# 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.