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genetic_descant_generator.py
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genetic_descant_generator.py
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import random
from dataclasses import dataclass
import utils
import music21
# TODO list:
# 1. Revise evaluation functions
# - more complex assessment of rhythmic variety of descant
# - improve speed of counterpoint evaluation
# 2. Allow different key and time signatures
# 3. Allow input of XML file with melody and chord voicings
# - congruence of descant with each voice
@dataclass(frozen=True)
class MelodyData:
"""
A data class representing the data of a melody.
This class encapsulates the details of a melody including its notes, total
duration, and the number of bars. The notes are represented as a list of
tuples, with each tuple containing a pitch and its duration. The total
duration and the number of bars are computed based on the notes provided.
Attributes:
notes (list of tuples): List of tuples representing the melody's notes.
Each tuple is in the format (pitch, duration).
duration (int): Total duration of the melody, computed from notes.
number_of_bars (int): Total number of bars in the melody, computed from
the duration assuming a 4/4 time signature.
Methods:
__post_init__: A method called after the data class initialization to
calculate and set the duration and number of bars based on the
provided notes.
"""
notes: list
duration: int = None # Computed attribute
number_of_bars: int = None # Computed attribute
def __post_init__(self):
object.__setattr__(
self, "duration", sum(duration for _, duration in self.notes)
)
object.__setattr__(self, "number_of_bars", self.duration // 4)
@dataclass(frozen=True)
class ChordData:
"""
A data class representing the data of an accompaniment chord sequence.
This class encapsulates the details of an accompaniment including its chords,
total duration, and the number of bars. The chords are represented as a list of
tuples, with each tuple containing a chord symbol and its duration. The total
duration and the number of bars are computed based on the chord sequence provided.
Attributes:
chords (list of tuples): List of tuples representing the chord sequence.
Each tuple is in the format (chord symbol, duration).
duration (int): Total duration of the accompaniment, computed from chords.
number_of_bars (int): Total number of bars in the accompaniment, computed from
the duration assuming a 4/4 time signature.
Methods:
__post_init__: A method called after the data class initialization to
calculate and set the duration and number of bars based on the
provided chords.
"""
chords: list
duration: int = None # Computed attribute
number_of_bars: int = None # Computed attribute
def __post_init__(self):
object.__setattr__(
self, "duration", sum(duration for _, duration in self.chords)
)
object.__setattr__(self, "number_of_bars", self.duration // 4)
class GeneticDescantGenerator:
"""
Generates a descant/melody for a given chord sequence using a genetic algorithm.
It evolves a population of melodies to find one that best fits the
chord sequence based on a fitness function.
Attributes:
chord_data (ChordData): Data containing accompaniment chords.
notes (list): Available notes for generating descant.
population_size (int): Size of the descant population.
mutation_rate (float): Probability of mutation in the genetic algorithm.
fitness_evaluator (FitnessEvaluator): Instance used to assess fitness.
"""
def __init__(
self,
chord_data,
notes,
population_size,
mutation_rate,
fitness_evaluator,
):
"""
Initializes the generator with chord data, notes, population size,
mutation rate, and a fitness evaluator.
Parameters:
chord_data (ChordData): Accompaniment information.
notes (list): Available notes.
population_size (int): Size of population in the algorithm.
mutation_rate (float): Mutation probability per chord.
fitness_evaluator (FitnessEvaluator): Evaluator for chord fitness.
"""
self.chord_data = chord_data
self.notes = notes
self.mutation_rate = mutation_rate
self.population_size = population_size
self.fitness_evaluator = fitness_evaluator
self._population = []
def generate(self, generations=1000):
"""
Generates a descant for a given chord sequence using a genetic
algorithm.
Parameters:
generations (int): Number of generations for evolution.
Returns:
best_descant (list): Descant with the highest fitness
found in the last generation.
