├── .gitignore ├── README.md ├── data_preprocessing.ipynb ├── generate.py ├── model.hdf5 ├── model_plot.png ├── music.png ├── notes ├── songs ├── 0fithos.mid ├── 8.mid ├── AT.mid ├── BlueStone_LastDungeon.mid ├── Cids.mid ├── DOS.mid ├── Eternal_Harvest.mid ├── EyesOnMePiano.mid ├── FF3_Battle_(Piano).mid ├── FF3_Third_Phase_Final_(Piano).mid ├── FF4.mid ├── FF6epitaph_piano.mid ├── FF8_Shuffle_or_boogie_pc.mid ├── FFIII_Edgar_And_Sabin_Piano.mid ├── FFIXQuMarshP.mid ├── FFIX_Piano.mid ├── FFVII_BATTLE.mid ├── FFX_-_Ending_Theme_(Piano_Version)_-_by_Angel_FF.mid ├── Ff4-BattleLust.mid ├── Ff7-Cinco.mid ├── Ff7-Jenova_Absolute.mid ├── Ff7-One_Winged.mid ├── Fiend_Battle_(Piano).mid ├── Fierce_Battle_(Piano).mid ├── Final_Fantasy_7_-_Judgement_Day_Piano.mid ├── Final_Fantasy_Matouyas_Cave_Piano.mid ├── Finalfantasy5gilgameshp.mid ├── Finalfantasy6fanfarecomplete.mid ├── Fyw_piano.mid ├── Gold_Silver_Rival_Battle.mid ├── HighwindTakestotheSkies.mid ├── In_Zanarkand.mid ├── JENOVA.mid ├── Kingdom_Hearts_Dearly_Beloved.mid ├── Kingdom_Hearts_Traverse_Town.mid ├── Life_Stream.mid ├── OTD5YA.mid ├── Oppressed.mid ├── Rachel_Piano_tempofix.mid ├── Rydia_pc.mid ├── Still_Alive-1.mid ├── Suteki_Da_Ne_(Piano_Version).mid ├── VincentPiano.mid ├── ViviinAlexandria.mid ├── Zelda_Overworld.mid ├── ahead_on_our_way_piano.mid ├── balamb.mid ├── bcm.mid ├── braska.mid ├── caitsith.mid ├── cosmo.mid ├── costadsol.mid ├── dayafter.mid ├── decisive.mid ├── dontbeafraid.mid ├── electric_de_chocobo.mid ├── ff11_awakening_piano.mid ├── ff1battp.mid ├── ff4-airship.mid ├── ff4-fight1.mid ├── ff4-town.mid ├── ff4_piano_collections-main_theme.mid ├── ff4pclov.mid ├── ff6shap.mid ├── ff7-mainmidi.mid ├── ff7themep.mid ├── ff8-lfp.mid ├── figaro.mid ├── fortresscondor.mid ├── gerudo.mid ├── goldsaucer.mid ├── great_war.mid ├── lurk_in_dark.mid ├── mining.mid ├── path_of_repentance.mid ├── pkelite4.mid ├── redwings.mid ├── relmstheme-piano.mid ├── roseofmay-piano.mid ├── rufus.mid ├── sandy.mid ├── sera_.mid ├── sobf.mid ├── thenightmarebegins.mid ├── thoughts.mid ├── tifap.mid ├── tpirtsd-piano.mid ├── traitor.mid ├── ultimafro.mid ├── ultros.mid ├── waltz_de_choco.mid └── z_aeristhemepiano.mid └── source.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | .DS_Store 2 | .DS_Store? 3 | ._* 4 | .Spotlight-V100 5 | .Trashes 6 | ehthumbs.db 7 | Thumbs.db -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 |

2 | music 6 |

7 | 8 |

AI Music Generator

9 | 10 |

11 | 12 | 13 | 14 | 15 | 16 |

17 | 18 |

19 | This is a Deep Learning & Natural Language Processing model which can generate Piano Music 20 |

