mirror of
https://github.com/WallyS02/Song-Lyrics-Generator.git
synced 2025-01-18 08:19:19 +00:00
Interface change and minor improvement in data cleaning.
This commit is contained in:
Sebastian Kutny
commit
a2c1694adb
65
main.py
65
main.py
@ -1,7 +1,7 @@
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import os
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import random
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from scrapper import scrap_data
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from scrapper import clean_data
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from markov_model import clean_data
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from markov_model import create_markov_model
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from markov_model import generate_lyrics
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@ -16,63 +16,44 @@ pink_floyd_selected_albums = ["The Piper At The Gates Of Dawn", "A Saucerful Of
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"A Momentary Lapse Of Reason", "The Division Bell"]
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time_stamp = 3.5
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path = os.path.dirname(os.path.abspath(__file__))
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path = os.path.join(path, "Data")
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def generate_song(name):
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dataset = clean_data(os.path.join(path, name))
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n_gram = int(input("Select number of words in Markov state: "))
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number_of_verses = int(input("Select number of verses: "))
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words_in_verses = int(int(input("Select number of words in verses: ")) / n_gram)
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model = create_markov_model(dataset, n_gram)
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print('\n')
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for i in range(number_of_verses):
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generated_lyrics = generate_lyrics(model, random.choice(list(model.keys())), words_in_verses)
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print(generated_lyrics)
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def main():
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print("Select data set to use in generation or other option:\n1. Pink Floyd lyrics generation\n2. Black Sabbath "
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"lyrics generation\n3. Bracia Figo Fagot\n4. Paktofonika\n5. Fused English (aka Pink Sabbath) lyrics "
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"generation\n6. Fused Polish (aka Braciofonika Pigo Pagot)\n7. Prepare data\n8. Scrap data\n9. Exit")
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pink_floyd_dataset = None
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black_sabbath_dataset = None
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pink_sabbath_dataset = None
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paktofonika_dataset = None
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bracia_figo_fagot_dataset = None
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braciofonika_pigo_pagot_dataset = None
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"generation\n6. Fused Polish (aka Braciofonika Pigo Pagot)\n7. Scrap data\n8. Exit")
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while True:
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selection = int(input())
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match selection:
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case 1:
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model = create_markov_model(pink_floyd_dataset)
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for i in range(5):
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generated_lyrics = generate_lyrics(model, random.choice(list(model.keys())), 10)
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print(generated_lyrics)
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generate_song("Pink Floyd.csv")
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case 2:
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model = create_markov_model(black_sabbath_dataset)
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for i in range(5):
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generated_lyrics = generate_lyrics(model, random.choice(list(model.keys())), 10)
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print(generated_lyrics)
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generate_song("Black Sabbath.csv")
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case 3:
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model = create_markov_model(bracia_figo_fagot_dataset)
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for i in range(5):
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generated_lyrics = generate_lyrics(model, random.choice(list(model.keys())), 10)
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print(generated_lyrics)
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generate_song("Bracia Figo Fagot.csv")
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case 4:
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model = create_markov_model(paktofonika_dataset)
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for i in range(5):
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generated_lyrics = generate_lyrics(model, random.choice(list(model.keys())), 10)
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print(generated_lyrics)
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generate_song("Paktofonika.csv")
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case 5:
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model = create_markov_model(pink_sabbath_dataset)
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for i in range(5):
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generated_lyrics = generate_lyrics(model, random.choice(list(model.keys())), 10)
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print(generated_lyrics)
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generate_song("Pink Sabbath.csv")
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case 6:
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model = create_markov_model(braciofonika_pigo_pagot_dataset)
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for i in range(5):
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generated_lyrics = generate_lyrics(model, random.choice(list(model.keys())), 10)
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print(generated_lyrics)
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generate_song("Braciofonika Pigo Pagot.csv")
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case 7:
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path = os.path.dirname(os.path.abspath(__file__))
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path = os.path.join(path, "Data")
