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| # This EXPERIMENTAL configuration is for ESPnet2 to finetune | |
| # Conformer FastSpeech2 + HiFiGAN vocoder jointly. 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 with 12GB GPU memory. | |
| # It takes around less than 1 week to finish the training but | |
| # 100k iters model should generate reasonable results. | |
| # YOU NEED TO MODIFY THE "*_params" AND "init_param" SECTIONS | |
| # IF YOU WANT TO USE YOUR OWN PRETRAINED MODLES. | |
| ########################################################## | |
| # TTS MODEL SETTING # | |
| ########################################################## | |
| tts: joint_text2wav | |
| tts_conf: | |
| # copied from pretrained model's config.yaml | |
| text2mel_type: tacotron2 | |
| text2mel_params: | |
| embed_dim: 512 # char or phn embedding dimension | |
| elayers: 1 # number of blstm layers in encoder | |
| eunits: 512 # number of blstm units | |
| econv_layers: 3 # number of convolutional layers in encoder | |
| econv_chans: 512 # number of channels in convolutional layer | |
| econv_filts: 5 # filter size of convolutional layer | |
| atype: location # attention function type | |
| adim: 512 # attention dimension | |
| aconv_chans: 32 # number of channels in convolutional layer of attention | |
| aconv_filts: 15 # filter size of convolutional layer of attention | |
| cumulate_att_w: true # whether to cumulate attention weight | |
| dlayers: 2 # number of lstm layers in decoder | |
| dunits: 1024 # number of lstm units in decoder | |
| prenet_layers: 2 # number of layers in prenet | |
| prenet_units: 256 # number of units in prenet | |
| postnet_layers: 5 # number of layers in postnet | |
| postnet_chans: 512 # number of channels in postnet | |
| postnet_filts: 5 # filter size of postnet layer | |
| output_activation: null # activation function for the final output | |
| use_batch_norm: true # whether to use batch normalization in encoder | |
| use_concate: true # whether to concatenate encoder embedding with decoder outputs | |
| use_residual: false # whether to use residual connection in encoder | |
| spk_embed_dim: 192 # speaker embedding dimension | |
| spk_embed_integration_type: add # how to integrate speaker embedding | |
| dropout_rate: 0.5 # dropout rate | |
| zoneout_rate: 0.1 # zoneout rate | |
| reduction_factor: 1 # reduction factor | |
| use_masking: true # whether to apply masking for padded part in loss calculation | |
| bce_pos_weight: 10.0 # weight of positive sample in binary cross entropy calculation | |
| use_guided_attn_loss: true # whether to use guided attention loss | |
| guided_attn_loss_sigma: 0.4 # sigma of guided attention loss | |
| guided_attn_loss_lambda: 1.0 # strength of guided attention loss | |
| # copied from pretrained vocoder's config.yaml | |
| vocoder_type: hifigan_generator | |
| vocoder_params: | |
| bias: true | |
| channels: 512 | |
| in_channels: 80 | |
| kernel_size: 7 | |
| nonlinear_activation: LeakyReLU | |
| nonlinear_activation_params: | |
| negative_slope: 0.1 | |
| out_channels: 1 | |
| resblock_dilations: | |
| - - 1 | |
| - 3 | |
| - 5 | |
| - - 1 | |
| - 3 | |
| - 5 | |
| - - 1 | |
| - 3 | |
| - 5 | |
| resblock_kernel_sizes: | |
| - 3 | |
| - 7 | |
| - 11 | |
| upsample_kernel_sizes: | |
| - 16 | |
| - 16 | |
| - 4 | |
| - 4 | |
| upsample_scales: | |
| - 8 | |
| - 8 | |
| - 2 | |
| - 2 | |
| use_additional_convs: true | |
| use_weight_norm: true | |
| # copied from pretrained vocoder's config.yaml | |
| discriminator_type: hifigan_multi_scale_multi_period_discriminator | |
| discriminator_params: | |
| follow_official_norm: true | |
| period_discriminator_params: | |
| bias: true | |
| channels: 32 | |
| downsample_scales: | |
| - 3 | |
| - 3 | |
| - 3 | |
| - 3 | |
| - 1 | |
| in_channels: 1 | |
| kernel_sizes: | |
| - 5 | |
| - 3 | |
| max_downsample_channels: 1024 | |