21 | 22 |

TABLE OF CONTENTS

23 |
    24 |
  1. Introduction
    2. 25 |
  2. Dataset
    3. 26 |
  3. Music Theory
    4. 27 |
  4. Model
    5. 28 |
  5. Frameworks, Libraries & Languages
    6. 29 |
  6. Usage
    7. 30 |
  7. Acknowledgement
    8. 31 |
32 | 33 |

Introduction

34 |

35 | The usage of Neural Networks has been steadily increasing over time. With a multitude of papers being published every year, Deep Learning has found its applications in many fields of our daily lives - ranging from recommedation systems and personalization to medical diagnosis and healthcare. A recently popularised area of applying these techniques is for content generation. 36 |
37 | Text Generation, the most commonly seen form of this has become a ubiquitous feature in recent years. Auto-complete features in our message apps, emails and even Google searches is a common and helpful application of this. The model on the backend inputs and processes the initial few words typed by us and predicts the next most probable word from its vocabulary. The user has an option to use this word or continue typing, either of which further trains the model as it learns from the actual next word. 38 |
39 | An attempt along a similar philosophy can be made to train a neural network to generate music, and this is indeed becoming popular in recent years. Here I build a Long-Short Term Memory (LSTM) neural network in Python using Keras, to generate piano music. 40 |

41 | 42 |

Dataset

43 |

44 | The dataset (contained in the /songs directory) consists of around 90 MIDI (Musical Instrument Digital Interface) audio files. Each of these files is a couple of minutes in duration and consists of piano music. Most of these files contain music from the Final Fantasy series of games, since the music is very distinct and has beautiful melodies. For playing the music of a file, follow the steps in Usage section below. 45 |

46 | 47 |

Music Theory

48 |

49 | The Concise Oxford Dictionary defines music as "the art of combining vocal or instrumental sounds (or both) to produce beauty of form, harmony, and expression of emotion". In simpler terms, music can be thought of comprising of a basic element - Note. A note essentially represents the pitch of the music at that point in time. Notes are a discretization of musical phenomena, and are often regarded as the building blocks of music. Pitch can be roughly realised to be correlated with the frequency of the sound, but in essence is more of an abstract property, which depends on the perception of person hearing it. It is often represented with capital letters - A, B, C, D, E, F, G. These letter names can also be modified by using two accidentals - # (the shap sign, which raises a note by half-step) and ♭(the flat sign, which lowers it by half-step). 50 |
51 | Each Note also has certain other characteristics namely - Offset (the length of time from the start of a piece when the note is played) and Duration (the time for which the note is held). If there are no periods of silence in the music and no occurrences of two notes being played together, then the offset of a note is effectively the sum of the previous durations. 52 |
53 | A Chord in music, is a set of multiple notes ("pitches") that are heard sounding simultaneously. A piano normally contains many spans (or sets) of eight-white keys called an Octave. 54 |

55 | 56 |

Model

57 |
58 |
59 | Model Plot 62 |
A plot of the model and its layers
63 |
64 |
65 |

66 | I built an LSTM model using Keras Sequential API, which inputs sequences (of notes) of fixed length, and learns to predict the next note in the sequence. A plot of the model layers is given above. 67 |

68 | 69 |

Frameworks, Libraries & Languages

70 | 80 | 81 |

Usage

82 |
    83 |
  1. 84 | Install all dependencies 85 |
    86 | pip install python3 87 |
    88 | pip install numpy 89 |
    90 | pip install tensorflow 91 |
    92 | pip install keras 93 |
    94 | pip install timidity 95 |
    96 | pip install pickle-mixin 97 |
    98 | pip install music21 99 |
    2. 100 |
  2. 101 | Clone the repository to your system and head over to it
    102 | git clone https://github.com/matakshay/AI_Music_Generator
    103 | cd AI_Music_Generator 104 |
    3. 105 |
  3. 106 | To listen to a music file -
    107 | cd songs
    108 | timidity [filename]
    109 | Replace [filename] with complete name of file you wish to listen 110 |
    4. 111 |
  4. 112 | To generate piano music from a random sequence from the songs/ directory
    113 | python3 generate.py
    114 | This will create a MIDI music file named "output.midi" in the same directory. To listen to this, type
    115 | timidity output.midi 116 |