"""
self._population = self._initialise_population()
for x in range(generations):
if (x + 1) % 100 == 0:
print(f"Generation {x + 1} out of {generations}")
parents = self._select_parents()
new_population = self._create_new_population(parents)
self._population = new_population
best_descant = (
self.fitness_evaluator.get_descant_with_highest_fitness(
self._population
)
)
# Print the final descant's scores for each fitness function
print("Best descant fitness scores:")
for func in self.fitness_evaluator.weights:
print(
f"{func}: {getattr(self.fitness_evaluator, f'_{func}')(best_descant)}"
)
return best_descant
def _initialise_population(self):
"""
Initializes population with random notes.
Returns:
list: List of randomly generated note sequences.
"""
return [
self._generate_random_notes()
for _ in range(self.population_size)
]
def _generate_random_notes(self):
"""
Generate a random note sequence with the same duration
as the accompaniment.
Returns:
list: List of randomly generated notes.
"""
note_sequence = []
total_duration = 0
while total_duration < self.chord_data.duration:
note = random.choice(self.notes)
total_duration += note[1]
note_sequence.append(note)
if total_duration > self.chord_data.duration:
note_sequence[-1] = (
note_sequence[-1][0], note_sequence[-1][1] - (total_duration - self.chord_data.duration)
)
return note_sequence
def _select_parents(self):
"""
Selects parent sequences for breeding based on fitness.
Returns:
list: Selected parent note sequences.
"""
fitness_values = [
self.fitness_evaluator.evaluate(seq) for seq in self._population
]
return random.choices(
self._population, weights=fitness_values, k=self.population_size
)
def _create_new_population(self, parents):
"""
Generates a new population of note sequences from the provided parents.
This method creates a new generation of note sequences using crossover
and mutation operations. For each pair of parent note sequences,
it generates two children. Each child is the result of a crossover
operation between the pair of parents, followed by a potential
mutation. The new population is formed by collecting all these
children.
The method ensures that the new population size is equal to the
predefined population size of the generator. It processes parents in
pairs, and for each pair, two children are generated.
Parameters:
parents (list): A list of parent note sequences from which to
generate the new population.
Returns:
list: A new population of note sequences, generated from the
parents.
Note:
This method assumes an even population size and that the number of
parents is equal to the predefined population size.
"""
new_population = []
for i in range(0, self.population_size, 2):
child1, child2 = self._crossover(
parents[i], parents[i + 1]
), self._crossover(parents[i + 1], parents[i])
child1 = self._mutate(child1)
child2 = self._mutate(child2)
new_population.extend([child1, child2])
return new_population
def _crossover(self, parent1, parent2):
"""
Combines two parent sequences into a new child sequence using one-point
crossover.
Parameters:
parent1 (list): First parent note sequence.
parent2 (list): Second parent note sequence.
Returns:
list: Resulting child note sequence.
"""
child = []
total_duration = self.chord_data.duration
cut_duration = random.randint(1, total_duration - 1)
# Count duration in parent 1 until cut_duration is reached
parent1_duration = 0
parent1_index = 0
while parent1_duration < cut_duration:
parent1_duration += parent1[parent1_index][1]
parent1_index += 1
# Extend parent1 notes to child and cap at cut_duration
child.extend(parent1[:parent1_index])
child_duration = sum(duration for _, duration in child)
if child_duration > cut_duration:
# Pitch of last note stays the same, duration decreased by the amount exceeding cut_duration
child[-1] = (child[-1][0], child[-1][1] - (child_duration - cut_duration))
# Count duration in parent 2 until cut_duration is reached
parent2_duration = 0
parent2_index = 0
while parent2_duration < cut_duration:
parent2_duration += parent2[parent2_index][1]
parent2_index += 1
# Extend parent2 notes to child and cap at total_duration
if parent2_duration == cut_duration:
child.extend(parent2[parent2_index:])
else:
child.append((parent2[parent2_index-1][0], parent2_duration - cut_duration))
child.extend(parent2[parent2_index:])
return child
def _mutate(self, note_sequence):
"""
Mutates a note in the sequence based on mutation rate.
Parameters:
note_sequence (list): Note sequence to mutate.
Returns:
list: Mutated note sequence.
