Hello! Welcome to the BoobaBot repository.

This project is about developping an A.I. rap writer that can write a word/sentence/para/full song or autocomplete your writing to help you find some inspiration!

For more techy people: this is a basic GloVe embedding using LSTM best practices and TensorFlow to predict words.

You can also train your own bot on a different corpus to potentially create whatever artiste you'd like (rock and roll, law, poems, etc.)

Disclaimer: The weights are not provided with the model

Install depedencies

pip install -r requirements.txt

You can train your GloVe on a corpus using the train_glove.py script with those options:

• debug: Debug mode
• random_search: Procede to a random search for hyperparameters tuning
• textfile: The textfile to use to build the corpus
• embedding_size: Embedding size
• context_size: Number of words to use for context
• num_epochs: How many epochs should we train the GloVe
• nb_search_iter: Batch size
• stem: Should we stem words?

You can train your model on a corpus using the train.py script with those options:

• debug: Debug mode
• profiling: Profiling mode, output a timeline readable inside chrome
• num_epochs: How many epochs should we train the RNN
• batch_size: Batch size
• lr: Learning rate for the adam optimizers
• textfile: The textfile used to train GloVe
• glove_dir The GloVe directory
• train_glove: Are we finetuning/training GloVe embedding
• cell_name: str, help="Cell architecture
• rnn_activation: str, help="RNN activation function names)")
• seq_length: RNN sequence length
• state_size: RNN state size
• num_layers: How deep is the RNN
• tye_embedding: Tye input/output word embedding weights

Before make him rap, you need to train the model (see previous point).

You can make your model rap using the predict.py script with those options:

• debug: Debug mode
• model_dir: The trained RNN dir
• inputs: Choose the beginning of the predicted sentence (see this as an initialization)
• random: Add some randomness, predict using temperature
• temperature: The temperature for predictions
• top_k: Return the top K prediction
• nb_word: How many words should it return
• nb_sentence: How many lines should it return
• nb_para: How many paragraph should it return

**See the README.md file in the app folder **

python -m unittest discover

• app: Holds everything related to the autocomplete app, for more information on this point, check the README file inside the folder
• crawler: This is a very simple and rough crawler, but still, it made the work for us, so if you want to use it, go on!
• dataviz: Holds all python script to visualize the word embedding using sklearn or tensorboard
• models: Holds models python files: RNNs and GloVe
• server: Holds some files to use TensorFlow serving architecture in production
• tests: Holds some tests sherlocks!
• Python files:
  • hyperparams_search.py: Bash script to handle hyperparamerters search on the model
  • predict.py: Generate new image from a trained model
  • train_glove.py: Train GloVe embedding
  • train.py: Train a model to achieve deep rap
  • util.py: Some utilitary function to clean and handles input data

Have fun! 🍻

The GloVe embedding script has been taken from this repository (Thank You! 👍) and be adapted to be more TensorFlow friendly (especially the saving/loading part)