mirror of
https://github.com/WallyS02/Song-Lyrics-Generator.git
synced 2024-10-03 11:26:10 +00:00
109 lines
4.8 KiB
Python
109 lines
4.8 KiB
Python
import random
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import re
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from nltk import SyllableTokenizer
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from nltk.tokenize import word_tokenize
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import pandas as pd
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import numpy as np
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from scipy import sparse
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def clean_data(name):
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document = pd.read_csv(name, usecols=["Lyrics"])
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rows = document["Lyrics"].values.tolist()
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dataset = []
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for lyric in rows:
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lyric = lyric.lower()
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lyric = re.sub(r"[,.\"\'!@#$%^&*(){}?/;`~:<>+=-\\]", "", lyric)
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lyric = re.sub(r"\([A-Za-z0-9:\s\.\?\,\&\*]+\)", "", lyric)
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lyric = re.sub(r"\[[A-Za-z0-9:\s\.\?\,\&\*]+\]", "", lyric)
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lyric = re.sub(r"[A-Za-z0-9]+::", "", lyric)
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lyric = re.sub(r"[A-Za-z0-9]+:", "", lyric)
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lyric = re.sub(r"/[A-Za-z0-9]+", "", lyric)
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lyric = re.sub(r"x[0-9]", "", lyric)
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forbidden_words = ['chorus', 'refrain', 'coda', 'solo', 'intro', 'introduction', 'verse', 'pre-chorus',
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'post-chorus', 'bridge', 'outro', 'ref']
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tokens = word_tokenize(lyric)
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words = [word for word in tokens if word.isalpha()]
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words = [word for word in words if word not in forbidden_words]
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dataset += words
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print(name.split('\\')[-1], "number of words in cleaned data: ", len(dataset))
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return dataset
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def create_markov_model(dataset, n_gram):
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markov_model = {}
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for i in range(len(dataset) - 1 - 2 * n_gram):
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current_state, next_state = "", ""
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for j in range(n_gram):
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current_state += dataset[i + j] + " "
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next_state += dataset[i + j + n_gram] + " "
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current_state = current_state[:-1]
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next_state = next_state[:-1]
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if current_state not in markov_model:
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markov_model[current_state] = {}
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markov_model[current_state][next_state] = 1
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else:
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if next_state in markov_model[current_state]:
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markov_model[current_state][next_state] += 1
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else:
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markov_model[current_state][next_state] = 1
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for current_state, transition in markov_model.items():
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total = sum(transition.values())
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for state, count in transition.items():
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markov_model[current_state][state] = count / total
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"""matrix = [[0 for _ in range(len(markov_model.items()))] for _ in range(int(len(markov_model.items())))]
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for current_state, transition in markov_model.items():
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tempRow = list(markov_model.items())
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indexRow = [idx for idx, key in enumerate(tempRow) if key[0] == current_state]
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total = sum(transition.values())
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for state, count in transition.items():
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tempCol = list(transition.items())
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indexCol = [idx for idx, key in enumerate(tempCol) if key[0] == state]
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markov_model[current_state][state] = count / total
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matrix[indexRow[0]][indexCol[0]] = markov_model[current_state][state]
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matrix = np.array(matrix)
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for i in range(n_step):
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matrix = matrix.dot(matrix)
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for current_state, transition in markov_model.items():
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tempRow = list(markov_model.items())
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indexRow = [idx for idx, key in enumerate(tempRow) if key[0] == current_state]
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for state, count in transition.items():
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tempCol = list(transition.items())
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indexCol = [idx for idx, key in enumerate(tempCol) if key[0] == state]
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markov_model[current_state][state] += matrix[indexRow[0]][indexCol[0]]"""
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return markov_model
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def generate_lyrics(markov_model, start, limit, isStartingVerse, rime):
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n = 0
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current_state = start
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lyrics = ""
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lyrics += current_state + " "
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lyrics = lyrics[0].upper() + lyrics[1:]
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while n < limit:
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if n == limit - 1 and not isStartingVerse:
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rime = rime.split(" ")[-1]
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tk = SyllableTokenizer()
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rime_syllab = tk.tokenize(rime)[-1]
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rime_states = {}
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for state, probability in markov_model[current_state].items():
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word = state.split(" ")[-1]
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syllab = tk.tokenize(word)[-1]
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if rime_syllab == syllab and rime != word:
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rime_states.update({state: probability})
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if rime_states:
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next_state = random.choices(list(rime_states.keys()),
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list(rime_states.values()))
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current_state = next_state[0]
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else:
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next_state = random.choices(list(markov_model[current_state].keys()),
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list(markov_model[current_state].values()))
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current_state = next_state[0]
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else:
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next_state = random.choices(list(markov_model[current_state].keys()),
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list(markov_model[current_state].values()))
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current_state = next_state[0]
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lyrics += current_state + " "
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n += 1
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return lyrics, current_state
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