| nonlinear_activation: LeakyReLU | |
| nonlinear_activation_params: | |
| negative_slope: 0.1 | |
| out_channels: 1 | |
| use_spectral_norm: false | |
| use_weight_norm: true | |
| periods: | |
| - 2 | |
| - 3 | |
| - 5 | |
| - 7 | |
| - 11 | |
| scale_discriminator_params: | |
| bias: true | |
| channels: 128 | |
| downsample_scales: | |
| - 4 | |
| - 4 | |
| - 4 | |
| - 4 | |
| - 1 | |
| in_channels: 1 | |
| kernel_sizes: | |
| - 15 | |
| - 41 | |
| - 5 | |
| - 3 | |
| max_downsample_channels: 1024 | |
| max_groups: 16 | |
| nonlinear_activation: LeakyReLU | |
| nonlinear_activation_params: | |
| negative_slope: 0.1 | |
| out_channels: 1 | |
| scale_downsample_pooling: AvgPool1d | |
| scale_downsample_pooling_params: | |
| kernel_size: 4 | |
| padding: 2 | |
| stride: 2 | |
| scales: 3 | |
| # 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" | |
| use_feat_match_loss: true # whether to use feat match loss | |
| 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 | |
| use_mel_loss: true # whether to use mel-spectrogram loss | |
| 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_text2mel: 1.0 # loss scaling coefficient for text2mel loss | |
| 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 | |
| # others | |
| sampling_rate: 22050 # needed in the inference for saving wav | |
| segment_size: 32 # segment size for random windowed discriminator | |
| cache_generator_outputs: true # whether to cache generator outputs in the training | |
| # extra module for additional inputs | |
| #pitch_extract: dio # pitch extractor type | |
| #pitch_extract_conf: | |
| # reduction_factor: 1 | |
| #pitch_normalize: global_mvn # normalizer for the pitch feature | |
| #energy_extract: energy # energy extractor type | |
| #energy_extract_conf: | |
| # reduction_factor: 1 | |
| #energy_normalize: global_mvn # normalizer for the energy feature | |
| # initialization (might need to modify for your own pretrained model) | |
| init_param: | |
| - exp/22k/tts_train_tacotron2_raw_char/train.loss.ave_5best.pth:tts:tts.generator.text2mel | |
| - exp/22k/ljspeech_hifigan.v1/generator.pth::tts.generator.vocoder | |
| - exp/22k/ljspeech_hifigan.v1/discriminator.pth::tts.discriminator | |
| ########################################################## | |
| # OPTIMIZER & SCHEDULER SETTING # | |
| ########################################################## | |
| # optimizer setting for generator | |
| optim: adam | |
| optim_conf: | |
| lr: 1.25e-5 | |
| betas: [0.5, 0.9] | |
| weight_decay: 0.0 | |
| scheduler: exponentiallr | |
| scheduler_conf: | |
| gamma: 0.999875 | |
| # optimizer setting for discriminator | |
| optim2: adam | |
| optim2_conf: | |
| lr: 1.25e-5 | |
| betas: [0.5, 0.9] | |
| weight_decay: 0.0 | |
| scheduler2: exponentiallr | |
| scheduler2_conf: | |
| gamma: 0.999875 | |
| generator_first: true # whether to start updating generator first | |
| ########################################################## | |
| # OTHER TRAINING SETTING # | |
| ########################################################## | |
| #num_iters_per_epoch: 1000 # number of iterations per epoch | |
| max_epoch: 500 # number of epochs | |
| accum_grad: 1 # gradient accumulation | |
| batch_bins: 1600000 # batch bins (feats_type=raw) | |
| batch_type: numel # how to make batch | |
| 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: 4 # number of workers of data loader | |
| use_amp: false # whether to use pytorch amp | |
| log_interval: 50 # log interval in iterations | |
| keep_nbest_models: 5 # number of models to keep | |
| num_att_plot: 3 # number of attention figures to be saved in every check | |
| seed: 777 # 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 | |
| - - valid | |
| - text2mel_loss | |
| - min | |
| - - train | |
| - text2mel_loss | |
| - min | |
| - - 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) | |