Model Releases
You can now fine-tune my 3.96M-parameter TTS on your own voice or language
When I released Inflect v2 last week, I thought most people would ask whether a TTS model this small actually sounded decent. Instead, I kept getting two questions: “Can I train it on my own voice?” “
When I released Inflect v2 last week, I thought most people would ask whether a TTS model this small actually sounded decent. Instead, I kept getting two questions: “Can I train it on my own voice?” “Can I move it to another language?” At the time, my answer was basically: technically yes, but not with any public workflow I’d trust someone else to use. So I spent the weekend building one. Quick context if you missed the original release: Inflect-Nano-v2: 3,966,721 parameters, 15.97 MB FP32 Inflect-Micro-v2: 9,356,513 parameters, 37.53 MB FP32 Those are the learned text-to-waveform models, including the 24 kHz waveform decoder. eSpeak-ng handles phonemization separately and isn’t included in the parameter count. Micro is the better-sounding version. If you haven’t heard it yet, start here: https://huggingface.co/owensong/Inflect-Micro-v2 The new toolkit lets you bring your own single-speaker recordings and transcripts, warm-start Nano or Micro, resume training, inspect held-out output, and export the result to PyTorch or ONNX. The two awkward parts were rebuilding the training-only components that aren’t present in the release checkpoints and handling new phoneme inventories without resetting every embedding shared with English. The toolkit now handles both. I tested the full software path with Nano: a real CUDA training step, save and resume, strict PyTorch loading, and ONNX Runtime parity. Toolkit: https://github.com/owenawsong/Inflect/tree/main/finetune I haven’t trained a non-English model that I’d call release-quality yet. The pipeline works, but that doesn’t tell me whether a model this small will transfer cleanly to another language. Each run also creates one fixed voice for one configured language. This is supervised adaptation, not zero-shot cloning. I only have enough compute to investigate one language properly first, so I made a short form. Only the first question is required: https://tally.so/r/44RBvB The existing English weights haven’t changed. If you’ve adapted a small TTS model before, what failed first when you changed languages? That’s the part I want to measure before starting the first serious run. submitted by /u/b111ue [link] [comments]
Source: r/LocalLLaMA | 2026-07-27