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# This is the hyperparameter configuration file for Tacotron2 v1.
# Please make sure this is adjusted for the synpaflex dataset. If you want to
# apply to the other dataset, you might need to carefully change some parameters.
# This configuration performs 200k iters but 65k iters is enough to get a good models.
###########################################################
# FEATURE EXTRACTION SETTING #
###########################################################
hop_size: 256 # Hop size.
format: "npy"
###########################################################
# NETWORK ARCHITECTURE SETTING #
###########################################################
model_type: "tacotron2"
tacotron2_params:
dataset: synpaflex
embedding_hidden_size: 512
initializer_range: 0.02
embedding_dropout_prob: 0.1
n_speakers: 1
n_conv_encoder: 5
encoder_conv_filters: 512
encoder_conv_kernel_sizes: 5
encoder_conv_activation: 'relu'
encoder_conv_dropout_rate: 0.5
encoder_lstm_units: 256
n_prenet_layers: 2
prenet_units: 256
prenet_activation: 'relu'
prenet_dropout_rate: 0.5
n_lstm_decoder: 1
reduction_factor: 1
decoder_lstm_units: 1024
attention_dim: 128
attention_filters: 32
attention_kernel: 31
n_mels: 80
n_conv_postnet: 5
postnet_conv_filters: 512
postnet_conv_kernel_sizes: 5
postnet_dropout_rate: 0.1
attention_type: "lsa"
###########################################################
# DATA LOADER SETTING #
###########################################################
batch_size: 32 # Batch size for each GPU with assuming that gradient_accumulation_steps == 1.
remove_short_samples: true # Whether to remove samples the length of which are less than batch_max_steps.
allow_cache: true # Whether to allow cache in dataset. If true, it requires cpu memory.
mel_length_threshold: 32 # remove all targets has mel_length <= 32
is_shuffle: true # shuffle dataset after each epoch.
use_fixed_shapes: true # use_fixed_shapes for training (2x speed-up)
# refer (https://github.com/dathudeptrai/TensorflowTTS/issues/34#issuecomment-642309118)
###########################################################
# OPTIMIZER & SCHEDULER SETTING #
###########################################################
optimizer_params:
initial_learning_rate: 0.001
end_learning_rate: 0.00001
decay_steps: 150000 # < train_max_steps is recommend.
warmup_proportion: 0.02
weight_decay: 0.001
gradient_accumulation_steps: 1
var_train_expr: null # trainable variable expr (eg. 'embeddings|decoder_cell' )
# must separate by |. if var_train_expr is null then we
# training all variables.
###########################################################
# INTERVAL SETTING #
###########################################################
train_max_steps: 200000 # Number of training steps.
save_interval_steps: 2000 # Interval steps to save checkpoint.
eval_interval_steps: 500 # Interval steps to evaluate the network.
log_interval_steps: 200 # Interval steps to record the training log.
start_schedule_teacher_forcing: 200001 # don't need to apply schedule teacher forcing.
start_ratio_value: 0.5 # start ratio of scheduled teacher forcing.
schedule_decay_steps: 50000 # decay step scheduled teacher forcing.
end_ratio_value: 0.0 # end ratio of scheduled teacher forcing.
###########################################################
# OTHER SETTING #
###########################################################
num_save_intermediate_results: 1 # Number of results to be saved as intermediate results. |