def transformer_librispeech_v2():
  """HParams for training ASR model on LibriSpeech V2."""
  hparams = transformer_base()

  hparams.max_length = 1240000
  hparams.max_input_seq_length = 1550
  hparams.max_target_seq_length = 350
  hparams.batch_size = 16
  hparams.num_decoder_layers = 4
  hparams.num_encoder_layers = 6
  hparams.hidden_size = 384
  hparams.learning_rate = 0.15
  hparams.daisy_chain_variables = False
  hparams.filter_size = 1536
  hparams.num_heads = 2
  hparams.ffn_layer = "conv_relu_conv"
  hparams.conv_first_kernel = 9
  hparams.weight_decay = 0
  hparams.layer_prepostprocess_dropout = 0.2
  hparams.relu_dropout = 0.2

  return hparams
def transformer_librispeech_v2 ( ) : hparams = transformer_base ( ) hparams . max_length = 1240000 hparams . max_input_seq_length = 1550 hparams . max_target_seq_length = 350 hparams . batch_size = 16 hparams . num_decoder_layers = 4 hparams . num_encoder_layers = 6 hparams . hidden_size = 384 hparams . learning_rate = 0.15 hparams . daisy_chain_variables = False hparams . filter_size = 1536 hparams . num_heads = 2 hparams . ffn_layer = "conv_relu_conv" hparams . conv_first_kernel = 9 hparams . weight_decay = 0 hparams . layer_prepostprocess_dropout = 0.2 hparams . relu_dropout = 0.2 return hparams
HParams for training ASR model on LibriSpeech V2.