News · 2026-07-25

A complete text-to-speech system now fits in 9.4 million parameters

A complete English text-to-speech system now fits in 9,356,513 parameters. Inflect-Micro-v2, released on Hugging Face, includes timing, speech generation and an integrated waveform decoder in that budget, and produces 24 kHz mono audio entirely locally - no external vocoder, no API call, no second learned model doing the hard part off-screen. The whole voice is roughly the size of a photo.

Key facts

• 9,356,513 deployable parameters for the full stack, per the model card.
• Output is 24 kHz mono, generated locally with an integrated decoder.
• One fixed synthetic English male voice - not multilingual, not multi-speaker, no voice cloning.
• Released by its author on r/LocalLLaMA at 02:17 on July 25, 2026.

Why the "complete" part is the claim that matters

Parameter counts for speech models are routinely misleading, because the published number often covers only the part that turns text into an intermediate representation. The vocoder - the component that turns that representation into an actual waveform - is frequently a separate model, sometimes larger than the one being advertised. A "5 million parameter TTS model" that requires a 50 million parameter vocoder is a 55 million parameter system.

Inflect-Micro-v2's number includes the decoder. That is the interesting engineering claim, and it is checkable: the ONNX release splits the learned path into duration and decode graphs, with the phonemization frontend remaining ordinary CPU code rather than a neural network. Our new explainer on how neural text-to-speech works walks through why that split exists and why the vocoder is usually where the parameters hide.

The architecture is from the VITS family: English phonemization, duration and alignment modeling, stochastic latent generation, normalizing flows, and an integrated waveform decoder trained end to end. Nothing in that list is new. Getting all of it under ten million parameters and keeping it intelligible is.

What it gives up

Everything except one voice. There is one fixed synthetic English male speaker. The author confirms that changing the speaker means training a replacement checkpoint on paired single-speaker English data - this is not few-shot cloning, and no amount of reference audio will make it someone else.

The sound quality verdict from the author's own evaluation notes is refreshingly unspun: flatter prosody than larger systems, instability on unfamiliar text, and context-sensitive handling of names and numbers. The supplied samples deliberately cover the hard cases - punctuation, numbers, names, technical text. The honest description is impressively intelligible compact narration, not human-equivalent speech.

That tradeoff is exactly the point. For an embedded device, a screen reader, a kiosk, a game NPC or an offline accessibility tool, one clear voice that runs anywhere beats an expressive one that needs a GPU or a network.

The other end of the same trend

The floor is dropping from two directions at once. On the same day, The Information reported that Apple has been in talks with PrismML, a Khosla-backed startup whose published technique attacks size from the numerical end rather than the architectural one.

PrismML's Bonsai 27B uses end-to-end low-bit weights - 1-bit binary or 1.58-bit ternary across the language-model path, with a compact vision tower alongside. The company says its roughly 27.8-billion-parameter Qwen-derived model fits in about 4 GB in the binary configuration, and reports iPhone-class operation. Weights are on Hugging Face and the technique is described in PrismML's own announcement. This is quantization pushed to its logical endpoint: not compressing a trained model afterward, but training in near-binary representation from the start.

Two systems, opposite strategies. One shrinks scope until a complete stack fits in ten million parameters. The other keeps the scope and shrinks the numbers until 27.8 billion parameters fit in a phone. Both land in the same place, and it is the same direction we tracked when three separate tricks dropped the local hardware floor earlier this week.

The honest caveat

The Apple report is paywalled and single-sourced, and neither Apple nor PrismML has announced a deal - the accurate phrasing is that Apple was reported to be evaluating PrismML, not partnering with or acquiring it. PrismML's performance figures are vendor-reported and not independently replicated. And Inflect-Micro-v2's reception so far is enthusiastic users confirming it downloads and runs, which is useful but is not independent expert evaluation of its speech quality.

Primary source, verified: read the paper →

Ground Truth. (2026, July 25). A complete text-to-speech system now fits in 9.4 million parameters. Ground Truth. https://groundtruth.day/news/a-complete-voice-model-in-under-10-million-parameters.html

@misc{groundtruth:a-complete-voice-model-in-under-10-million-parameters,
  title  = {A complete text-to-speech system now fits in 9.4 million parameters},
  author = {{Ground Truth}},
  year   = {2026},
  month  = {jul},
  url    = {https://groundtruth.day/news/a-complete-voice-model-in-under-10-million-parameters.html}
}

Topics: text-to-speech · local-inference · efficiency · open-weights · on-device