# The smallest neural text-to-speech family

> Source: <https://tts.ampixa.com/sanoTTS/>
> Published: 2026-11-03 08:52:29+00:00

The smallest neural text-to-speech family we know of — and it plays right here, live, in your browser.

a full neural stack — duration → acoustic → decoder — quantized to int8, phonemizer and all. 28 voices, 16 languages, all spoken on this page.

Type anything. The text is phonemized by **espeak-ng compiled to
  WebAssembly** (the same 275 KB port that runs on the chip; Chinese uses a
  **448 KB pinyin lexicon** instead) and spoken by that voice's own neural stack
  — all client-side, no server. Pick a voice:

| Phonemes | — | 
| Audio produced | — | 
| Time (this CPU) | — | 
| Speed on this machine | — | 

The speed shown is **this browser on your CPU** —
  the on-device English voice runs at 0.22× real time on an ESP32-S3. Every voice in the
  picker synthesizes your typed text live, right here, with no server involved. Where a language
  offers two sizes, the chips under its mascot switch between them.
  *heart* and *heart-nano* take a different route from text to sound than the other
  voices (a 100-band mel interface into a noise-shaping decoder), so they load their own small
  runtime; *heart-nano* runs the int8 arithmetic of the microcontroller build unchanged.

Measured with our no-reference harness (SCOREQ / UTMOS / DNSMOS-SIG) on the same 24-sentence diverse set, run identically across every model below.

**Size comparison (parameters)**

Kokoro is 36× larger than our largest voice — and 279× larger than our smallest.

Shipped-file sizes: sanoTTS amy 2.8 MB fp16 and TinyTTS 3.5 MB fp16, both verified from the released files; Kokoro's ~330 MB fp32 is its widely cited public figure.

**Three metrics, head to head**

|  | sanoTTS amy1.46M params | sanoTTS heart2.27M params | TinyTTS1.62M params | Inflect Nano4.63M params | 
|---|---|---|---|---|
| SCOREQ | 4.13 | 3.48 | 3.94 | 3.81 | 
| UTMOS | 4.10 | 3.38 | 3.65 | 3.65 | 
| DNSMOS-SIG | 3.61 | 3.51 | 3.62 | 3.58 | 

Same 24-sentence set, three independent no-reference metrics; best value per row in
  crimson. sanoTTS amy leads on SCOREQ and UTMOS; on DNSMOS-SIG TinyTTS edges us by 0.01 — no single
  metric tells the whole story. *heart* is a different voice and recipe (24 kHz, noise-shaping
  decoder) scored on the identical 24 sentences; *heart-nano*, its 294k sibling, scores
  2.29 / 2.45 / 3.35 on the same three metrics.

**The size-quality frontier we traced**

Same voice (amy), same duration/acoustic recipe — only decoder size changes. Quality lives in the decoder: doubling it from 1.09M to 1.84M params moves SCOREQ from 3.70 to 4.16.

**Every language we ship, and what we know about it**

**WER** is Whisper word error rate over 16 held-out Tatoeba sentences per
  language — conversational text, deliberately a different domain from the corpus these
  voices were distilled on. The **sample** buttons synthesize that language on this page, right
  now, through the same runtime as the picker above. Where a language ships in two sizes, both are
  playable. Chinese is the one exception: written Chinese has no spaces, so word error rate means
  nothing there and the figure is **character** error rate over the same 16 Tatoeba sentences,
  with traditional and simplified folded together. On held-out FLORES that voice scores 0.199
  against its teacher’s 0.188 — the voice it replaced scored 0.468.

| Language | WER | Sample | 
|---|---|---|
| Portuguese 🇧🇷 | 0.038 |  | 
| Italian 🇮🇹 | 0.080 |  | 
| German 🇩🇪 | 0.099512k · 0.145 |  | 
| Russian 🇷🇺 | 0.120 |  | 
| Spanish 🇪🇸 | 0.147 |  | 
| French 🇫🇷 | 0.221 |  | 
| Indonesian 🇮🇩 | 0.256 |  | 
| Arabic 🇯🇴 | 0.274 |  | 
| Turkish 🇹🇷 | 0.303 |  | 
| Czech 🇨🇿 | 0.351 |  | 
| Polish 🇵🇱 | — |  | 
| Romanian 🇷🇴 | 0.392 |  | 
| Vietnamese 🇻🇳 | 0.468 |  | 
| English 🇺🇸 | — |  | 
| Nepali 🇳🇵 | — |  | 
| Hindi 🇮🇳 | — |  | 
| Chinese 🇨🇳 | 0.024CER, not WER |  | 

Lower is better. Press play to judge the rest.

**What these numbers are, and are not.** WER is measured with Whisper over 16
  held-out Tatoeba sentences per language — directional, not precise. It says the words come
  through; it says nothing about whether a voice sounds good. No listening test has been run on any
  of the ten languages added on 2026-09-08, and none of them has a SCOREQ. Czech, Romanian and
  French got *worse* on unseen text than on the corpus they were trained on, so they are the
  ones to press first. Indonesian and Vietnamese are the espeak arm of a paired A/B — the
  phoneme path this page actually runs. Everything above is reproducible from
  `experiments/evidence/ood-tatoeba-20260908.json`,
  `idvi-espeak-free-ab-20260904.json` and
  `heart-diverse24-remeasure-20260904.json`.

Open source under **GPL-3.0** — builds on
  [piper](https://github.com/OHF-Voice/piper1-gpl) and
  [espeak-ng](https://github.com/espeak-ng/espeak-ng). Distill your own tiny voice:
  [github.com/Ampixa/sanoTTS](https://github.com/Ampixa/sanoTTS).

© 2026 Ampixa