new_contents: #!/usr/bin/env python
  # ----------------------------------------------------------------------------
  # Copyright 2016 Nervana Systems Inc.
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ----------------------------------------------------------------------------
  """
  Classify music clips according to genre
  Example dataset can be downloaded from http://marsyasweb.appspot.com/download/data_sets/
  After unpacking the dataset, point the script to the unpacked directory:
  
  ./music_genres.py -e8 -w /path/to/data -r0
  """
  
  import os
  import glob
  import numpy as np
  from neon.util.argparser import NeonArgparser
  from neon.initializers import Gaussian
  from neon.layers import Conv, Pooling, GeneralizedCost, Affine, Dropout
  from neon.optimizers import Adadelta
  from neon.transforms import Rectlin, Softmax, CrossEntropyMulti, Misclassification
  from neon.models import Model
  from neon.data import DataLoader, AudioParams
  from neon.callbacks.callbacks import Callbacks
  
  
  def create_index_files(source_path, train_percent=80, pattern='*'):
      assert os.path.exists(source_path)
      train_idx = os.path.join(source_path, 'train-index.csv')
      val_idx = os.path.join(source_path, 'val-index.csv')
      if os.path.exists(train_idx) and os.path.exists(val_idx):
          return train_idx, val_idx
      subdirs = glob.iglob(os.path.join(source_path, '*'))
      subdirs = filter(lambda x: os.path.isdir(x), subdirs)
      classes = sorted(map(lambda x: os.path.basename(x), subdirs))
      class_map = {key: val for key, val in zip(classes, range(len(classes)))}
  
      # Split into training and validation subsets.
      np.random.seed(0)
      with open(train_idx, 'w') as train_fd, open(val_idx, 'w') as val_fd:
          train_fd.write('filename,label1\n')
          val_fd.write('filename,label1\n')
          for subdir in subdirs:
              label = class_map[os.path.basename(subdir)]
              files = glob.glob(os.path.join(subdir, pattern))
              np.random.shuffle(files)
              train_count = len(files) * train_percent / 100
              for idx, filename in enumerate(files):
                  fd = train_fd if idx < train_count else val_fd
                  rel_path = os.path.join(os.path.basename(subdir),
                                          os.path.basename(filename))
                  fd.write(rel_path + ',' + str(label) + '\n')
      return train_idx, val_idx
  
  parser = NeonArgparser(__doc__)
  args = parser.parse_args()
  train_idx, val_idx = create_index_files(args.data_dir)
  
  common_params = dict(sampling_freq=22050, clip_duration=31000, frame_duration=20)
  train_params = AudioParams(add_noise=True, **common_params)
  val_params = AudioParams(**common_params)
  common = dict(target_size=1, nclasses=10, repo_dir=args.data_dir)
  train = DataLoader(set_name='genres-train', media_params=train_params,
                     index_file=train_idx, shuffle=True, **common)
  val = DataLoader(set_name='genres-val', media_params=val_params,
                   index_file=val_idx, shuffle=False, **common)
  init = Gaussian(scale=0.01)
  layers = [Conv((5, 5, 64), init=init, activation=Rectlin(),
                 strides=dict(str_h=2, str_w=4)),
            Pooling(2, strides=2),
            Conv((5, 5, 128), init=init, batch_norm=True, activation=Rectlin(),
                 strides=dict(str_h=1, str_w=4)),
            Pooling(2, strides=2),
            Conv((3, 3, 256), init=init, batch_norm=True, activation=Rectlin()),
            Pooling(2, strides=2),
            Conv((3, 3, 512), init=init, batch_norm=True, activation=Rectlin()),
            Pooling(2, strides=2),
            Conv((2, 2, 1024), init=init, batch_norm=True, activation=Rectlin()),
            Pooling(2, strides=2),
            Conv((2, 2, 2048), init=init, batch_norm=True, activation=Rectlin()),
            Dropout(),
            Affine(256, init=init, batch_norm=True, activation=Rectlin()),
            Affine(nout=common['nclasses'], init=init, activation=Softmax())]
  
  model = Model(layers=layers)
  opt = Adadelta()
  metric = Misclassification()
  callbacks = Callbacks(model, eval_set=val, metric=metric, **args.callback_args)
  cost = GeneralizedCost(costfunc=CrossEntropyMulti())
  
  model.fit(train, optimizer=opt, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
  print('Misclassification error = %.1f%%' % (model.eval(val, metric=metric)*100))
  
subject: Add example script for audio dataloader