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        "author": "Ranjith M S",
        "author_email": "ranjithms523@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
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        "description": "\u00ef\u00bb\u00bf# musicgen\nmusicgen is a module for pre-processing audio and to prepare the dataset for neural networks in genarating music. Built on top of Music21 and youtube_dl\n### Features\n  - Download the songs,videos from URL\n  - Get the notes from music\n  - Prepare the dataset for neural networks\n\n### Installation\n\n\n\n```sh\n$ pip install musicgen\n```\n#### get_notes(path='*.mid')\nGet all the notes and chords from the midi files  \n##### Args:  \npath : path for the song with midi extension. \u00e2\u20ac\u02dc*.mid\u00e2\u20ac\u2122 by default which gets the note of all the songs in the current working directory.  \n##### Returns:  \nList of notes obtained from the midi file   \n#### download_song_with_url(url,audio_type='mp3',quality='192')\nDownloads the audio of a video present in the given video URL to current working directory.  \n##### Args:   \nurl : (string)  URL of a video  \naudio_type :(string)\"aac\", \"flac\", \"mp3\", \"m4a\", \"opus\", \"vorbis\", or \"wav\". \u00e2\u20ac\u02dcmp3\u00e2\u20ac\u2122 by default  \n#### create_midi(prediction_output,name='output')\nconvert the output from the prediction to notes and create a midi file from the notes in current working directory.  \n##### Args:   \nprediction_output : the output predictions of a trained model.  \nname : (string) name of the generated file. output by default.  \n#### prepare_sequences(notes, n_vocab,sequence_length = 100)\nPrepare the sequences used by the Neural Network  \n##### Args:  \nnotes : (list) notes of midi file   \nn_vocab : (int) number of unique notes  \nsequence_length : (int) number of time steps required. 100 by default.  \n##### Returns:  \nnetwork_input, network_output   \n\n#### download_video_with_url(url)\nDownloads the video from the given URL into current working directory.  \n##### Args:  \nurl : (string)  URL of a video  \n\n#### download_videos(path)\nDownloads the videos from the URL\u00e2\u20ac\u2122s present in a text file into current working directory.  \n##### Args:  \npath : (string)  path of a text file containing URL\u00e2\u20ac\u2122s  \n\n#### download_songs(path)\nDownloads the audio of videos from the URL\u00e2\u20ac\u2122s present in a text file into current working directory.  \n##### Args:  \npath : (string)  path of a text file containing URL\u00e2\u20ac\u2122s.  \n\n#### song_notes_to_pickle(path,output)\nsaves the notes of a midi file as a pickle object.  \n##### Args:  \npath : (string) path  of the songs    \noutput : (string) name of the pickle file.  \n\n#### generate_notes(model, network_input, pitchnames, n_vocab)\ngenerates the notes from the trained keras model  \n##### Args:  \nmodel : Trained keras model for prediction  \nnetwork_input : input to the network  \npitchnames : set of items in the notes. It is found using pitchnames = sorted(set(item for item in notes))  \nn_vocab : (int) number of unique notes  \n##### Returns:  \npredicted_output  \n\n\n\n\n\n\n\n",
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        "keywords": "LSTM,Sequence models,RNN,Neural network,Music generation",
        "license": "MIT",
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        "name": "musicgen",
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        "summary": "A module for pre-processing steps in music generation",
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