Darkflow Persian Music Recommendation on Lyrics

About

the target og this project is identifying topic of each persian song, whith unsupervised process by LDA method of topic modelling

Online Test in Colab

We currently have three topics : Gang/Diss & Romantic & Religious you can test online this AI model in the link below:

DataSet

data set contains 800 lyrics of Hip Hop Gang/Diss Tracks & Romantic songs & Religious songs i fetch 554 lyric from RadioJavan songs with RadioJavanApi and fetch 225 lyric from beharalashar with web scrapping and insert all to Postgresql database

Natural Language Processing Libraries

i used Stanza and Hazm for natural language processing for Perian language

LDA Training

i used tomotopy library for LDA training

Stop Word

for remove Stop Words, i used Stop Word list prepared by Rahmani Dashti

Files & Package Structure

├── data                          # For data handling
│   ├── Database.py               # Interaction with database   
│   ├── FetchSongRadioJavan.py    # Fetch lyrics from RadioJavan
│   └── WebScrappingPraise.py     # Fetch lyrics from Beharalashar
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├── model                         # Model classes
│   ├── SongCategory.py           # Song Category Enum
│   └── SongTokensModel.py        # Song Tokens Model
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├── nlp                           # NLP Classes         
│   ├── CountVector.py            # Count Vector
│   ├── LDAModelTraining.py       # LDA Model training & test       
│   ├── NlpProcess                # normalize & tokenize & lemmatize   
│   └── StopWords.py              # check each token is StopWord
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├── resources                     # resource files
│   ├── word-cloud.png            # word cloud image
│   ├── stopwords_all.txt         # list of stopwords
│   └── test_data.txt             # text file to insert lyric to test model 
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├── training_model                
│   └── lda_model.bin             # prepared model
│
└── utils                
    └── utils.py                  # utils for check has numbers & remove punctuation

License

MIT License

Copyright (c) 2021 Mohammad amin abbasi

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.