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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 How to use Kapre - example
Step1:  Loading an mp3 file
Step2:  Trim it and make it a 2d.
If your file is mono, librosa.load returns a 1D array. Kapre always expects 2d array, so make it 2d.
On my computer, I use default image_data_format == 'channels_last'. I'll keep it in that way for the audio data, too.
Step3:  Let's make it a batch of 4 items
to make it more like a proper dataset. You should have many files indeed.
Step4:  A Keras model
A simple model with 10-class and single-label classification.
Step5:  The model has no trainable parameters because STFT layer uses tf.signal.stft() function which is just an implementation of FFT-based short-time Fourier transform.
The output shape is (batch, time, frequency, channels). 
42 (time) is the number of STFT frames. A shorter hop length would make it (nearly) proportionally longer. If pad_end=True, the input audio signals become a little longer, hence the number of frames would get longer, too.
1025 is the number of STFT bins and decided as n_fft // 2 + 1. 
1 channel
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Python Code:
import librosa
import kapre
import tensorflow as tf
from tensorflow.keras.models import Sequential
import numpy as np
from datetime import datetime
now = datetime.now()
print('%s/%s/%s' % (now.year, now.month, now.day))
print('Tensorflow: {}'.format(tf.__version__))
print('Librosa: {}'.format(librosa.__version__))
print('Image data format: {}'.format(tf.keras.backend.image_data_format()))
print('Kapre: {}'.format(kapre.__version__))
Explanation: How to use Kapre - example
End of explanation
src, sr = librosa.load('../srcs/bensound-cute.mp3', sr=None, mono=True)
print('Audio length: %d samples, %04.2f seconds. \n' % (len(src), len(src) / sr) +
      'Audio sample rate: %d Hz' % sr)
Explanation: Loading an mp3 file
End of explanation
len_second = 1.0 # Let's trim it to make it quick
src = src[:int(sr*len_second)]
src = np.expand_dims(src, axis=1)
input_shape = src.shape
print('The shape of an item', input_shape)
Explanation: Trim it and make it a 2d.
If your file is mono, librosa.load returns a 1D array. Kapre always expects 2d array, so make it 2d.
On my computer, I use default image_data_format == 'channels_last'. I'll keep it in that way for the audio data, too.
End of explanation
x = np.array([src] * 4)
print('The shape of a batch: ',x.shape)
Explanation: Let's make it a batch of 4 items
to make it more like a proper dataset. You should have many files indeed.
End of explanation
from kapre.time_frequency import STFT, Magnitude, MagnitudeToDecibel
model = Sequential()
# A STFT layer
model.add(STFT(n_fft=2048, win_length=2018, hop_length=1024,
               window_fn=None, pad_end=False,
               input_data_format='channels_last', output_data_format='channels_last',
               input_shape=input_shape,
              name='stft-layer'))
model.summary()
Explanation: A Keras model
A simple model with 10-class and single-label classification.
End of explanation
from tensorflow.keras.layers import Conv2D, BatchNormalization, ReLU, GlobalAveragePooling2D, Dense, Softmax
model.add(Magnitude())
model.add(MagnitudeToDecibel())
model.add(Conv2D(32, (3, 3), strides=(2, 2)))
model.add(BatchNormalization())
model.add(ReLU())
model.add(GlobalAveragePooling2D())
model.add(Dense(10))
model.add(Softmax())
# Compile the model
model.compile('adam', 'categorical_crossentropy') # if single-label classification
model.summary()
Explanation: The model has no trainable parameters because STFT layer uses tf.signal.stft() function which is just an implementation of FFT-based short-time Fourier transform.
The output shape is (batch, time, frequency, channels). 
42 (time) is the number of STFT frames. A shorter hop length would make it (nearly) proportionally longer. If pad_end=True, the input audio signals become a little longer, hence the number of frames would get longer, too.
1025 is the number of STFT bins and decided as n_fft // 2 + 1. 
1 channel: because the input signal was single-channel.
The output of STFT layer is complex number.
Let's add more layers like a real model!
End of explanation
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