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PaddleSpeech/examples/ljspeech/tts0/conf/default.yaml

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# This configuration is for Paddle to train Tacotron 2. Compared to the
# original paper, this configuration additionally use the guided attention
# loss to accelerate the learning of the diagonal attention. It requires
# only a single GPU with 12 GB memory and it takes ~1 days to finish the
# training on Titan V.
###########################################################
# FEATURE EXTRACTION SETTING #
###########################################################
fs: 22050 # Sampling rate.
n_fft: 1024 # FFT size (samples).
n_shift: 256 # Hop size (samples). 11.6ms
win_length: null # Window length (samples).
# If set to null, it will be the same as fft_size.
window: "hann" # Window function.
n_mels: 80 # Number of mel basis.
fmin: 80 # Minimum freq in mel basis calculation. (Hz)
fmax: 7600 # Maximum frequency in mel basis calculation. (Hz)
###########################################################
# DATA SETTING #
###########################################################
batch_size: 64
num_workers: 2
###########################################################
# MODEL SETTING #
###########################################################
model: # keyword arguments for the selected model
embed_dim: 512 # char or phn embedding dimension
elayers: 1 # number of blstm layers in encoder
eunits: 512 # number of blstm units
econv_layers: 3 # number of convolutional layers in encoder
econv_chans: 512 # number of channels in convolutional layer
econv_filts: 5 # filter size of convolutional layer
atype: location # attention function type
adim: 512 # attention dimension
aconv_chans: 32 # number of channels in convolutional layer of attention
aconv_filts: 15 # filter size of convolutional layer of attention
cumulate_att_w: True # whether to cumulate attention weight
dlayers: 2 # number of lstm layers in decoder
dunits: 1024 # number of lstm units in decoder
prenet_layers: 2 # number of layers in prenet
prenet_units: 256 # number of units in prenet
postnet_layers: 5 # number of layers in postnet
postnet_chans: 512 # number of channels in postnet
postnet_filts: 5 # filter size of postnet layer
output_activation: null # activation function for the final output
use_batch_norm: True # whether to use batch normalization in encoder
use_concate: True # whether to concatenate encoder embedding with decoder outputs
use_residual: False # whether to use residual connection in encoder
dropout_rate: 0.5 # dropout rate
zoneout_rate: 0.1 # zoneout rate
reduction_factor: 1 # reduction factor
spk_embed_dim: null # speaker embedding dimension
###########################################################
# UPDATER SETTING #
###########################################################
updater:
use_masking: True # whether to apply masking for padded part in loss calculation
bce_pos_weight: 5.0 # weight of positive sample in binary cross entropy calculation
use_guided_attn_loss: True # whether to use guided attention loss
guided_attn_loss_sigma: 0.4 # sigma of guided attention loss
guided_attn_loss_lambda: 1.0 # strength of guided attention loss
##########################################################
# OPTIMIZER SETTING #
##########################################################
optimizer:
optim: adam # optimizer type
learning_rate: 1.0e-03 # learning rate
epsilon: 1.0e-06 # epsilon
weight_decay: 0.0 # weight decay coefficient
###########################################################
# TRAINING SETTING #
###########################################################
max_epoch: 300
num_snapshots: 5
###########################################################
# OTHER SETTING #
###########################################################
seed: 42