JETS; e2e tts model

egs2/kss/tts1/conf/tuning/train_jets.yaml

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+# This configuration is for ESPnet2 to train JETS, 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 24000 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: jets
+tts_conf:
+    # generator related
+    generator_type: jets_generator
+    generator_params:
+        adim: 256                                    # attention dimension
+        aheads: 2                                    # number of attention heads
+        elayers: 4                                   # number of encoder layers
+        eunits: 1024                                 # number of encoder ff units
+        dlayers: 4                                   # number of decoder layers
+        dunits: 1024                                 # number of decoder ff units
+        positionwise_layer_type: conv1d              # type of position-wise layer
+        positionwise_conv_kernel_size: 3             # kernel size of position wise conv layer
+        duration_predictor_layers: 2                 # number of layers of duration predictor
+        duration_predictor_chans: 256                # number of channels of duration predictor
+        duration_predictor_kernel_size: 3            # filter size of duration predictor
+        use_masking: True                            # whether to apply masking for padded part in loss calculation
+        encoder_normalize_before: True               # whether to perform layer normalization before the input
+        decoder_normalize_before: True               # whether to perform layer normalization before the input
+        encoder_type: transformer                    # encoder type
+        decoder_type: transformer                    # decoder type
+        conformer_rel_pos_type: latest               # relative positional encoding type
+        conformer_pos_enc_layer_type: rel_pos        # conformer positional encoding type
+        conformer_self_attn_layer_type: rel_selfattn # conformer self-attention type
+        conformer_activation_type: swish             # conformer activation type
+        use_macaron_style_in_conformer: true         # whether to use macaron style in conformer
+        use_cnn_in_conformer: true                   # whether to use CNN in conformer
+        conformer_enc_kernel_size: 7                 # kernel size in CNN module of conformer-based encoder
+        conformer_dec_kernel_size: 31                # kernel size in CNN module of conformer-based decoder
+        init_type: xavier_uniform                    # initialization type
+        transformer_enc_dropout_rate: 0.2            # dropout rate for transformer encoder layer
+        transformer_enc_positional_dropout_rate: 0.2 # dropout rate for transformer encoder positional encoding
+        transformer_enc_attn_dropout_rate: 0.2       # dropout rate for transformer encoder attention layer
+        transformer_dec_dropout_rate: 0.2            # dropout rate for transformer decoder layer
+        transformer_dec_positional_dropout_rate: 0.2 # dropout rate for transformer decoder positional encoding
+        transformer_dec_attn_dropout_rate: 0.2       # dropout rate for transformer decoder attention layer
+        pitch_predictor_layers: 5                    # number of conv layers in pitch predictor
+        pitch_predictor_chans: 256                   # number of channels of conv layers in pitch predictor
+        pitch_predictor_kernel_size: 5               # kernel size of conv leyers in pitch predictor
+        pitch_predictor_dropout: 0.5                 # dropout rate in pitch predictor
+        pitch_embed_kernel_size: 1                   # kernel size of conv embedding layer for pitch
+        pitch_embed_dropout: 0.0                     # dropout rate after conv embedding layer for pitch
+        stop_gradient_from_pitch_predictor: true     # whether to stop the gradient from pitch predictor to encoder
+        energy_predictor_layers: 2                   # number of conv layers in energy predictor
+        energy_predictor_chans: 256                  # number of channels of conv layers in energy predictor
+        energy_predictor_kernel_size: 3              # kernel size of conv leyers in energy predictor
+        energy_predictor_dropout: 0.5                # dropout rate in energy predictor
+        energy_embed_kernel_size: 1                  # kernel size of conv embedding layer for energy
+        energy_embed_dropout: 0.0                    # dropout rate after conv embedding layer for energy
+        stop_gradient_from_energy_predictor: false   # whether to stop the gradient from energy predictor to encoder
+        generator_out_channels: 1
+        generator_channels: 512
+        generator_global_channels: -1
+        generator_kernel_size: 7
+        generator_upsample_scales: [8, 8, 2, 2]
+        generator_upsample_kernel_sizes: [16, 16, 4, 4]
+        generator_resblock_kernel_sizes: [3, 7, 11]
+        generator_resblock_dilations: [[1, 3, 5], [1, 3, 5], [1, 3, 5]]
+        generator_use_additional_convs: true
+        generator_bias: true
+        generator_nonlinear_activation: "LeakyReLU"
+        generator_nonlinear_activation_params:
+            negative_slope: 0.1
+        generator_use_weight_norm: true
+        segment_size: 64                             # segment size for random windowed discriminator
+
+    # 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: 24000                        # 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_var: 1.0
+    lambda_align: 2.0
+    # others
+    sampling_rate: 24000                 # needed in the inference for saving wav
+    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
+    use_token_averaged_f0: false
+pitch_normalize: global_mvn  # normalizer for the pitch feature
+energy_extract: energy       # energy extractor type
+energy_extract_conf:
+    reduction_factor: 1
+    use_token_averaged_energy: false
+energy_normalize: global_mvn # normalizer for the energy feature
+
+##########################################################
+#            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: true # whether to start updating generator first
+
+##########################################################
+#                OTHER TRAINING SETTING                  #
+##########################################################
+num_iters_per_epoch: 1000 # number of iterations per epoch
+max_epoch: 1000           # number of epochs
+accum_grad: 1             # gradient accumulation
+batch_bins: 2000000       # 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)