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pink_floyd_dataset = clean_data(os.path.join(path, "Pink Floyd.csv"))
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black_sabbath_dataset = clean_data(os.path.join(path, "Black Sabbath.csv"))
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pink_sabbath_dataset = clean_data(os.path.join(path, "Pink Sabbath.csv"))
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paktofonika_dataset = clean_data(os.path.join(path, "Paktofonika.csv"))
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bracia_figo_fagot_dataset = clean_data(os.path.join(path, "Bracia Figo Fagot.csv"))
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braciofonika_pigo_pagot_dataset = clean_data(os.path.join(path, "Braciofonika Pigo Pagot.csv"))
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case 8:
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scrap_data(pink_floyd_selected_albums, black_sabbath_selected_albums, time_stamp)
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case 9:
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case 8:
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break
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print("\nCommand executed")
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@ -1,7 +1,33 @@
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import random
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import re
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from nltk.tokenize import word_tokenize
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import pandas as pd
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def create_markov_model(dataset, n_gram=2):
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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) - n_gram - 1):
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current_state, next_state = "", ""
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@ -25,11 +51,12 @@ def create_markov_model(dataset, n_gram=2):
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return markov_model
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def generate_lyrics(markov_model, start, limit=100):
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def generate_lyrics(markov_model, start, limit):
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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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next_state = random.choices(list(markov_model[current_state].keys()),
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list(markov_model[current_state].values()))
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32
scrapper.py
32
scrapper.py
@ -4,26 +4,10 @@ from bs4 import BeautifulSoup
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import requests
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import os
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import time
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import re
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from nltk.tokenize import word_tokenize
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from ScrapThread import ScrapThread
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from proxy_handling import proxies_validation
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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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tokens = word_tokenize(lyric)
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words = [word for word in tokens if word.isalpha()]
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dataset += words
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print(name.split('\\')[-1], ": ", len(dataset))
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return dataset
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def connect(url, proxies_list):
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headers = {
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'User-Agent': "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8) AppleWebKit/534.30 (KHTML, like Gecko) "
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@ -156,19 +140,17 @@ def scrap_data(pink_floyd_selected_albums, black_sabbath_selected_albums, time_s
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proxies_list = proxies_validation()
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file = open("links.txt")
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path = os.path.dirname(os.path.abspath(__file__))
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path = path + "\\Data\\"
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file.readline()
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file.readline()
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paktofonika = do_threading(file.readline()[0:-1], [], 0.0, proxies_list)
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figofagot = do_threading(file.readline(), [], 0.0, proxies_list)
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braciofonika_pigo_pagot = pd.concat([paktofonika, figofagot], ignore_index=True)
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paktofonika.to_csv((path + "Paktofonika.csv"))
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figofagot.to_csv((path + "Bracia Figo Fagot.csv"))
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braciofonika_pigo_pagot.to_csv((path + "Braciofonika Pigo Pagot.csv"))
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path = os.path.join(path, "Data")
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pink_floyd_data_frame = do_threading(file.readline()[0:-1], pink_floyd_selected_albums, time_stamp, proxies_list)
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black_sabbath_data_frame = do_threading(file.readline(), black_sabbath_selected_albums, time_stamp, proxies_list)
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pink_sabbath_data_frame = pd.concat([pink_floyd_data_frame, black_sabbath_data_frame], ignore_index=True)
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pink_floyd_data_frame.to_csv((path + "PinkFloyd.csv"))
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black_sabbath_data_frame.to_csv((path + "BlackSabbath.csv"))
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pink_sabbath_data_frame.to_csv((path + "PinkSabbath.csv"))
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paktofonika = do_threading(file.readline()[0:-1], [], 0.0, proxies_list)
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figofagot = do_threading(file.readline(), [], 0.0, proxies_list)
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braciofonika_pigo_pagot = pd.concat([paktofonika, figofagot], ignore_index=True)
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paktofonika.to_csv((path + "Paktofonika.csv"))
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figofagot.to_csv((path + "Bracia Figo Fagot.csv"))
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braciofonika_pigo_pagot.to_csv((path + "Braciofonika Pigo Pagot.csv"))
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os.remove("valid_proxy_list")
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