Alpine Milk Chocolate

I just released Inflect v2, pushing complete TTS below 4M parameters.

Inflect-Nano-v2 fits every learned inference component, including waveform generation, into 3.96M parameters (15.97 MB FP32), making it one of the smallest publicly available complete TTS models. Inflect-Micro-v2 stays under 10M at 9.36M parameters (37.53 MB), using the extra capacity for clearer, more natural speech.

This was not about building a toy that technically produces audio. I wanted to see how much useful TTS could actually fit at this scale.

Nano stayed surprisingly close to Micro across the reported release metrics, but if you want to hear Inflect at its best, start with Micro:
owensong/Inflect-Micro-v2

Text goes in and 24 kHz speech comes out without a second learned model. Both models have PyTorch and ONNX releases: PyTorch runs on CPU or CUDA, while the browser demo runs ONNX locally through WebGPU with a WASM fallback. The Inflect code and weights are released under Apache 2.0.

Try Nano and Micro side by side:
owensong/Inflect-v2

Some people may remember the much rougher Inflect-Nano-v1 experiment. V2 was rebuilt and retrained to address its biggest weaknesses: metallic audio, unstable timing, weak prosody, and difficulty with unseen text. Nano-v2 is smaller while sounding substantially cleaner and more stable.

In an anonymous community blind test across nine voices, Micro and Nano placed second and third by normalized preference rate. Full results, samples, limitations, and inference instructions are on the model pages.

Inflect-Nano-v2:
owensong/Inflect-Nano-v2

I built Inflect independently with a limited training budget. If you test it, give it something difficult: unusual names, numbers, abbreviations, symbols, or awkward punctuation. Send me the exact text when something breaks. If you build something with it, I would genuinely love to see it.

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Compact Language Model Index

📊

Chance-normalized evaluation of compact language models.

reacted to ucr-max's post with 🔥 12 days ago

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We have released the first version of CompactLMIndex UniversalComputingResearch/clmi, a curated benchmark for smaller language models.

The index evaluates models across six benchmarks covering commonsense, science and world knowledge, and grammatical competence. Scores are normalized for random chance, so results are easier to compare across tasks with different baselines.
The composite gives 50% weight to commonsense, 25% to science and world knowledge, and 25% to grammatical competence. BLiMP is macro-averaged across its 12 linguistic categories. Every result includes 95% bootstrap intervals, and rank ranges reflect paired comparisons on matched evaluation items.

The goal is to compare compact models under one fixed, zero-shot evaluation protocol and make differences in architecture, training, and inference visible without treating parameter count as a substitute for quality.

If you have results for a model that fits the benchmark scope, please open a discussion on the Space.