# e.g: Conv-Transformer encoder
etype: custom
enc-block-arch:
        - type: conv1d
          idim: 80
          odim: 32
          kernel_size: [3, 7]
          stride: [1, 2]
        - type: conv1d
          idim: 32
          odim: 32
          kernel_size: 3
          stride: 2
        - type: conv1d
          idim: 32
          odim: 384
          kernel_size: 3
          stride: 1
        - type: transformer
          d_hidden: 384
          d_ff: 1536
          heads: 4

# 1D convolution (TDNN) block
- type: conv1d
  idim: [Input dimension. (int)]
  odim: [Output dimension. (int)]
  kernel_size: [Size of the context window. (int or tuple)]
  stride (optional): [Stride of the sliding blocks. (int or tuple, default = 1)]
  dilation (optional): [Parameter to control the stride of elements within the neighborhood. (int or tuple, default = 1)]
  groups (optional): [Number of blocked connections from input channels to output channels. (int, default = 1)
  bias (optional): [Whether to add a learnable bias to the output. (bool, default = True)]
  use-relu (optional): [Whether to use a ReLU activation after convolution. (bool, default = True)]
  use-batchnorm: [Whether to use batch normalization after convolution. (bool, default = False)]
  dropout-rate (optional): [Dropout-rate for TDNN block. (float, default = 0.0)]

# Transformer
- type: transformer
  d_hidden: [Input/output dimension of Transformer block. (int)]
  d_ff: [Hidden dimension of the Feed-forward module. (int)]
  heads: [Number of heads in multi-head attention. (int)]
  dropout-rate (optional): [Dropout-rate for Transformer block. (float, default = 0.0)]
  pos-dropout-rate (optional): [Dropout-rate for positional encoding module. (float, default = 0.0)]
  att-dropout-rate (optional): [Dropout-rate for attention module. (float, default = 0.0)]

# Conformer
- type: conformer
  d_hidden: [Input/output dimension of Conformer block (int)]
  d_ff: [Hidden dimension of the Feed-forward module. (int)]
  heads: [Number of heads in multi-head attention. (int)]
  macaron_style: [Whether to use macaron style. (bool)]
  use_conv_mod: [Whether to use convolutional module. (bool)]
  conv_mod_kernel (required if use_conv_mod = True): [Number of kernel in convolutional module. (int)]
  dropout-rate (optional): [Dropout-rate for Transformer block. (float, default = 0.0)]
  pos-dropout-rate (optional): [Dropout-rate for positional encoding module. (float, default = 0.0)]
  att-dropout-rate (optional): [Dropout-rate for attention module. (float, default = 0.0)]

# Causal Conv1d
- type: causal-conv1d
  idim: [Input dimension. (int)]
  odim: [Output dimension. (int)]
  kernel_size: [Size of the context window. (int)]
  stride (optional): [Stride of the sliding blocks. (int, default = 1)]
  dilation (optional): [Parameter to control the stride of elements within the neighborhood. (int, default = 1)]
  groups (optional): [Number of blocked connections from input channels to output channels. (int, default = 1)
  bias (optional): [Whether to add a learnable bias to the output. (bool, default = True)]
  use-relu (optional): [Whether to use a ReLU activation after convolution. (bool, default = True)]
  use-batchnorm: [Whether to use batch normalization after convolution. (bool, default = False)]
  dropout-rate (optional): [Dropout-rate for TDNN block. (float, default = 0.0)]

# e.g.: 2x (Causal-Conv1d + Transformer) decoder
dtype: transformer
dec-block-arch:
        - type: causal-conv1d
          idim: 256
          odim: 256
          kernel_size: 5
        - type: transformer
          d_hidden: 256
          d_ff: 256
          heads: 4
          dropout-rate: 0.1
          att-dropout-rate: 0.4
dec-block-repeat: 2

# Transducer loss (L1)
transducer-loss-weight: [Weight of the main Transducer loss (float)]

# CTC loss (L2)
use-ctc-loss: True
ctc-loss-weight (optional): [Weight of the CTC loss. (float, default = 0.5)]
ctc-loss-dropout-rate (optional): [Dropout rate for encoder output representation. (float, default = 0.0)]

# Auxiliary Transducer loss (L3)
use-aux-transducer-loss: True
aux-transducer-loss-weight (optional): [Weight of the auxiliary Transducer loss. (float, default = 0.4)]
aux-transducer-loss-enc-output-layers (required if use-aux-transducer-loss = True): [List of intermediate encoder layer IDs to compute auxiliary Transducer loss(es). (list)]
aux-transducer-loss-mlp-dim (optional): [Hidden dimension for the MLP network. (int, default = 320)]
aux-transducer-loss-mlp-dropout-rate: [Dropout rate for the MLP network. (float, default = 0.0)]

# Symmetric KL divergence loss (L4)
# Note: It can be only used in addition to the auxiliary Transducer loss.
use-symm-kl-div-loss: True
symm-kl-div-loss-weight (optional): [Weight of the symmetric KL divergence loss. (float, default = 0.2)]

# LM loss (L5)
use-lm-loss: True
lm-loss-weight (optional): [Weight of the LM loss. (float, default = 0.2)]
lm-loss-smoothing-rate: [Smoothing rate for LM loss. If > 0, label smoothing is enabled. (float, default = 0.0)]

# Default beam search
search-type: default

# Time-synchronous decoding
search-type: tsd
max-sym-exp: [Number of maximum symbol expansions at each time step (int)]

# Alignement-length decoding
search-type: alsd
u-max: [Maximum output sequence length (int)]

# N-step Constrained beam search
search-type: nsc
nstep: [Number of maximum expansion steps at each time step (int)]
        # nstep = max-sym-exp + 1 (blank)
prefix-alpha: [Maximum prefix length in prefix search (int)]

# modified Adaptive Expansion Search
search-type: maes
nstep: [Number of maximum expansion steps at each time step (int, > 1)]
prefix-alpha: [Maximum prefix length in prefix search (int)]
expansion-gamma: [Number of additional candidates in expanded hypotheses selection (int)]
expansion-beta: [Allowed logp difference for prune-by-value method (float, > 0)]