text: import tensorflow as tf
  import os
  import glob
  from tensorflow.python.data.experimental import AUTOTUNE
  
  #downloading the dataset same as used in official paper
  def download_data():
      if not os.path.exists("drums"):
          link = 'http://deepyeti.ucsd.edu/cdonahue/wavegan/data/drums.tar.gz'
          print("downloading Dataset from {}".format(link))
          tf.keras.utils.get_file(
                  'ds.tar.gz', link, cache_subdir="/content/", extract=True)
          os.system('rm ds.tar.gz')
      else:
          print("dataset already exists")
  
  def load_audio(filename,im_shape):
      f = tf.io.read_file(filename)
      data,_ = tf.audio.decode_wav(f)
      data = tf.reshape(data,im_shape)
      return data
  
  #function to get the data pipe ready
  def get_dataset(split='train',batch_size=64,im_shape=(128,128,1)):
      download_data()
      buffer_size = 60000 #as used in google's implementation of DCGAN
      files = glob.glob('drums/'+split+'/*')
      ds = tf.data.Dataset.from_tensor_slices(files)
      ds = ds.map(lambda x: load_audio(x,im_shape), num_parallel_calls=AUTOTUNE)
      ds = ds.shuffle(buffer_size)
      ds = ds.batch(batch_size)
      return ds