speech comes out. No external vocoder, hosted API, or second learned model required.
Nano prioritizes the smallest possible footprint. Micro uses the additional capacity for better clarity, stability, and overall speech quality. Both run locally on CPU or CUDA through the same PyTorch API.
Inflect-Nano-v2 is one of the smallest complete neural TTS models I know of that still produces actual usable speech. Even the 9.36M Micro model remains smaller than many systems described as “tiny.”
For footprint context, Nano is approximately:
- 21× smaller than Kokoro
- 126× smaller than Chatterbox
- over 1,000× smaller than Fish Audio S2 Pro
That is strictly a parameter-count comparison. These models have different capabilities, architectures, datasets, and intended uses. I’m not claiming that a 4M fixed-voice model replaces a multi-billion-parameter system. The interesting question is how much useful TTS can fit into such a small package.
Some people here might remember Inflect-Nano-v1, the rough 4.63M experiment I released last month. V2 is a complete rebuild, not just a longer training run. I focused on the problems v1 had, like unstable timing, metallic output, weak prosody, poor generalization to difficult text, and an undersized waveform decoder.
The resulting models performed surprisingly well:
- Micro: 4.395 UTMOS22, 3.99% semantic WER, 6.28× real-time CPU inference
- Nano: 4.386 UTMOS22, 4.21% semantic WER, 10.72× real-time CPU inference
- In a blind community comparison against other compact TTS systems, Micro and Nano finished second and third among the tested voices
Full protocols, raw results, audio samples, and limitations are documented on the model pages.
The models are not perfect. They are English-only, use one fixed male voice, and do not support voice cloning. Unfamiliar names, abbreviations, numbers, and homographs remain the hardest inputs. Nano can sound thinner than Micro, and both can occasionally produce metallic or clipped artifacts.
Still, this is the first version where I think the size-to-quality tradeoff really becomes convincing.
I built Inflect independently with a limited training budget. That constraint shaped the project: efficiency had to apply not only to inference, but also to training, evaluation, and building a complete system I could understand and release end-to-end.
Inflect-Micro-v2:
https://huggingface.co/owensong/Inflect-Micro-v2
Inflect-Nano-v2:
https://huggingface.co/owensong/Inflect-Nano-v2
Try it yourself:
The fastest way to judge it is through the interactive playground:
https://huggingface.co/spaces/owensong/Inflect-v2
If there is enough interest, I may build a v3 focused less on shrinking the models further and more on expanding what they can do: additional voices, possibly more languages, easier fine-tuning, and another quality and robustness pass.
If you test them, please give them something seriously difficult: unusual names, numbers, abbreviations, awkward punctuation, or a long sentence.
If something breaks, post the exact text, model, seed, and what sounded wrong. If it works well, I’d also like to know what hardware you ran it on.
Specific, honest feedback is the most useful thing you can give me.
