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| # This configuration is for ESPnet2 to train VITS, which | |
| # is truely end-to-end text-to-waveform model. To run | |
| # this config, you need to specify "--tts_task gan_tts" | |
| # option for tts.sh at least and use 22050 hz audio as | |
| # the training data (mainly tested on LJspeech). | |
| # This configuration tested on 4 GPUs (V100) with 32GB GPU | |
| # memory. It takes around 2 weeks to finish the training | |
| # but 100k iters model should generate reasonable results. | |
| ########################################################## | |
| # TTS MODEL SETTING # | |
| ########################################################## | |
| tts: vits | |
| tts_conf: | |
| # generator related | |
| generator_type: vits_generator | |
| generator_params: | |
| hidden_channels: 192 | |
| spks: 128 | |
| global_channels: 256 | |
| segment_size: 32 | |
| text_encoder_attention_heads: 2 | |
| text_encoder_ffn_expand: 4 | |
| text_encoder_blocks: 6 | |
| text_encoder_positionwise_layer_type: "conv1d" | |
| text_encoder_positionwise_conv_kernel_size: 3 | |
| text_encoder_positional_encoding_layer_type: "rel_pos" | |
| text_encoder_self_attention_layer_type: "rel_selfattn" | |
| text_encoder_activation_type: "swish" | |
| text_encoder_normalize_before: true | |
| text_encoder_dropout_rate: 0.1 | |
| text_encoder_positional_dropout_rate: 0.0 | |
| text_encoder_attention_dropout_rate: 0.1 | |
| use_macaron_style_in_text_encoder: true | |
| # NOTE(kan-bayashi): Conformer conv requires BatchNorm1d which causes | |
| # errors when multiple GPUs in pytorch 1.7.1. Therefore, we disable | |
| # it as a default. We need to consider the alternative normalization | |
| # or different version pytorch may solve this issue. | |
| use_conformer_conv_in_text_encoder: false | |
| text_encoder_conformer_kernel_size: -1 | |
| decoder_kernel_size: 7 | |
| decoder_channels: 512 | |
| decoder_upsample_scales: [8, 8, 2, 2] | |
| decoder_upsample_kernel_sizes: [16, 16, 4, 4] | |
| decoder_resblock_kernel_sizes: [3, 7, 11] | |
| decoder_resblock_dilations: [[1, 3, 5], [1, 3, 5], [1, 3, 5]] | |
| use_weight_norm_in_decoder: true | |
| posterior_encoder_kernel_size: 5 | |
| posterior_encoder_layers: 16 | |
| posterior_encoder_stacks: 1 | |
| posterior_encoder_base_dilation: 1 | |
| posterior_encoder_dropout_rate: 0.0 | |
| use_weight_norm_in_posterior_encoder: true | |
| flow_flows: 4 | |
| flow_kernel_size: 5 | |
| flow_base_dilation: 1 | |
| flow_layers: 4 | |
| flow_dropout_rate: 0.0 | |
| use_weight_norm_in_flow: true | |
| use_only_mean_in_flow: true | |
| stochastic_duration_predictor_kernel_size: 3 | |
| stochastic_duration_predictor_dropout_rate: 0.5 | |
| stochastic_duration_predictor_flows: 4 | |
| stochastic_duration_predictor_dds_conv_layers: 3 | |
| # discriminator related | |
| discriminator_type: hifigan_multi_scale_multi_period_discriminator | |
| discriminator_params: | |
| scales: 1 | |
| scale_downsample_pooling: "AvgPool1d" | |
| scale_downsample_pooling_params: | |
| kernel_size: 4 | |
| stride: 2 | |
| padding: 2 | |
| scale_discriminator_params: | |
| in_channels: 1 | |
| out_channels: 1 | |
| kernel_sizes: [15, 41, 5, 3] | |
| channels: 128 | |
| max_downsample_channels: 1024 | |
| max_groups: 16 | |
| bias: True | |
| downsample_scales: [2, 2, 4, 4, 1] | |
| nonlinear_activation: "LeakyReLU" | |
| nonlinear_activation_params: | |
| negative_slope: 0.1 | |
| use_weight_norm: True | |
| use_spectral_norm: False | |
| follow_official_norm: False | |
| periods: [2, 3, 5, 7, 11] | |
| period_discriminator_params: | |
| in_channels: 1 | |
| out_channels: 1 | |
| kernel_sizes: [5, 3] | |
| channels: 32 | |
| downsample_scales: [3, 3, 3, 3, 1] | |
| max_downsample_channels: 1024 | |
| bias: True | |
| nonlinear_activation: "LeakyReLU" | |
| nonlinear_activation_params: | |
| negative_slope: 0.1 | |
| use_weight_norm: True | |
| use_spectral_norm: False | |
| # loss function related | |
