How to write flow function without memory leaks in tensorflow

Question

I am trying to compete in kaggle's cornell birdcall detection challenge and there is in total 23 gb of data which mainly composed as mp3 sound files. As you may know 23 gb of data is impossible to fit into the RAM kaggle or google colab. Therefore, I tried to write a datagenerator to fetch mp3 files while training my model and convert them in order to prevent out of memory issue. However, I am still getting out of memory issue after first few epochs. Below you can find my generator and training code where I use del command to specifically de-allocate objects from memory but apparently I did something wrong. Is there any resource you can suggest for that or any suggestion to improve my code to prevent memory leak? Calling garbage collector makes no difference too.

Thx

My datagenerator code

from tensorflow import keras
import random
import glob
import gc

class My_Custom_Generator(keras.utils.Sequence):
    def __init__(self, batch_size):
        files = glob.glob("../input/birdsong-recognition/train_audio/*/*.mp3")
        random.shuffle(files)
        self.files = files
        self.batch_size = batch_size
    

    def __len__(self) :
        return (np.ceil(len(self.files) / float(self.batch_size))).astype(np.int)
  
  
    def __getitem__(self, idx) :
        gc.collect(2)
        
        batch_x = self.files[idx * self.batch_size : (idx+1) * self.batch_size]
        #batch_y = self.labels[idx * self.batch_size : (idx+1) * self.batch_size]

        train_image = []
        train_label = []

        for i in range(0, len(batch_x)):
            image, label = get_data(batch_x[i])
            image = tf.convert_to_tensor(image)
            label_matrix = get_cat_label(label)
            
            train_image.append(image)
            train_label.append(label_matrix)
            
        self.train_image = np.array(train_image)
        self.train_label = np.array(train_label)
        del train_image
        del train_label
        return self.train_image, self.train_label

My training loop which I got from tensorflow tutorial and edited

 ## Note: Rerunning this cell uses the same model variables

# Keep results for plotting
train_loss_results = []
train_accuracy_results = []

num_epochs = int(len(glob.glob("../input/birdsong-recognition/train_audio/*/*.mp3")) // 8)

for epoch in range(num_epochs):
    epoch_loss_avg = tf.keras.metrics.Mean()
    epoch_accuracy = tf.keras.metrics.CategoricalAccuracy()
    
    imgs, labels = my_training_batch_generator.__getitem__(epoch)
    
    # Training loop - using batches of 32
    for i in range(1):
        # Optimize the model
        loss_value, grads = grad(xceptionModel, imgs, labels)
        optimizer.apply_gradients(zip(grads, xceptionModel.trainable_variables))

        # Track progress
        epoch_loss_avg.update_state(loss_value)  # Add current batch loss
        # Compare predicted label to actual label
        # training=True is needed only if there are layers with different
        # behavior during training versus inference (e.g. Dropout).
        epoch_accuracy.update_state(labels, xceptionModel(imgs, training=True))

    del imgs
    del labels
      # End epoch
    train_loss_results.append(epoch_loss_avg.result())
    train_accuracy_results.append(epoch_accuracy.result())

    if epoch % 2 == 0:
        print("Epoch {:03d}: Loss: {:.3f}, Accuracy: {:.3%}".format(epoch,
                                                                    epoch_loss_avg.result(),
                                                                epoch_accuracy.result()))