text: from backbones import iresnet34
  from dataset import voxceleb1_collate_fn
  from models.voice import VoiceStyleNet
  
  exp_name = 'voice_style_encoder'
  exp_path = './experiments'
  batch_size = 32
  sample_num = 4
  
  dataset_config = {
      'root_path': '/home/aitical/data/voxceleb/voxceleb/train',
      # voice dataset
      'voice_ext': 'npy',
      'img_ext': 'jpg',
      'batch_size': 64,
      'voice_frame': [300, 800],
      'num_workers': 8,
      'collate_fn': voxceleb1_collate_fn,
      'sample_num': 2,
      'iters': 50000,
      # test data
      'test_path': '/home/aitical/data4t2/voxceleb/test'
  }
  
  voice_encoder = dict(
      model=VoiceStyleNet,
      params=dict(
          input_channel=64,
          output_channel=64,
          channels=[256, 384, 576, 864]
      ),
      pretrained=False,
      model_path='-'
  )
  
  arcface = dict(
      model=iresnet34,
      params=dict(
          fp16=False,
      ),
      pretrained=True,
      model_path='./pretrained_models/arc_face_model/backbone.pth'
  )
  
  
  training_config = dict(
      optimizer=dict(
          lr=2e-3,
          betas=(0.5, 0.999)
      ),
      multi_gpu=True,
  )
  