About Technical Rhymer

Rhyme lives in pronunciation, and English spelling is a famously unreliable witness to it: colonel rhymes with kernel, through with threw, and cough, though, and tough agree on almost nothing. Letter-based rhyme tools inherit all of that noise. Technical Rhymer instead searches over phonemes — the actual sounds — written in ARPABET, the notation used by the CMU Pronouncing Dictionary: orange is AO1 R AH0 N JH.

Digits on vowels mark stress: 1 primary, 2 secondary, 0 unstressed. A perfect rhyme is "same sounds from the last stressed vowel to the end", which is exactly a suffix match on the phoneme string — that one observation is the whole design. Look a word up, take the tail you care about, and search it.

Every entry is indexed by its phoneme string, and a search scans all of them (a linear pass over ~145k pronunciations takes tens of milliseconds — no clever index needed). The match modes anchor the fragment: Ends with is the rhyme case, Starts with finds alliterative twins, Anywhere and Exact do what they say. Two operators build richer patterns: * requires parts in order with anything between (AO R * JH), and | requires parts in any order. Listing a part twice (B | B) demands it occur twice.

Stress is ignored by default: requiring stress digits to match eliminates most slant rhymes people actually want, so exact-stress matching is the opt-in, not the default. Double rhymes — words where the searched segment occurs twice or more, like rat-a-tat for AE T — get split into their own section with a ×N badge, since a repeated rhyme is usually the better find.

Fuzzy matching (opt-in) treats like-sounding consonants as interchangeable for slant rhymes. The classes are grouped by manner and voicing of articulation — so T↔K match but T↔D (a voicing change) do not:

You never have to type ARPABET cold: looked-up pronunciations drop into the search box with a click, results highlight exactly which sounds matched, and a tap-to-build phoneme keyboard under the search box shows every sound with an example word.

Vowels are never fuzzed — vowel identity is most of what makes a rhyme feel like one.

Those are the defaults, not dogma: the gear next to the Fuzzy toggle opens an editor where you can regroup the consonants however your ear likes — merge voicings, split the sibilants, set a sound loose so it only matches itself. Custom groups persist in your browser, and Reset to defaults brings back the table above.

A rhyme search for a short tail can return thousands of words, and most of them are words nobody would use. Results are therefore ranked by commonality — the wordfreq project's Zipf scale, where ~7 is the and ~1 is deep obscurity. The thin bar under each result is that score. Usable rhymes surface first; the exotic tail is still there at the bottom. Sorting by syllable count or alphabetically is one click away.

Rhyming is usually rhyming about something. The optional filter keeps only results related to a word you name — searching EH ZH ER while writing about pirates keeps treasure. It has two engines:

• AI mode — the top candidates are sent to a small relay we run, which asks Claude which of them are related / synonyms / opposites. No key, no account, nothing to configure — the API key lives on the relay, never in your browser. Requests are rate-limited and budget-capped (it's a free site). This is the primary engine because real-world association ("pirates" → "treasure") is exactly what language models are good at and word vectors are mediocre at.
• Offline mode — if the AI is busy or over budget, "related" falls back to GloVe word vectors (50-dimensional, int8-quantized to keep the file small) with a cosine-similarity cutoff, and "synonym" / "opposite" use WordNet. The split exists because embeddings famously place antonyms close together (hot and cold appear in identical contexts), so cosine similarity can't tell likeness from opposition — a curated lexicon can.

Three layers, each honestly labeled in the UI:

• CMU Pronouncing Dictionary — ~135k standard-English words with curated pronunciations. The backbone.
• Urban Dictionary top 10,000 (gold UD badge) — the highest-net-voted slang terms. Pronunciations for terms CMUdict lacks are auto-generated with a grapheme-to-phoneme model, so they're approximate. UD content is user-submitted and can be crude or NSFW; it's included as-is, with vote scores and links back to the source for attribution.
• New words of the 2020s (teal ’2x badge) — the 2,000 top-rated terms whose first meaningful Urban Dictionary definition appeared in 2020 or later and that don't exist in CMUdict. "First meaningful" matters: a raw first-definition-date rule would exclude rizz because someone defined it as a nickname in 2015 (scored −50, against 626 for the real 2023 definition), so a term qualifies as long as any pre-2020 definitions are marginal next to its 2020s peak. Pronunciations are approximate: grapheme-to-phoneme for pronounceable terms, letter names for vowel-less acronyms (tds → T IY1 D IY1 EH1 S). The source dataset ends in November 2023, so the newest of the new isn't here yet.

The site is static files — no database, no framework, no build step at runtime. All dictionary data (~15 MB across six bundles) is baked into plain JavaScript files loaded with <script> tags, which means search works offline and even opened directly from disk over file://. The heavy word-vector and WordNet bundles are lazy-loaded only if the offline sense filter is actually used. The single exception to "no server" is the word-sense relay described above — a fixed-function endpoint that exists purely so the AI key never ships to your browser.

Data files are regenerated by small Python scripts in build/ (CMUdict parsing, wordfreq scores, GloVe quantization, WordNet extraction, Urban Dictionary ranking and grapheme-to-phoneme). The runtime never depends on them. Pinned result tabs and your preferences (fuzzy matching, stress handling, sort, match mode, panels) persist in localStorage only — no cookies — and stick across visits. Searches also sync into the address bar (?q=AH+N+JH), so any query is shareable as a plain link; opening someone else's link never overwrites your own preferences.

No accounts, no analytics, no cookies, no tracking. Nothing you type leaves your machine, with one explicit exception: when you use the word-sense filter's AI mode, the filter word and the candidate rhyme list (just words — never your searches or anything else) are sent to our relay and on to Anthropic to be judged. They aren't tied to any identity, and there's nothing else to send — the site has no accounts to associate them with.

• CMU Pronouncing Dictionary — pronunciations (ARPABET).
• wordfreq — Zipf commonality scores.
• GloVe — offline word vectors (glove-wiki-gigaword-50).
• WordNet — synonyms and antonyms.
• Urban Dictionary — slang terms and vote scores (© their authors), via the georgiyozhegov/urbandictionary-raw dataset.
• g2p_en — grapheme-to-phoneme for words CMUdict doesn't know.
• Lucide — icons.