Build poems from text sources.

• Fetch Trump tweet data
• Generate vocabulary from Trump tweets
• Build baseline model to count syllables in English words
• Optimise syllable counter
• Use model to build Haikus from Trump tweets
• Build progressive web app that generates poems
• Enable working with live tweets
• Enable working with other text sources

A large part of this project was to develop a model that counts syllables in English words.

The syllable counter is trained on a (slightly modified version of) the Gutenberg syllable corpus, consisting of ~170,000 English words split into syllables. The process_gutsyls notebook converts these into a format which is more convenient for our purposes. The raw dataset can be freely downloaded here, and the preprocessed versions used for this project can be found here.

The model is a recurrent neural network that works at the character level, with the following rough architecture:

1. Embed the characters into 64-dimensional vectors
2. Process the characters through three bidirectional GRU layers, each having 2x128 = 256 hidden units
3. Process the GRU outputs through a time-distributed dense layers with 256 hidden units followed by a ReLU activation
4. Finally project the outputs from the dense layer down to a single dimension across time, outputting a sequence of real numbers between 0 and 1, of the same length as we started with
5. To get the syllable count, we sum up the probabilities and round to nearest integer

To get a more detailed view of the model's architecture see syllablecounter.py, and check out core.py for an idea of how the model is trained.

This model currently achieves a 96.89% validation accuracy.

The reason why we sum up (aggregates of) the probabilities in point (5), rather than firstly rounding the probabilities, is to deal with the situation where the model is unsure whether two consecutive characters begin a new syllable. These will have probabilities ~50% and so will constitute a single syllable rather than two.