Incompatible shapes in tensorflow and question regarding the way batching is done by the Keras model fit function (newbie) - numpy

Question

I'm trying to build an audio super-resolution model. The inputs to the model are audio clips of length 256, so the actual shape of one input sample is (256,1), where 256 is the length of the audio clip and 1 is the number of channels. If we also take in consideration the size of a batch (in my case it's 8), the overall shape of the input should be (8, 256, 1).
My issue is that I don't understand what happens when the input and target data are passed as parameters to the model.fit(...) function. If I specify a batch size, does this mean that model.fit() will create the batches automatically? If so, why is there any incompatibility between my input data and the input shape of the model?
This is the error that I get:
WARNING:tensorflow:Model was constructed with shape (8, 256, 1) for input KerasTensor(type_spec=TensorSpec(shape=(8, 256, 1), dtype=tf.float32, name='input_1'), name='input_1', description="created by layer 'input_1'"), but it was called on an input with incompatible shape (None,).
Traceback (most recent call last):
File "/home/calandrinon/Documents/Repos/Audio-Super-Resolution/Project/training.py", line 31, in <module>
model.fit(input_data, target_data,
File "/home/calandrinon/Documents/Repos/Audio-Super-Resolution/Project/venv/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/calandrinon/Documents/Repos/Audio-Super-Resolution/Project/venv/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 1129, in autograph_handler
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
File "/home/calandrinon/Documents/Repos/Audio-Super-Resolution/Project/venv/lib/python3.8/site-packages/keras/engine/training.py", line 1366, in test_function *
return step_function(self, iterator)
File "/home/calandrinon/Documents/Repos/Audio-Super-Resolution/Project/venv/lib/python3.8/site-packages/keras/engine/training.py", line 1356, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/home/calandrinon/Documents/Repos/Audio-Super-Resolution/Project/venv/lib/python3.8/site-packages/keras/engine/training.py", line 1349, in run_step **
outputs = model.test_step(data)
File "/home/calandrinon/Documents/Repos/Audio-Super-Resolution/Project/venv/lib/python3.8/site-packages/keras/engine/training.py", line 1303, in test_step
y_pred = self(x, training=False)
File "/home/calandrinon/Documents/Repos/Audio-Super-Resolution/Project/venv/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/calandrinon/Documents/Repos/Audio-Super-Resolution/Project/venv/lib/python3.8/site-packages/keras/engine/input_spec.py", line 227, in assert_input_compatibility
raise ValueError(f'Input {input_index} of layer "{layer_name}" '
ValueError: Exception encountered when calling layer "model" (type Functional).
Input 0 of layer "conv1d" is incompatible with the layer: expected min_ndim=3, found ndim=1. Full shape received: (None,)
Call arguments received:
• inputs=tf.Tensor(shape=(None,), dtype=string)
• training=False
• mask=None
I do understand that the model needs inputs with 3 dimensions, but why does the error mention that my inputs have 1 dimension when in fact they have 2 dimensions (i.e (256,1)).
Here is the code from the training script:
...
for index in range(0, BATCH_SIZE*NUMBER_OF_BATCHES):
input_data.append(np.load("preprocessed_dataset/low_res/" + input_data_files[index]))
target_data.append(np.load("preprocessed_dataset/high_res/" + target_data_files[index]))
print("Loaded sample {}".format(index))
input_data = np.array(input_data)
target_data = np.array(target_data)
print("Some input tensor shape: {}".format(input_data[0].shape))
print("Some target tensor shape: {}".format(target_data[0].shape))
print("Input data: {}".format(input_data.shape))
print("Target data: {}".format(target_data.shape))
print("Training started...")
model.fit(input_data, target_data,
batch_size=BATCH_SIZE,
epochs=1,
validation_data=(input_validation_files, target_validation_files),
verbose=True)
...and here is the model input shape
...
def create_model(number_of_blocks, batch_size=BATCH_SIZE, input_size=SAMPLE_SIZE):
x = Input((input_size, 1), batch_size=batch_size)
...
Do I actually have to arrange the input samples in batches? Or is that job done by model.fit(...) if I pass a value to the batch_size parameter?
Here is the Github repo, if that helps: https://github.com/Calandrinon/Audio-Super-Resolution/tree/master/Project