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Syllable Counter & Poetry Analyzer

MathText

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INPUT

Auto Process

OUTPUT

Client Side

Annotated text with per-word syllable counts will appear here.

Statistics

Metric	Value
Total Syllables	0
Total Words	0
Total Lines	0
Average Syllables / Line	0
Average Syllables / Word	0
Stressed Syllables (est.)	0

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Guide

Syllable Counter & Poetry Analyzer

Paste any text, lyric, or poem and instantly see how many syllables each word and line contains. The tool also validates classic forms — haiku (5-7-5), tanka (5-7-5-7-7), and limerick (8-8-5-5-8) — and can highlight estimated stressed syllables so you can audit the meter of your verse.

Unlike a chat-based assistant, this counter runs a deterministic algorithm in your browser using a phonetic dictionary plus fallback rules. The result is consistent, reproducible counts for songwriting, scansion practice, classroom exercises, and poetic-form revisions.

How to Use the Syllable Counter

Paste or type your text into the input area, or use one of the example buttons (haiku, sonnet excerpt, song lyrics).
Select an analysis mode. Standard shows per-word and per-line syllable counts. Haiku, Tanka, and Limerick additionally validate each line against the expected pattern.
Optionally enable Highlight stressed syllables to mark the primary stress in multi-syllable content words.
Read the annotations: every word is suffixed with a small superscript showing its syllable count, and each line ends with a pill showing the line total. Pills turn green when a line matches the target pattern and red when it does not.
Check the Statistics table below for totals, averages, and stressed-syllable estimates across the whole passage.

Features

Per-word syllable counts — every word is annotated with a superscript number.
Per-line totals — pill badges on each line so you can scan meter at a glance.
Form validation — built-in checks for haiku, tanka, and limerick patterns.
Stress highlighting — optional first-stress detection for content words.
Whole-passage statistics — totals, averages per line and per word, plus an estimated stressed-syllable count.
Deterministic algorithm — uses the syllable library, not an AI guess, for repeatable results.
Runs entirely in your browser — no text is uploaded to a server.

Use Cases

Poets auditing meter, scansion, and traditional forms.
Songwriters and lyricists matching syllables to melody and hook length.
Students and teachers practising poetry analysis, prosody, and language arts.
ESL learners comparing pronunciation against text shape.
Copywriters tightening headlines and taglines to a specific rhythm.

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 FAQ

What exactly is a syllable in English?

A syllable is a single uninterrupted unit of speech sound, typically built around a vowel nucleus. Linguists usually count syllables by identifying vowel groups in a word and accounting for silent letters, diphthongs and pronunciation rules. Different dictionaries and dialects can disagree on edge cases, which is why algorithmic counters rely on pronunciation lookup tables such as the CMU Pronouncing Dictionary supplemented by phonotactic fallback rules.
What is the 5-7-5 haiku rule and is it universal?

The 5-7-5 pattern is the most common English convention for haiku, referring to syllables per line. In Japanese, the original meter is based on mora (sound units) rather than syllables, so authentic haiku do not always map cleanly onto the English syllable model. Many modern English-language poets treat 5-7-5 as a stylistic guideline rather than a strict requirement.
How does meter and stress work in English poetry?

English meter is built from patterns of stressed and unstressed syllables. A foot like an iamb has one unstressed followed by one stressed syllable, while a trochee reverses that order. Iambic pentameter, for example, repeats the iamb five times per line. Function words such as articles and prepositions are usually unstressed, while content words like nouns and verbs typically carry the stress.
Why are AI assistants unreliable for counting syllables?

Large language models predict text token by token rather than analyse pronunciation. Tokens do not align with syllables, so the model has to guess based on word shape, which leads to inconsistent results, especially for unusual words, proper nouns, contractions and song lyrics. Deterministic algorithms built on phonetic dictionaries give reproducible counts that humans can verify.