Is there any other platform which will process the data speedly?

THIS IS THE LINE

model.compile(loss=['binary_crossentropy', 'mae'], optimizer='adam', metrics=['accuracy'])

In recent updates of keras and tensorflow it is asking for atleast 2 metrics as we are predicting for age and gender.
So Please check accordingly.

While training the model,

train the model

epochs = 20
batch_size = 32
steps = len(train) // batch_size

for i in range(epochs):
# create data generator
generator = data_generator(train, mapping, features, tokenizer, max_length, vocab_size, batch_size)
# fit for one epoch
model.fit(generator, epochs=1, steps_per_epoch=steps, verbose=1)

Getting error:

TypeError Traceback (most recent call last)
Cell In[28], line 10
8 generator = data_generator(train, mapping, features, tokenizer, max_length, vocab_size, batch_size)
9 # fit for one epoch
---> 10 model.fit(generator, epochs=1, steps_per_epoch=1, verbose=1)

File /opt/conda/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:123, in filter_traceback..error_handler(*args, **kwargs)
120 filtered_tb = _process_traceback_frames(e.traceback)
121 # To get the full stack trace, call:
122 # keras.config.disable_traceback_filtering()
--> 123 raise e.with_traceback(filtered_tb) from None
124 finally:
125 del filtered_tb

File /opt/conda/lib/python3.10/site-packages/tensorflow/python/data/ops/from_generator_op.py:124, in _from_generator(generator, output_types, output_shapes, args, output_signature, name)
122 for spec in nest.flatten(output_signature):
123 if not isinstance(spec, type_spec.TypeSpec):
--> 124 raise TypeError(f"output_signature must contain objects that are "
125 f"subclass of tf.TypeSpec but found {type(spec)} "
126 f"which is not.")
127 else:
128 if output_types is None:

TypeError: output_signature must contain objects that are subclass of tf.TypeSpec but found <class 'list'> which is not.