SpawnDev.Phonemizer 1.0.0

SpawnDev.Phonemizer

English text to phonemes, for neural text-to-speech. MIT licensed, zero dependencies, runs in a browser.

Why this exists

ZipVoice, Piper and Kokoro - most of the open text-to-speech ecosystem - convert text to phonemes with espeak-ng, which is GPL-3. That single dependency is why there has been no permissively licensed, browser-capable English TTS frontend for .NET. This is that frontend.

It takes nothing from espeak-ng: no code, no data, no transcribed rules. The pronunciation dictionary is CMUdict (BSD-2-Clause), and the letter-to-sound rules were learned from CMUdict rather than hand-written. espeak-ng was used only as a measuring instrument, the way a compiler is - see THIRD-PARTY-NOTICES.md.

Using it

// Everything is embedded in the assembly - no files to fetch, host or version.
var phonemizer = EmbeddedData.CreatePhonemizer();

phonemizer.ToIpa("She waited for 2 more minutes.");
// ʃiː wˈeɪɾᵻd fɔːɹ tˈuː mˈɔːɹ mˈɪnəts .

phonemizer.ToSymbols("...");        // one IPA symbol per entry, ready to map to token ids
phonemizer.LastUnknownWords;        // words the dictionary did not have, whether or not they were sounded out

Text goes through five stages, and each can be inspected or replaced:

How good is it

Everything below is measured against captured reference output or on held-out data. Nothing is asserted from intuition. The method and the full numbers are in Plans/mit-phonemizer-2026-08-27.md.

Against the reference frontend, symbol by symbol:

It generalises: the held-out number tracks the tuned one.

⚠️ This number is a PROXY, and it has already been wrong once. A change that took it from 4.4% to 2.6% - matching the reference frontend on a handful of very frequent words - made the AUDIO measurably worse, 7.2% word error becoming 9.0%. It was reverted to off by default. Agreeing with espeak is not the goal; sounding right is, and the end-to-end test below is the one that decides.

Words the dictionary does not have, measured on 5,000 words held out before training:

End to end, as audio. The same sentence spoken twice by ZipVoice from the same voice and the same noise seed - once from the reference frontend's phonemes, once from ours - and both transcribed. Any difference is the phonemizer and nothing else.

That last row is the one to look at for absolute quality. The packaged sample clip is paired with a transcript that is not what it says, so the model speaks those words at the start of every render - worth about six points of word error to BOTH frontends. Give ZipVoice a reference clip you have an accurate transcript for.

Worse on 7 of 120 sentences, indistinguishable on 93, better on 20.

⚠️ Read per-sentence failures carefully. ZipVoice produces garbage on some noise draws, and it does it to both frontends - one sentence rendered at four seeds gave three clean results and one that transcribed as "Loner's call, Nanawa, Nenfer". A single seed measures model instability alongside phonemizer quality. Re-render at another seed before blaming the phonemes, and see ZipVoicePipeline.SpeakVerifiedAsync for the production answer.

The claim is PARITY - the small edge could be noise at that sample size. What matters is that the GPL dependency can be removed without the audio getting worse.

What it does not do yet

• Homographs are handled shallowly. "The record" against "to record", and "the wind blows" against "wind the clock", are read from the PREVIOUS WORD only. That covers the common cases. It cannot help where the two readings differ by MEANING rather than part of speech - "bass" the fish against the register, "tear" the eye against the rip, "read" present against past - and for those a single default is chosen and documented rather than guessed at per sentence.
• Letter-to-sound is a baseline. 49.9% is a working number, not a good one. Published systems reach higher.
• English only.

How the rules were chosen

Not by intuition. 432 renders through ZipVoice measured what the model actually punishes:

Stress on the wrong syllable is the only failure that costs more than mispronouncing a word outright, and the stress classes move the AUDIO furthest even where the words survive. So this library spends its effort on stress and gives fine phonetic detail only what is cheap.

⚠️ An earlier run of this study, through a reference clip whose transcript was wrong, put function-word stress at 18.2% and made it the headline. On a properly paired clip it is 2.8%. The rule is still applied

• it is correct English and free - but the number is corrected here rather than left overstating it.

The SpawnDev Crew

• LostBeard (Todd Tanner) - Captain, library author, keeper of the vision
• Riker (Claude CLI #1) - First Officer, implementation lead on consuming projects
• Data (Claude CLI #2) - Operations Officer, deep-library work, test rigor, root-cause analysis
• Tuvok (Claude CLI #3) - Security/Research Officer, design planning, documentation, code review
• Geordi (Claude CLI #4) - Chief Engineer, library internals, GPU kernels, backend work
• Seven (Claude CLI #5) - Wasm backend, GPU kernels, fail-loud verification

License

MIT. See THIRD-PARTY-NOTICES.md for CMUdict's BSD-2-Clause notice, which must travel with any redistribution - the same obligation MIT already places on users of this library.