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[免费]基于Python的深度学习音乐推荐系统(后端Django)【论文+源码+SQL脚本】

news 2025/9/23 11:43:30

大家好，我是python222_小锋老师，看到一个不错的基于Python的深度学习音乐推荐系统(后端Django)，分享下哈。

项目视频演示

https://www.bilibili.com/video/BV1mznFzEEHF/

项目介绍

数字化时代带动着整个社会的信息化发展，随着数字媒体的不断发展，现在通多媒体数字产品的内容越来越丰富，传播影响力越来越强，以音乐为例，现在的音乐文化多样、音乐资源也异常的丰富，在这种大数据的环境下，人们要想找到想要的音乐类型、找到心里所想的那首音乐无疑是大海捞针。现在音乐的推荐系统也非常的多，但是推荐的内容、推荐的方式却与用户的感知差距明显，或多或少都会存在一些问题。而随着深度学习、卷积神经网络的不断发展，现在的深度学习在图像识别、自然语言等领域都有着很好的发展，也很好的应用在了音乐的推荐过程中。

本次的研究是基于使用自动编码器，通过与卷积神经网络相结合，以挖掘音频、歌词本身的非线性特征，来实现很好的音乐推荐、音乐查找识别的功能实现，并将内容特征与协同过滤共同作用，训练紧耦合模型。通过此次的系统搭建与开发，能够通过深度学习的方式让系统可以实现按照用于的喜好来进行音乐的推荐的功能实现。

系统展示

部分代码

import refrom django.db.models import Max, Count
from django.http import JsonResponse
from django.shortcuts import render
from django.views import View
from index.models import Song, Dynamic,Music
from .predict import predict_baseon_item
def check(str):my_re = re.compile(r'[A-Za-z]', re.S)res = re.findall(my_re, str)if len(res):return Trueelse:return False
class RankingView(View):def get(self, request):from .tests import mainresults = Music.objects.all()## for i in results:#     url = main(i.name)#     if not url:#         url = ''##     Music.objects.filter(id=i.id).update(#         url=url#     )#rint('{}已更新{}'.format(i.name,url))# 热搜歌曲search_song = Dynamic.objects.all().order_by('-dynamic_plays')[0]music_type = Song.objects.filter(song_id=search_song.song_id).first().song_typesongs = Song.objects.filter(song_type=music_type).all()print(music_type)# 歌曲分类列表# All_list = Song.objects.values('song_type').distinct() # 去重# 歌曲信息列表song_type = request.GET.get('type', '')return render(request, 'ranking/ranking.html', {'songs':songs})def predict(request):inputText = request.POST.get("name")result= predict_baseon_item(str(inputText))print('1111',result)result_list = []if not result:pid = Music.objects.filter(name=inputText).first().pidall_data = Music.objects.filter(pid=pid).all()if len(all_data) > 10:all_data = all_data[0:10]for i in all_data:result_dict = {'name':i.name,'singer':i.singer,'src':'/static/songFile/体面.m4a',}result_list.append(result_dict)else:for i in result:obj_music = Music.objects.filter(name=i).first()result_dict = {'name': i,'src': '/static/songFile/体面.m4a',}result_list.append(result_dict)print(result_list)return JsonResponse({'input_text': inputText, 'result_list': result_list})

