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import tensorflow as tf
from tensorflow.keras.layers import LSTM, Dense, Embedding
from tensorflow.keras.models import Sequential
import numpy as np
class MusicGenerator:
def __init__(self, vocab_size):
self.model = Sequential([
Embedding(vocab_size, 128, input_length=1),
LSTM(256, return_sequences=True),
LSTM(256),
Dense(vocab_size, activation='softmax')
])
self.model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
self.vocab_size = vocab_size
def train(self, sequences, epochs=10):
X = np.array([s[:-1] for s in sequences])
y = np.array([s[1:] for s in sequences])
self.model.fit(X, y, epochs=epochs)
def generate(self, start_sequence, length=10):
result = start_sequence
current_sequence = np.array(start_sequence)
for _ in range(length):
predicted_probs = self.model.predict(current_sequence, verbose=0)[0]
predicted_id = np.random.choice(len(predicted_probs), p=predicted_probs)
result.append(predicted_id)
current_sequence = np.array([predicted_id])
return result
if __name__ == '__main__':
# Example usage
vocab_size = 12 # C, C#, D, ...
generator = MusicGenerator(vocab_size)
# Dummy training data (replace with real data)
sequences = [
[0, 4, 7, 0], # Cmaj -> C
[5, 9, 0, 5] # Fmaj -> F
]
generator.train(sequences)
# Generate a new sequence
start_sequence = [0] # Start with C
generated_sequence = generator.generate(start_sequence)
print("Generated sequence:", generated_sequence)
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