"""
if random.random() < self.mutation_rate:
mutation_index = random.randint(0, len(note_sequence) - 1)
note_sequence[mutation_index] = (
random.choice(self.notes)[0], note_sequence[mutation_index][1]
)
return note_sequence
class FitnessEvaluator:
"""
Evaluates the fitness of a note sequence based on various musical criteria.
Attributes:
chords (list): List of tuples representing chords as (chord symbol, duration).
melody (list): List of tuples representing melody notes as (note name, duration).
chord_mappings (dict): Dictionary of chords with their corresponding notes.
notes (list): List of available notes for the descant.
weights (dict): Weights for different fitness evaluation functions.
"""
def __init__(
self, chord_data, melody_data, chord_mappings, notes, weights
):
"""
Initialize the FitnessEvaluator with accompaniment, melody, chord mappings,
notes, and weights.
Parameters:
chord_data (ChordData): Accompaniment information.
melody_data (MelodyData): Melody information.
chord_mappings (dict): Available chords mapped to their notes.
notes (list): Available notes for the descant.
weights (dict): Weights for each fitness evaluation function.
"""
self.chord_data = chord_data
self.melody_data = melody_data
self.chord_mappings = chord_mappings
self.notes = notes
self.weights = weights
def get_descant_with_highest_fitness(self, note_sequences):
"""
Returns the note sequence with the highest fitness score.
Parameters:
note_sequences (list): List of note sequences to evaluate.
Returns:
list: Note sequence with the highest fitness score.
"""
return max(note_sequences, key=self.evaluate)
def evaluate(self, note_sequence):
"""
Evaluate the fitness of a given note sequence.
Parameters:
note_sequence (list): The note sequence to evaluate.
Returns:
float: The overall fitness score of the note sequence.
"""
return sum(
self.weights[func] * getattr(self, f"_{func}")(note_sequence)
for func in self.weights
)
def _chord_descant_congruence(self, note_sequence):
"""
Calculates the congruence between the chord sequence and the descant.
This function assesses how well each chord in the sequence aligns
with the corresponding segment of the descant. The alignment is
measured by checking if the notes in the descant are present in the
chords being played at the same time, rewarding sequences where the
descant notes fit well with the chords.
Parameters:
note_sequence (list): A list of notes to be evaluated against the
accompaniment.
Returns:
float: A score representing the degree of congruence between the
chord sequence and the descant, normalized by the descant's
duration.
"""
score = 0
descant_index, chord_index = 0, 0
descant_time, chord_time = 0, 0
while chord_time < self.chord_data.duration:
descant_note, descant_duration = note_sequence[descant_index]
chord_name, chord_duration = self.chord_data.chords[chord_index]
# Check if the descant note is in the chord
chord_notes = self.chord_mappings.get(chord_name, [])
if descant_note[:-1] in chord_notes:
# reward descant note that is in the chord
score += min(descant_duration, chord_duration)
# Update the indices and durations
# If the descant note ends before the chord, move to the next descant note
if descant_time + descant_duration < chord_time + chord_duration:
descant_time += descant_duration
descant_index += 1
# If the notes end together, advance both indices
elif descant_time + descant_duration == chord_time + chord_duration:
descant_time += descant_duration
descant_index += 1
chord_time += chord_duration
chord_index += 1
# If the chord ends before the descant note, move to the next chord
else:
chord_time += chord_duration
chord_index += 1
assert descant_index == len(note_sequence), "Descant notes not fully evaluated"
assert chord_index == len(self.chord_data.chords), "Chords not fully evaluated"
return score / self.chord_data.duration
def _counterpoint(self, note_sequence):
"""
Measures the quality of counterpoint between the descant and the melody.
Rewards consonances and penalizes dissonances.
Rewards resolution of dissonances on the following beat.
Parameters:
note_sequence (list): A list of notes to be evaluated against the
melody.
Returns:
float: A score representing the degree of congruence between the
melody and the descant, normalized by the duration.