# -*- coding: UTF-8 -*-import os
import pickle
import surprise
from surprise import KNNBaseline, Reader
from surprise import Datasetwork_dir = os.path.dirname(os.path.abspath(__file__))def split_file(in_path, out_path, scale):f1 = open(in_path, "r")f2 = open(out_path, "w")context = f1.readlines()offset = int(len(context) * scale)print("new length:", offset)split_context = ""for i in range(offset):split_context += context[i]f2.write(split_context)f1.close()f2.close()# data pre-prossess
def parse_playlist_get_info(in_line, playlist_dic, song_dic):contents = in_line.strip().split("\t")name, tags, playlist_id, subscribed_count = contents[0].split("##")playlist_dic[playlist_id] = namefor song in contents[1:]:try:print(song)song_id, song_name, artist, popularity = song.split(":::")song_dic[song_id] = song_nameexcept Exception as e:print("song format error", e)print(song + "\n")def parse_file(in_file, out_playlist, out_song):# 从歌单id到歌单名称的映射字典playlist_dic = {}# 从歌曲id到歌曲名称的映射字典song_dic = {}for line in open(in_file, 'r', encoding='UTF-8'):parse_playlist_get_info(line, playlist_dic, song_dic)# 把映射字典保存在二进制文件中pickle.dump(playlist_dic, open(out_playlist, "wb"))# 可以通过 playlist_dic = pickle.load(open("playlist.pkl","rb"))重新载入pickle.dump(song_dic, open(out_song, "wb"))def data_preprocess():playlist_id_name_dic_file = os.path.join(work_dir, "data/playlist_id_name_dic.pkl")song_id_name_dic_file = os.path.join(work_dir, "data/song_id_name_dic.pkl")input_file = os.path.join(work_dir, "data/popular.playlist")# 解析文件，保存字典「歌单ID-歌单名称」、字典「歌曲ID-歌曲名称」parse_file(in_file=input_file, out_playlist=playlist_id_name_dic_file, out_song=song_id_name_dic_file)print("已保存字典「歌单ID-歌单名称」、字典「歌曲ID-歌曲名称」")def train_baseon_item():# 数据预处理data_preprocess()path = os.path.join(work_dir, "data/")file_path = os.path.expanduser(path + "popular_music_suprise_format1.txt")# 指定文件格式reader = Reader(line_format='user item rating timestamp', sep=',')# 从文件读取数据music_data = Dataset.load_from_file(file_path, reader=reader)# 计算歌曲和歌曲之间的相似度print("构建数据集...")trainset = music_data.build_full_trainset()  # 把全部数据进行训练，不进行交叉验证print("开始训练模型...")sim_options = {'user_based': False}  # 基于歌曲的协同过滤algo = KNNBaseline(sim_options=sim_options)# algo = SVD(sim_options=sim_options)algo.fit(trainset)surprise.dump.dump(os.path.join(work_dir, 'model/KNNBaseline_Item_Recommand.model'), algo=algo)# 保证数据一致性# 重建歌曲id到歌曲名的映射字典f1 = open(path + "song_id_name_dic.pkl", "rb")song_id_name_dic = pickle.load(f1)f1.close()f2 = open(path + "popular_music_suprise_format1.txt")context = f2.readlines()new_song_id_name_dic = {}for line in context:playlist_id, song_id, rating, time = line.split(',')new_song_id_name_dic[song_id] = song_id_name_dic[song_id]pickle.dump(new_song_id_name_dic, open(path + "song_id_name_dic.pkl", "wb"))f2.close()def train_baseon_playlist():# 数据预处理data_preprocess()path = os.path.join(work_dir, "data/")file_path = os.path.expanduser(path + "popular_music_suprise_format.txt")# 指定文件格式reader = Reader(line_format='user item rating timestamp', sep=',')# 从文件读取数据music_data = Dataset.load_from_file(file_path, reader=reader)# 计算歌单和歌单之间的相似度print("构建数据集...")trainset = music_data.build_full_trainset()  # 把全部数据进行训练，不进行交叉验证print("开始训练模型...")sim_options = {'user_based': True}  # 基于歌单的协同过滤algo = KNNBaseline(sim_options=sim_options)algo.fit(trainset)surprise.dump.dump(os.path.join(work_dir, 'model/KNNBaseline_Playlist_Recommand.model'), algo=algo)# 保证数据一致性# 重建歌单id到歌单名的映射字典f1 = open(path + "playlist_id_name_dic.pkl", "rb")playlist_id_name_dic = pickle.load(f1)f1.close()f2 = open(path + "popular_music_suprise_format1.txt")context = f2.readlines()new_playlist_id_name_dic = {}for line in context:playlist_id, song_id, rating, time = line.split(',')new_playlist_id_name_dic[playlist_id] = playlist_id_name_dic[playlist_id]pickle.dump(new_playlist_id_name_dic, open(path + "playlist_id_name_dic.pkl", "wb"))f2.close()def predict_baseon_item(song_name):path = os.path.join(work_dir, "data/")# 重建歌曲id到歌曲名的映射字典song_id_name_dic = pickle.load(open(path + "song_id_name_dic.pkl", "rb"))# 重建歌单名到歌单id的映射字典song_name_id_dic = {}for song_id in song_id_name_dic:song_name_id_dic[song_id_name_dic[song_id]] = song_idif song_name not in song_name_id_dic.keys():return "数据库还没有收录这首歌"_, algo = surprise.dump.load(os.path.join(work_dir, 'model/KNNBaseline_Item_Recommand.model'))# 取出近邻# 映射名字到idsong_id = song_name_id_dic[song_name]print("歌曲id", song_id)# 取出来对应的内部item id => to_inner_iidtry:song_inner_id = algo.trainset.to_inner_iid(song_id)except Exception as e:print("查找内部歌曲id发生异常:", e)song_list_neighbors = []returnelse:print("歌曲内部id", song_inner_id)song_list_neighbors = algo.get_neighbors(song_inner_id, k=10)# 把歌曲id转成歌曲名字# to_raw_uid映射回去song_list_neighbors = (algo.trainset.to_raw_iid(inner_id)for inner_id in song_list_neighbors)song_list_neighbors = list(song_id_name_dic[song_id]for song_id in song_list_neighbors)result = ""for song_name in song_list_neighbors:result += song_namereturn song_list_neighborsdef predict_baseon_playlist(playlist_name):path = "./data/"# 重建歌单id到歌单名的映射字典playlist_id_name_dic = pickle.load(open(path + "playlist_id_name_dic.pkl", "rb"))# 重建歌单名到歌单id的映射字典playlist_name_id_dic = {}for playlist_id in playlist_id_name_dic:playlist_name_id_dic[playlist_id_name_dic[playlist_id]] = playlist_id# if playlist_name not in playlist_name_id_dic.keys():#     return "数据库还没有收录这首歌"_, algo = surprise.dump.load(path + '/KNNBaseline_Playlist_Recommand.model')# 取出近邻# 映射名字到idprint(playlist_name_id_dic)playlist_id = playlist_name_id_dic[playlist_name]print("歌单id", playlist_id)# 取出来对应的内部user id => to_inner_uidtry:playlist_inner_id = algo.trainset.to_inner_uid(playlist_id)except ValueError:print("查找内部歌曲id发生异常:", ValueError.__name__)returnelse:print("歌曲内部id", playlist_inner_id)playlist_list_neighbors = algo.get_neighbors(playlist_inner_id, k=10)# 把歌曲id转成歌曲名字# to_raw_uid映射回去playlist_list_neighbors = (algo.trainset.to_raw_uid(inner_id)for inner_id in playlist_list_neighbors)# raw_uid -> playlist_nameplaylist_list_neighbors = list(playlist_id_name_dic[playlist_id]for playlist_id in playlist_list_neighbors)playlist_list_neighbors.insert(0, "和歌单 《" + playlist_name + "》 最接近的10个歌曲为：")for song_name in playlist_list_neighbors:print(song_name)return playlist_list_neighborsif __name__ == '__main__':# data_preprocess()# file = open("./data/song_id_name_dic.pkl",'rb')# song_id_name_dic = pickle.load(file)# key = list(song_id_name_dic.keys())[6]# print(song_id_name_dic[key])# file.close()# data_preprocess()# file = open("./data/playlist_id_name_dic.pkl",'rb')# playlist_id_name_dic = pickle.load(file)# keys = list(playlist_id_name_dic.keys())[:30]# for key in keys:#     print(playlist_id_name_dic[key])# file.close()# predict_baseon_playlist("中国好声音第四季原唱")# result = predict("本草纲目")# predict(result)split_file("./data/popular_music_suprise_format.txt", "./data/popular_music_suprise_format1.txt", 0.08)train_baseon_item()train_baseon_playlist()