Facing an issue while training UNet for Image Segmentation

I am using the keras_unet_collection python library's U-Net model to perform self supervised learning on images (spectrograms; dim=(376, 128)) to learn background/foreground masks. I am very new to self supervised learning but after reading all the related research papers I have created this approach:

• Generate spectrograms from audio files (they are the ground truth)
• Mask random sections of spectrograms (input images)I am trying to train the U-Net model to reconstruct the distorted spectrogram and in the process learn to identify background noise for deniosing the image/audio by classifying each pixel as 0 or 1 (where 0 should be background).

ground truth (y):Image may be NSFW.
Clik here to view.

input image (X):Image may be NSFW.
Clik here to view.

But I have encountered an error when creating the model.

Given a PIL image of a spectrogram (img.size returns (376, 128))This is how I've defined the model:

model = models.unet_2d(input_size=(img.size[0], img.size[1], 3),                        filter_num=[64, 128, 256, 512, 1024],                       stack_num_down=2, stack_num_up=1,                       weights='imagenet', n_labels=1,                       activation='GELU', output_activation='Softmax',                       batch_norm=True, pool='max', unpool='nearest', name='unet')

I get this error:

ValueError: A Concatenate layer requires inputs with matching shapes except for the concatenation axis. Received: input_shape=[(None, 46, 16, 512), (None, 47, 16, 512)]

This is the stack trace:

> keras_unet_collection/_model_unet_2d.py:288X = unet_2d_base(IN, filter_num, stack_num_down=stack_num_down, stack_num_up=stack_num_up,                       activation=activation, batch_norm=batch_norm, pool=pool, unpool=unpool,                       backbone=backbone, weights=weights, freeze_backbone=freeze_backbone,                       freeze_batch_norm=freeze_backbone, name=name)> keras_unet_collection/_model_unet_2d.py:213 X = UNET_right(X, [X_decode[i],], filter_num_decode[i], stack_num=stack_num_up, activation=activation,                         unpool=unpool, batch_norm=batch_norm, name='{}_up{}'.format(name, i))> keras_unet_collection/_model_unet_2d.py:86X = concatenate([X,]+X_list, axis=3, name=name+'_concat')> keras/src/layers/merging/concatenate.py:172return Concatenate(axis=axis, **kwargs)(inputs)

I'm really not able to figure this out. Any help/suggestion would be great!