"""
score = 0
melody_index, descant_index = 0, 0
melody_time, descant_time = 0, 0
previous_dissonant = False
prev_overlap_duration = 0.0
while melody_time < self.melody_data.duration:
melody_note, melody_duration = self.melody_data.notes[melody_index]
descant_note, descant_duration = note_sequence[descant_index]
overlap_duration = (
min(melody_time + melody_duration, descant_time + descant_duration) \
- max(melody_time, descant_time)
)
# Find the interval between the current melody and descant notes
melody_pitch = music21.pitch.Pitch(melody_note)
descant_pitch = music21.pitch.Pitch(descant_note)
interval = music21.interval.Interval(melody_pitch, descant_pitch)
# Check if the interval is consonant or dissonant
if interval.isConsonant():
score += overlap_duration # Consonant intervals are preferred
if previous_dissonant:
# Reward resolution of dissonance
# Add prev_overlap_duration to correct for previously penalized dissonance
score += prev_overlap_duration
previous_dissonant = False
else:
previous_dissonant = True
score -= overlap_duration # Penalize dissonant intervals
prev_overlap_duration = overlap_duration
# Update the indices and durations
# If the descant note ends before the melody note,
# move to the next descant note
if descant_time + descant_duration < melody_time + melody_duration:
descant_time += descant_duration
descant_index += 1
# If the notes end together, advance both indices
elif descant_time + descant_duration == melody_time + melody_duration:
descant_time += descant_duration
descant_index += 1
melody_time += melody_duration
melody_index += 1
# If the melody note ends before the descant note,
# move to the next melody note
else:
melody_time += melody_duration
melody_index += 1
assert descant_index == len(note_sequence), "Descant notes not fully evaluated"
assert melody_index == len(self.melody_data.notes), "Melody notes not fully evaluated"
return score / self.melody_data.duration # Normalize by total duration
def _pitch_variety(self, note_sequence):
"""
Evaluates the diversity of pitches used in the sequence. This function
calculates a score based on the number of unique pitches present in the
sequence compared to the total available pitches. Higher variety in the
note sequence results in a higher score, promoting musical
complexity and interest.
Parameters:
note_sequence (list): The note sequence to evaluate.
Returns:
float: A normalized score representing the variety of pitches in the
sequence relative to the total number of available pitches.
"""
unique_pitches = len(set([pitch for pitch, _ in note_sequence]))
total_pitches = len(set([pitch for pitch, _ in self.notes]))
return unique_pitches / total_pitches
def _rhythmic_variety(self, note_sequence):
"""
Evaluates the diversity of rhythms used in the sequence. This function
calculates a score based on the number of unique rhythms present in the
sequence. Higher variety in the note sequence results in a higher score,
promoting musical complexity and interest.
Parameters:
note_sequence (list): The note sequence to evaluate.
Returns:
float: A normalized score representing the variety of rhythms in the
sequence.
"""
# TODO: Do something different with rhythmic evaluation
# Variety between melody and descant rhythms,
# or variety in rhythm from one note to the next
unique_rhythms = len(set(duration for _, duration in note_sequence))
total_rhythms = len(set(duration for _, duration in self.notes))
return unique_rhythms / total_rhythms
def _voice_leading(self, note_sequence):
"""
Assesses the voice leading of the note sequence by examining the
transitions between successive notes. This function scores the
sequence based on how frequently the note transitions align with
voice leading rules. Smooth and musically pleasant
transitions result in a higher score.
Parameters:
note_sequence (list): The note sequence to evaluate.
Returns:
float: A normalized score based on the frequency of preferred note
transitions in the sequence.
"""
score = 0
for i in range(len(note_sequence) - 1):
current_note = note_sequence[i][0]
next_note = note_sequence[i + 1][0]
current_pitch = music21.pitch.Pitch(current_note)
next_pitch = music21.pitch.Pitch(next_note)
interval = music21.interval.Interval(current_pitch, next_pitch)
if (current_note[0] == 'B' and interval.directedName == 'm2'):
# reward leading tone resolution
score += 1
elif interval.name in ["m2", "M2", "m3", "M3"]:
# reward stepwise motion and motion by thirds
score += 1
elif interval.name == "P1":
# small penalty for repeated note
score -= 0.5
else:
# penalize leaps over a 3rd
score -= 1
return score / (len(note_sequence) - 1)
def _functional_harmony(self, note_sequence):
"""
Evaluates the note sequence based on principles of functional harmony.
