text: # model settings
  model = dict(
      type='AudioRecognizer',
      backbone=dict(type='ResNet', depth=50, in_channels=1, norm_eval=False),
      cls_head=dict(
          type='AudioTSNHead',
          num_classes=400,
          in_channels=2048,
          dropout_ratio=0.5,
          init_std=0.01))
  # model training and testing settings
  train_cfg = None
  test_cfg = dict(average_clips='prob')
  # dataset settings
  dataset_type = 'AudioDataset'
  data_root = 'data/kinetics400/audios'
  data_root_val = 'data/kinetics400/audios'
  ann_file_train = 'data/kinetics400/kinetics400_train_list_audio.txt'
  ann_file_val = 'data/kinetics400/kinetics400_val_list_audio.txt'
  ann_file_test = 'data/kinetics400/kinetics400_val_list_audio.txt'
  train_pipeline = [
      dict(type='AudioDecodeInit'),
      dict(type='SampleFrames', clip_len=64, frame_interval=1, num_clips=1),
      dict(type='AudioDecode'),
      dict(type='AudioAmplify', ratio=1.5),
      dict(type='MelLogSpectrogram'),
      dict(type='FormatAudioShape', input_format='NCTF'),
      dict(type='Collect', keys=['audios', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['audios'])
  ]
  val_pipeline = [
      dict(type='AudioDecodeInit'),
      dict(
          type='SampleFrames',
          clip_len=64,
          frame_interval=1,
          num_clips=1,
          test_mode=True),
      dict(type='AudioDecode'),
      dict(type='AudioAmplify', ratio=1.5),
      dict(type='MelLogSpectrogram'),
      dict(type='FormatAudioShape', input_format='NCTF'),
      dict(type='Collect', keys=['audios', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['audios'])
  ]
  test_pipeline = [
      dict(type='AudioDecodeInit'),
      dict(
          type='SampleFrames',
          clip_len=64,
          frame_interval=1,
          num_clips=1,
          test_mode=True),
      dict(type='AudioDecodeInit'),
      dict(type='AudioAmplify', ratio=1.5),
      dict(type='MelLogSpectrogram'),
      dict(type='FormatAudioShape', input_format='NCTF'),
      dict(type='Collect', keys=['audios', 'label'], meta_keys=[]),
      dict(type='ToTensor', keys=['audios'])
  ]
  data = dict(
      videos_per_gpu=320,
      workers_per_gpu=4,
      train=dict(
          type=dataset_type,
          ann_file=ann_file_train,
          data_prefix=data_root,
          pipeline=train_pipeline),
      val=dict(
          type=dataset_type,
          ann_file=ann_file_val,
          data_prefix=data_root_val,
          pipeline=val_pipeline),
      test=dict(
          type=dataset_type,
          ann_file=ann_file_test,
          data_prefix=data_root_val,
          pipeline=test_pipeline))
  # optimizer
  optimizer = dict(
      type='SGD', lr=0.1, momentum=0.9,
      weight_decay=0.0001)  # this lr is used for 8 gpus
  optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
  # learning policy
  lr_config = dict(policy='CosineAnnealing', min_lr=0)
  total_epochs = 100
  checkpoint_config = dict(interval=5)
  evaluation = dict(
      interval=5, metrics=['top_k_accuracy', 'mean_class_accuracy'])
  log_config = dict(
      interval=20,
      hooks=[
          dict(type='TextLoggerHook'),
          # dict(type='TensorboardLoggerHook'),
      ])
  # runtime settings
  dist_params = dict(backend='nccl')
  log_level = 'INFO'
  work_dir = './work_dirs/tsn_r50_64x1x1_100e_kinetics400_audio/'
  load_from = None
  resume_from = None
  workflow = [('train', 1)]
  