Initial import: Music_Server, MusicFree, catalog-sync

2026-05-23 16:51:14 +08:00
commit 069af30dba
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+'''
+Function:
+    Implementation of SingerLyricsAnalysis
+Author:
+    Zhenchao Jin
+WeChat Official Account (微信公众号):
+    Charles的皮卡丘
+'''
+import os
+import jieba
+import pickle
+import numpy as np
+from PIL import Image
+from snownlp import SnowNLP
+from wordcloud import WordCloud
+from musicdl.modules.sources import MiguMusicClient
+
+
+'''SingerLyricsAnalysis'''
+class SingerLyricsAnalysis():
+    def __init__(self):
+        self.root_dir = os.path.split(os.path.abspath(__file__))[0]
+        self.music_client = MiguMusicClient(search_size_per_source=2000)
+    '''start'''
+    def start(self):
+        while True:
+            singer_name = input('Input singer to analyze: ')
+            print(f'[INFO]: Searching {singer_name}')
+            infos = self.crawler(singer_name)
+            print(f'[INFO]: Analyzing {singer_name}')
+            self.analysis(infos)
+    '''crawler'''
+    def crawler(self, singer_name):
+        song_infos = self.music_client.search(keyword=singer_name)
+        self.save(singer_name=singer_name, song_infos=song_infos)
+        return song_infos
+    '''analysis'''
+    def analysis(self, song_infos):
+        # data clean
+        lyrics = []
+        for song_info in song_infos:
+            lyric = song_info['lyric']
+            lyric = lyric.split('\r\n')
+            lyric_filtered = []
+            for sentence in lyric:
+                sentence = sentence[10:]
+                if (not sentence) or ('：' in sentence) or (self.root_dir in sentence) or ('[' in sentence) or (']' in sentence) or ('歌曲' in sentence): continue
+                lyric_filtered.append(sentence)
+            lyrics += lyric_filtered
+        # generatewordcloud
+        words_dict = {}
+        for sentence in lyrics:
+            words = jieba.cut(sentence)
+            for word in words:
+                word = word.strip()
+                if not word: continue
+                if len(word) < 2: continue
+                if word in words_dict: words_dict[word] += 1
+                else: words_dict[word] = 1
+        words_freq_sorted = sorted(words_dict.items(), key=lambda item: item[1])
+        words_freq_top10 = words_freq_sorted[-10:]
+        self.generatewordcloud(words_dict)
+        self.drawbar('%s歌曲中的词语TOP10' % self.root_dir, words_freq_top10)
+        # nlp analysis
+        nlp_dict = {'内容极度负面': 0, '内容较为负面': 0, '内容中性': 0, '内容较为正面': 0, '内容非常正面': 0}
+        for sentence in lyrics:
+            score = SnowNLP(sentence).sentiments
+            if score < 0.2:
+                nlp_dict['内容极度负面'] += 1
+            elif score >= 0.2 and score < 0.4:
+                nlp_dict['内容较为负面'] += 1 
+            elif score >= 0.4 and score < 0.6:
+                nlp_dict['内容中性'] += 1 
+            elif score >= 0.6 and score < 0.8:
+                nlp_dict['内容较为正面'] += 1 
+            else:
+                nlp_dict['内容非常正面'] += 1 
+        self.drawpie('%s的歌词情感分析' % self.root_dir, nlp_dict)
+    '''drawbar'''
+    def drawbar(self, title, infos):
+        from pyecharts.charts import Bar
+        from pyecharts import options as opts
+        from pyecharts.globals import ThemeType
+        bar = Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
+        bar.add_xaxis([item[0] for item in infos])
+        bar.add_yaxis('freq', [item[1] for item in infos])
+        bar.set_global_opts(title_opts=opts.TitleOpts(title=title))
+        bar.render(os.path.join(self.root_dir, title+'.html'))
+    '''drawpie'''
+    def drawpie(self, title, infos):
+        from pyecharts.charts import Pie
+        from pyecharts import options as opts
+        pie = Pie(init_opts=dict(theme='westeros', page_title=title)).add(title, data_pair=tuple(zip(infos.keys(), infos.values())), rosetype='area')
+        pie.set_global_opts(title_opts=opts.TitleOpts(title=title))
+        pie.render(os.path.join(self.root_dir, '%s.html' % title))
+    '''generatewordcloud'''
+    def generatewordcloud(self, infos):
+        mask = Image.open(os.path.join(self.root_dir, 'resources/mask.jpg'))
+        mask = np.array(mask)
+        wc = WordCloud(background_color='white', font_path=os.path.join(self.root_dir, 'resources/font_cn.TTF'), mask=mask)
+        result = wc.generate_from_frequencies(infos)
+        result.to_file(os.path.join(self.root_dir, 'wordcloud.png'))
+    '''save'''
+    def save(self, song_infos, singer_name):
+        data_save_path = os.path.join(self.root_dir, f'song_infos_{singer_name}.pkl')
+        with open(data_save_path, 'wb') as fp:
+            pickle.dump(song_infos, fp)
+    '''load'''
+    def load(self, singer_name):
+        data_save_path = os.path.join(self.root_dir, f'song_infos_{singer_name}.pkl')
+        fp = open(data_save_path, 'rb')
+        return pickle.load(fp)
+
+
+'''tests'''
+if __name__ == '__main__':
+    client = SingerLyricsAnalysis()
+    client.start()