# This is the hyperparameter configuration file for Tacotron2 v1.
# 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 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.