This function checks for the presence of key harmonic functions such as
notes from the tonic chord at the beginning and end of the sequence.
Adherence to these harmonic conventions is rewarded with a higher score.
Parameters:
note_sequence (list): The note sequence to evaluate.
Returns:
float: A score representing the extent to which the sequence
adheres to traditional functional harmony, normalized by
the number of checks performed.
"""
score = 0
if note_sequence[0][0][:-1] in ["C", "E", "G"]:
score += 1
if note_sequence[-1][0][:-1] in ["C", "E", "G"]:
score += 1
return score / 2
def create_score(descant, melody, chord_sequence, chord_mappings, instrument="violin"):
"""
Create a music21 score with a given descant, melody, and chord sequence.
Args:
descant (list): A list of tuples representing notes in the format
(note_name, duration).
melody (list): A list of tuples representing notes in the format
(note_name, duration).
chord_sequence (list): A list of tuples representing chords
in the format (chord_symbol, duration).
chord_mappings (dict): Available chords mapped to their notes.
instrument (str): The instrument to use for the descant.
Returns:
music21.stream.Score: A music score containing the descant and chord
sequence.
"""
# Create a Score object
score = music21.stream.Score()
# Create the descant part and add notes to it
descant_part = music21.stream.Part()
descant_part.append(music21.instrument.fromString(instrument))
clef = music21.clef.TrebleClef() if instrument == "violin" else music21.clef.AltoClef()
descant_part.append(clef)
for note_name, duration in descant:
descant_note = music21.note.Note(note_name, quarterLength=duration)
descant_part.append(descant_note)
# Create the melody part and add notes to it
melody_part = music21.stream.Part()
melody_part.append(music21.instrument.Vocalist())
for note_name, duration in melody:
melody_note = music21.note.Note(note_name, quarterLength=duration)
melody_part.append(melody_note)
# Create the chord part and add chords to it
chord_part = music21.stream.Part()
current_duration = 0 # Track the duration for chord placement
for chord_name, duration in chord_sequence:
# Translate chord names to note lists
chord_notes_list = chord_mappings.get(chord_name, [])
# Create a music21 chord
chord_notes = music21.chord.Chord(
chord_notes_list, quarterLength=duration
) # Assuming 4/4 time signature
chord_notes.offset = current_duration
chord_part.append(chord_notes)
current_duration += duration # Increase by 4 beats
# Append parts to the score
score.append(descant_part)
score.append(melody_part)
score.append(chord_part)
return score
def main():
jesus_loves_me_chords = [
("C", 4),
("C", 4),
("F", 4),
("C", 4),
("C", 4),
("C", 4),
("F", 2),
("C", 2),
("G", 2),
("C", 2),
("C", 4),
("F", 4),
("C", 4),
("G", 4),
("C", 4),
("F", 4),
("C", 2),
("G", 2),
("C", 4)
]
jesus_loves_me_melody = [
("G4", 1),
("E4", 1),
("E4", 1),
("D4", 1),
("E4", 1),
("G4", 1),
("G4", 2),
("A4", 1),
("A4", 1),
("C5", 1),
("A4", 1),
("A4", 1),
("G4", 1),
("G4", 2),
("G4", 1),
("E4", 1),
("E4", 1),
("D4", 1),
