title: Caching a dataset with examples of varied length
body: <p>My dataset is comprised of audio segments of between 5-180 seconds. The number of examples is small enough to allow caching it in memory, instead of reading from the disk over and over. Storing the data in a constant tensor / variable and using <code>tf.train.slice_input_producer</code> will allow me to cache the dataset in memory, but it requires storing all the data in one matrix. Since some examples are much longer than others, this matrix might be unnecessarily large and perhaps too large for the RAM.</p>
  
  <p>I can simply have a list of numpy arrays for my data, and do the whole input reading-randomizing-preprocessing in a non-tensforflow way with a <code>feed_dict</code>, but I wonder if there is a way to do it without completely giving up on tensorflow for the input reading-randomizing-preprocessing part. </p>
  
  <p>Thanks!</p>