Nonsense verse with Python and machine learning
summary
Claims a Python library called Pincelate converts between spelling and sound, using the CMU Pronouncing Dictionary for training and a seq2seq neural model to produce phoneme and grapheme mappings. It produces phoneme feature vectors, encodes words as 256‑dim states, and supports interpolation, noise injection and constrained decoding to generate plausible neologisms, rhyme and meter searches, haiku and sonnet manipulations. The model runs and is useful for creative experiments but is imperfect (phoneme→spelling hits around ~80% and some outputs are noisy).
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chapters (37)
- 0:00Start
- 0:37Introduction
- 1:48Genre of Nonsense Verse
- 4:57Sound Poetry
- 15:32Neural Networks to the Rescue
- 17:18Internal Phonetic Representation of a Word
- 18:34How Sequence Sequence Models Work
- 24:11Adjust the Phonetic Probabilities
- 25:06Bilabial Features
- 32:39Jupiter Notebooks
- 34:53Tensorflow Installation
- 38:13Cmu Pronouncing Dictionary
- 40:20Jupiter Notebook
- 41:37How Many Vowels Are There in English
- 48:11Lexical Stress
- 52:45Most Common Sounds
- 53:40Search
- 57:40Rewriting Exercise
- 1:01:30Meter
- 1:01:44Syllable Count
- 1:04:51Word Stresses
- 1:05:44Stress Pattern
- 1:07:31Search Stresses Function
- 1:11:16Metrical Feet
- 1:14:47Output
- 1:15:31Rhymes
- 1:19:26Rewriting with Rhymes
- 1:30:28Phoneme Frequency and Analysis
- 1:34:03Phoneme Count
- 1:36:33Baseline Corpus
- 1:37:25Most Common Phonemes
- 1:38:40Invent New Nonsense Words
- 1:41:24Phoneme Frequencies
- 1:47:37Phoneme Features
- 1:56:28Phoneme Features Function
- 2:02:33Random Uniform
- 2:07:08Image Interpolation
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