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# This is the hyperparameter configuration file for FastSpeech v3.
# Please make sure this is adjusted for the LJSpeech dataset. If you want to
# apply to the other dataset, you might need to carefully change some parameters.
# This configuration performs 200k iters but a best checkpoint is around 150k iters.
###########################################################
# FEATURE EXTRACTION SETTING #
###########################################################
hop_size: 256 # Hop size.
format: "npy"
###########################################################
# NETWORK ARCHITECTURE SETTING #
###########################################################
model_type: "fastspeech"
fastspeech_params:
n_speakers: 1
encoder_hidden_size: 384
encoder_num_hidden_layers: 4
encoder_num_attention_heads: 2
encoder_attention_head_size: 192 # hidden_size // num_attention_heads
encoder_intermediate_size: 1024
encoder_intermediate_kernel_size: 3
encoder_hidden_act: "mish"
decoder_hidden_size: 384
decoder_num_hidden_layers: 4
decoder_num_attention_heads: 2
decoder_attention_head_size: 192 # hidden_size // num_attention_heads
decoder_intermediate_size: 1024
decoder_intermediate_kernel_size: 3
decoder_hidden_act: "mish"
num_duration_conv_layers: 2
duration_predictor_filters: 256
duration_predictor_kernel_sizes: 3
num_mels: 80
hidden_dropout_prob: 0.2
attention_probs_dropout_prob: 0.1
duration_predictor_dropout_probs: 0.2
max_position_embeddings: 2048
initializer_range: 0.02
output_attentions: False
output_hidden_states: False
###########################################################
# DATA LOADER SETTING #
###########################################################
batch_size: 16 # 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.
###########################################################
# OPTIMIZER & SCHEDULER SETTING #
###########################################################
optimizer_params:
initial_learning_rate: 0.001
end_learning_rate: 0.00005
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|encoder|decoder' )
# must separate by |. if var_train_expr is null then we
# training all variable
###########################################################
# INTERVAL SETTING #
###########################################################
train_max_steps: 200000 # Number of training steps.
save_interval_steps: 5000 # 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.
###########################################################
# OTHER SETTING #
###########################################################
num_save_intermediate_results: 1 # Number of batch to be saved as intermediate results. |