| generator_adv_loss_params: | |
| average_by_discriminators: false # whether to average loss value by #discriminators | |
| loss_type: mse # loss type, "mse" or "hinge" | |
| discriminator_adv_loss_params: | |
| average_by_discriminators: false # whether to average loss value by #discriminators | |
| loss_type: mse # loss type, "mse" or "hinge" | |
| feat_match_loss_params: | |
| average_by_discriminators: false # whether to average loss value by #discriminators | |
| average_by_layers: false # whether to average loss value by #layers of each discriminator | |
| include_final_outputs: true # whether to include final outputs for loss calculation | |
| mel_loss_params: | |
| fs: 22050 # must be the same as the training data | |
| n_fft: 1024 # fft points | |
| hop_length: 256 # hop size | |
| win_length: null # window length | |
| window: hann # window type | |
| n_mels: 80 # number of Mel basis | |
| fmin: 0 # minimum frequency for Mel basis | |
| fmax: null # maximum frequency for Mel basis | |
| log_base: null # null represent natural log | |
| lambda_adv: 1.0 # loss scaling coefficient for adversarial loss | |
| lambda_mel: 45.0 # loss scaling coefficient for Mel loss | |
| lambda_feat_match: 2.0 # loss scaling coefficient for feat match loss | |
| lambda_dur: 1.0 # loss scaling coefficient for duration loss | |
| lambda_kl: 1.0 # loss scaling coefficient for KL divergence loss | |
| # others | |
| sampling_rate: 22050 # needed in the inference for saving wav | |
| cache_generator_outputs: true # whether to cache generator outputs in the training | |
| ########################################################## | |
| # OPTIMIZER & SCHEDULER SETTING # | |
| ########################################################## | |
| # optimizer setting for generator | |
| optim: adamw | |
| optim_conf: | |
| lr: 2.0e-4 | |
| betas: [0.8, 0.99] | |
| eps: 1.0e-9 | |
| weight_decay: 0.0 | |
| scheduler: exponentiallr | |
| scheduler_conf: | |
| gamma: 0.999875 | |
| # optimizer setting for discriminator | |
| optim2: adamw | |
| optim2_conf: | |
| lr: 2.0e-4 | |
| betas: [0.8, 0.99] | |
| eps: 1.0e-9 | |
| weight_decay: 0.0 | |
| scheduler2: exponentiallr | |
| scheduler2_conf: | |
| gamma: 0.999875 | |
| generator_first: false # whether to start updating generator first | |
| ########################################################## | |
| # OTHER TRAINING SETTING # | |
| ########################################################## | |
| #num_iters_per_epoch: 1000 # number of iterations per epoch | |
| max_epoch: 30 # number of epochs | |
| accum_grad: 1 # gradient accumulation | |
| batch_bins: 1900000 # batch bins (feats_type=raw) | |
| batch_type: numel # how to make batch | |
| #batch_type: sorted # how to make batchbatch_size: 1 | |
| grad_clip: -1 # gradient clipping norm | |
| grad_noise: false # whether to use gradient noise injection | |
| sort_in_batch: descending # how to sort data in making batch | |
| sort_batch: descending # how to sort created batches | |
| num_workers: 1 # number of workers of data loader | |
| use_amp: false # whether to use pytorch amp | |
| train_dtype: float32 | |
| log_interval: 50 # log interval in iterations | |
| keep_nbest_models: 10 # number of models to keep | |
| num_att_plot: 3 # number of attention figures to be saved in every check | |
| seed: 3407 # random seed number | |
| patience: null # patience for early stopping | |
| unused_parameters: true # needed for multi gpu case | |
| best_model_criterion: # criterion to save the best models | |
| - - train | |
| - total_count | |
| - max | |
| cudnn_deterministic: false # setting to false accelerates the training speed but makes it non-deterministic | |
| # in the case of GAN-TTS training, we strongly recommend setting to false | |
| cudnn_benchmark: false # setting to true might acdelerate the training speed but sometimes decrease it | |
| # therefore, we set to false as a default (recommend trying both cases) | |