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ponicode_rapper.py
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import random
import markovify # Use Markov models to generate new sentences
from rhyme import rhyme_finder # Use cmudict to find phonic rhymes
import tqdm
import os
from nltk.tokenize import word_tokenize
import pickle
from multiprocessing import Pool
import string
class Rapper:
"""Ponicode Rapper generates lyrics by training on rap songs."""
def __init__(self):
self.data_dir = 'data'
self.pools = 5
def remove_punctuation(self, word):
return word.strip(string.punctuation)
def load_data(self, data_dir=None):
"""Produce lyrics from loaded data.
Input: String data dir
Output: String with all lyrics
"""
lyrics_data = ''
if not data_dir:
data_dir = self.data_dir
lyrics_files_paths = [f'{data_dir}/{file_path}'
for file_path in os.listdir(data_dir)
if '.txt' in file_path]
for file_path in lyrics_files_paths:
with open(file_path, 'r', encoding="utf8") as f:
lyrics_data += f.read() + '\n'
self.tokenized_text = word_tokenize(lyrics_data)
return lyrics_data
def build_space(self, lyrics_data, sentences_number=5000, state_size=2):
"""Produce sentences from loaded lyrics.
Input: String of lyrics with all data
Output: List of generated sentences of size sentences_number
"""
self.markov_model = markovify.NewlineText(lyrics_data, state_size)
sentences = []
while len(sentences) < sentences_number:
line = self.markov_model.make_sentence()
if line:
sentences.append(line)
return sentences
def build_rhyme_dict(self, sentence):
try:
rhymes_dict = {}
last_word = sentence.rsplit()[-1]
last_word = self.remove_punctuation(last_word)
rhymes_dict = {'line': sentence,
'last_word' : last_word,
'rhymes': [last_word]
+ rhyme_finder(last_word,self.tokenized_text)
}
return rhymes_dict
except Exception:
pass
return None
def build_rhyme_list(self, sentences, show_errors=False):
"""Produce rhymes from loaded sentences.
Input: Generated sentences by Markov model
Output: List of dict with rhyme and lyrics data
[{'line': 'All the pain I thought we could be',
'last_word': 'be',
'rhymes': ['be','me', 'somebody', 'only', ...]}, ...]
"""
rhymes_list = []
poolmasters = Pool(self.pools)
results = poolmasters.imap(self.build_rhyme_dict, sentences)
for result in tqdm.tqdm(results):
if result:
rhymes_list.append(result)
poolmasters.terminate()
return rhymes_list
def build_equivalence_classes(self, rhymes_list):
"""Produce rhyme equivalence classes from loaded rhymes and lyrics.
Input: List of dict with rhyme and lyrics data
[{'line': 'All the pain I thought we could be',
'last_word': 'be',
'rhymes': ['be', 'me', 'somebody', 'only', ...]}, ...]
Output: List of dict of equivalent classes grouped by rhymes
[{'be': [{'line': 'All the pain I thought we could be',
'last_word': 'be',
'rhymes': ['be', 'me', 'somebody', ...]}, ...],
'me': [{'line': 'Do you feel me?',
'last_word': 'me',
'rhymes': ['me','baby','somebody','only',...]},
...]
"""
equivalence_list = []
k = 0
while len(rhymes_list) > 0:
# Initialize first representor
representor = rhymes_list[0]
# Create last word first key
last_word_representor = representor['last_word']
equivalence_list.append({last_word_representor: [representor]})
rhymes_list = rhymes_list[1:]
# Loop over all sentences to group by rhymes and last word
for i, rhyme_dict in enumerate(rhymes_list):
rhymes = rhyme_dict['rhymes']
if list(set(rhymes).intersection(representor['rhymes']))!=[]:
last_word = rhyme_dict['last_word']
# For never seen last word
if last_word not in equivalence_list[k].keys():
equivalence_list[k][last_word] = [rhyme_dict]
# For already seen last word
else:
equivalence_list[k][last_word].append(rhyme_dict)
rhymes_list.remove(rhyme_dict)
k += 1
return equivalence_list
def train(self, lyrics_data,
sentences_number=5000, state_size=2,
mod='artistic'):
"""Train model of equivalence classes from lyrics"""
print(f'Generating a space of {sentences_number} sentences')
sentences = self.build_space(lyrics_data,
sentences_number, state_size)
self.free_style_sentences = sentences
print(f'Grouping the sentences by rhymes')
if mod == 'artistic':
rhymes_list = self.build_rhyme_list(sentences)
print(f'Creating the equivalence classes')
equivalence_list = self.build_equivalence_classes(rhymes_list)
print('Training Done')
self.equivalence_list = equivalence_list
def save_model(self, file_path):
with open(file_path, 'wb') as file:
pickle.dump(self, file, pickle.HIGHEST_PROTOCOL)
def load_model(self, file_path):
with open(file_path, 'rb') as file:
self.__dict__.update(pickle.load(file).__dict__)
def generate_random_pair(self, cluster):
values_cluster = list(cluster.values())
random.shuffle(values_cluster)
rhyme1_cluster = random.choice(values_cluster[0])
rhyme2_cluster = random.choice(values_cluster[1])
return rhyme1_cluster['line'], rhyme2_cluster['line']
def generate_artistic_verses(self, N_VERSES=8):
patterns = ['abab', 'aabb', 'abba']
generated_lyrics = ''
equivalence_clusters = [cluster for cluster
in self.equivalence_list
if len(cluster.keys()) > 1]
for _ in range(N_VERSES):
pattern = random.choice(patterns)
random.shuffle(equivalence_clusters)
cluster1 = equivalence_clusters[0]
cluster2 = equivalence_clusters[1]
a1, a2 = self.generate_random_pair(cluster1)
b1, b2 = self.generate_random_pair(cluster2)
if pattern == 'abab':
generated_lyrics += '\n'.join([a1, b1, a2, b2])
elif pattern == 'aabb':
generated_lyrics += '\n'.join([a1, a2, b1, b2])
elif pattern == 'abba':
generated_lyrics += '\n'.join([a1, b1, b2, a2])
generated_lyrics += '\n'
generated_lyrics += '\n'
generated_lyrics = self.replace_forbidden_words(generated_lyrics)
return generated_lyrics
def generate_freestyle_verses(self, N_VERSES=8):
verse_length = 4
index = random.choice(range(len(self.free_style_sentences)
-N_VERSES*verse_length-1))
verses = self.free_style_sentences[index:index+N_VERSES*verse_length]
verses = [verses[i:i+verse_length]
for i in range(0, len(verses), verse_length)]
generated_lyrics = ''
for verse_set in verses:
generated_lyrics += '\n'.join(verse_set)
generated_lyrics += '\n'
generated_lyrics += '\n'
generated_lyrics = self.replace_forbidden_words(generated_lyrics)
return generated_lyrics
def replace_forbidden_words(self, generated_text):
BAD_WORDS = [
['hitler','sniffler'],
['white', 'knight'],
['fuck', 'truck'],
['did her', 'bid her'],
['bitch', 'quiche'],
['nigg', 'pig'],
[' ho ', 'toe '],
[' hore ', ' chore '],
['ass', 'bass'],
['shit', 'split']
]
for bad_word in BAD_WORDS:
forbidden_word = bad_word[0]
new_word = bad_word[1]
generated_text=generated_text.replace(forbidden_word.capitalize(),
new_word)
generated_text=generated_text.replace(forbidden_word,
new_word)
generated_text=generated_text.replace(forbidden_word.upper(),
new_word)
return generated_text