("E4", 1),
("G4", 1),
("G4", 2),
("A4", 1),
("A4", 1),
("G4", 1),
("C4", 1),
("E4", 1),
("D4", 1),
("C4", 2),
("G4", 2),
("E4", 1),
("G4", 1),
("A4", 1),
("C5", 3),
("G4", 2),
("E4", 1),
("C4", 1),
("E4", 1),
("D4", 3),
("G4", 2),
("E4", 1),
("G4", 1),
("A4", 1),
("C5", 2),
("A4", 1),
("G4", 1),
("C4", 1),
("E4", 1),
("D4", 1),
("C4", 4),
]
joy_to_the_world_chords = [
("C", 1),
("G/C", 0.75),
("F/C", 0.25),
("C", 1),
("Dm", 1),
("C", 1),
("G7", 1),
("C", 1.5),
("C/E", 0.5),
("F", 2),
("G", 2),
("C", 2),
("C", 1),
("F/C", 0.5),
("C", 0.5),
("C", 1),
("C", 1),
("C", 1),
("F/C", 0.5),
("C", 0.5),
("C", 1),
("C", 1),
("C", 1),
("C", 1),
("C", 2),
("G", 1),
("G", 1),
("G7", 1.5),
("C/G", 0.25),
("G", 0.25),
("C", 1.5),
("F/C", 0.5),
("C", 1),
("C", 0.5),
("Dm", 0.5),
("C/G", 1),
("G7", 1),
("C", 4)
]
joy_to_the_world_melody = [
("C5", 1),
("B4", 0.75),
("A4", 0.25),
("G4", 1.5),
("F4", 0.5),
("E4", 1),
("D4", 1),
("C4", 1.5),
("G4", 0.5),
("A4", 1.5),
("A4", 0.5),
("B4", 1.5),
("B4", 0.5),
("C4", 1.5),
("C4", 0.5),
("C4", 0.5),
("B4", 0.5),
("A4", 0.5),
("G4", 0.5),
("G4", 0.75),
("F4", 0.25),
("E4", 0.5),
("C4", 0.5),
("C4", 0.5),
("B4", 0.5),
("A4", 0.5),
("G4", 0.5),
("G4", 0.75),
("F4", 0.25),
("E4", 0.5),
("E4", 0.5),
("E4", 0.5),
("E4", 0.5),
("E4", 0.5),
("E4", 0.25),
("F4", 0.25),
("G4", 1.5),
("F4", 0.25),
("E4", 0.25),
("D4", 0.5),
("D4", 0.5),
("D4", 0.5),
("D4", 0.25),
("E4", 0.25),
("F4", 1.5),
("E4", 0.25),
("D4", 0.25),
("C4", 0.5),
("C5", 1),
("A4", 0.5),
("G4", 0.75),
("F4", 0.25),
("E4", 0.5),
("F4", 0.5),
("E4", 1),
("D4", 1),
("C4", 4)
]
weights = utils.get_weights_from_user()
chord_mappings = {
"C": ["C", "E", "G"],
"C/E": ["E", "G", "C"],
"C/G": ["G", "C", "E"],
"Dm": ["D", "F", "A"],
"Em": ["E", "G", "B"],
"F": ["F", "A", "C"],
"F/C": ["C", "F", "A"],
"G": ["G", "B", "D"],
"G/C": ["C", "G", "B"],
"G7": ["G", "B", "D", "F"],
"Am": ["A", "C", "E"],
"Bdim": ["B", "D", "F"]
}
# Choose Violin or Viola
# instrument = "viola"
instrument = "violin"
range = (60,84) if instrument == "violin" else (48,72)
note_bank = utils.generate_notes(range, chromatic=False)
# Choose which tune
chords = joy_to_the_world_chords
melody = joy_to_the_world_melody
# chords = jesus_loves_me_chords
# melody = jesus_loves_me_melody
# Instantiate objects for generating harmonization
chord_data = ChordData(chords)
melody_data = MelodyData(melody)
assert chord_data.duration == melody_data.duration, "Chord and melody durations must match"
fitness_evaluator = FitnessEvaluator(
chord_data=chord_data,
melody_data=melody_data,
chord_mappings=chord_mappings,
notes=note_bank,
weights=weights,
)
generator = GeneticDescantGenerator(
chord_data=chord_data,
notes=note_bank,
population_size=100,
mutation_rate=0.05,
fitness_evaluator=fitness_evaluator,
)
# Generate descant with genetic algorithm
generated_descant = generator.generate(generations=500)
# Render to music21 score and show it
music21_score = create_score(
generated_descant, melody, chords, chord_mappings, instrument=instrument
)
music21_score.show()
if __name__ == "__main__":
main()