    117 | This step can be repeated any number of times, and at each iteration a random music file will be generated 118 |
    5. 119 |
120 | 121 |

Acknowledgement

122 |

123 | I referred many articles, blogs and websites while building this project, some of them are mentioned below- 124 |

125 | 133 |

134 | The cover picture at the beginning of this document (just above the title) has been taken from here. 135 |

136 | 137 | -------------------------------------------------------------------------------- /generate.py: -------------------------------------------------------------------------------- 1 | # Generate music using the model trained earlier 2 | 3 | from keras.models import load_model 4 | import numpy as np 5 | import pickle 6 | from music21 import converter, instrument, note, chord, stream 7 | 8 | notes = [] 9 | 10 | with open ("notes", "rb") as file: 11 | notes = pickle.load(file) 12 | 13 | pitch_names = sorted(set(notes)) 14 | 15 | model = load_model("model.hdf5") 16 | 17 | # Create a mapping from int to element 18 | int_to_element = dict ((num, element) for num, element in enumerate (pitch_names)) 19 | element_to_int = dict ((element, num) for num, element in int_to_element.items()) 20 | 21 | sequence_length = 100 22 | 23 | test_input = [] 24 | 25 | for i in range (len(notes) - sequence_length): 26 | seq_inp = notes[i:i+sequence_length] 27 | test_input.append([element_to_int[ch] for ch in seq_inp]) 28 | 29 | vocab_len = 359 30 | 31 | # Randomly select a sequence of notes from test_input 32 | start = np.random.randint(len(test_input)-1) 33 | 34 | 35 | # Feed this to model and get prediction 36 | pattern = test_input[start] 37 | final_prediction = [] 38 | print("Running the model...") 39 | 40 | for note_index in range(200): 41 | pred_inp = np.reshape(pattern, (1, len(pattern), 1)) 42 | inp = pred_inp/float(vocab_len) 43 | 44 | prediction = model.predict(inp, verbose=0) 45 | idx = np.argmax(prediction) 46 | result = int_to_element[idx] 47 | 48 | # Append this predicted note output to final_prediction 49 | final_prediction.append(result) 50 | 51 | # Next input to model should also have same size 52 | # So use this same input, but drop the first note and append the predicted note 53 | pattern = pattern[1:] 54 | pattern.append(idx) 55 | 56 | print("Music generated!") 57 | # Create MIDI files 58 | print("Creating MIDI file...") 59 | 60 | offset = 0 #Time 61 | final_notes = [] 62 | 63 | for pattern in final_prediction: 64 | #If pattern is a chord 65 | if ('+' in pattern ) or pattern.isdigit(): 66 | notes_in_chord = pattern.split('+') 67 | temp_notes = [] 68 | 69 | for curr_note in notes_in_chord: 70 | # For each note in the chord, create a new Note object 71 | new_note = note.Note(int(curr_note)) 72 | new_note.storedInstrument = instrument.Piano() 73 | temp_notes.append(new_note) 74 | 75 | new_chord = chord.Chord(temp_notes) 76 | new_chord.offset = offset 77 | final_notes.append(new_chord) 78 | 79 | #If pattern is a note 80 | else: 81 | curr_note = note.Note(pattern) 82 | curr_note.offset = offset 83 | curr_note.storedInstrument = instrument.Piano() 84 | final_notes.append(curr_note) 85 | 86 | offset += 0.5 87 | 88 | 89 | # Create a stream object from the generated notes 90 | MIDI_stream = stream.Stream(final_notes) 91 | MIDI_stream.write('midi', fp = 'output.mid') 92 | print("Music saved as \"output.mid\" in current directory") 93 | 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