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| # This is the configuration file for JSUT dataset. | |
| # This configuration is based on HiFiGAN V1, which is | |
| # an official configuration. But I found that the optimizer | |
| # setting does not work well with my implementation. | |
| # So I changed optimizer settings as follows: | |
| # - AdamW -> Adam | |
| # - betas: [0.8, 0.99] -> betas: [0.5, 0.9] | |
| # - Scheduler: ExponentialLR -> MultiStepLR | |
| # To match the shift size difference, the upsample scales | |
| # is also modified from the original 256 shift setting. | |
| ########################################################### | |
| # FEATURE EXTRACTION SETTING # | |
| ########################################################### | |
| sampling_rate: 24000 # Sampling rate. | |
| fft_size: 2048 # FFT size. | |
| hop_size: 300 # Hop size. | |
| win_length: 1200 # Window length. | |
| # If set to null, it will be the same as fft_size. | |
| window: "hann" # Window function. | |
| num_mels: 80 # Number of mel basis. | |
| fmin: 80 # Minimum freq in mel basis calculation. | |
| fmax: 7600 # Maximum frequency in mel basis calculation. | |
| global_gain_scale: 1.0 # Will be multiplied to all of waveform. | |
| trim_silence: false # Whether to trim the start and end of silence. | |
| trim_threshold_in_db: 20 # Need to tune carefully if the recording is not good. | |
| trim_frame_size: 1024 # Frame size in trimming. | |
| trim_hop_size: 256 # Hop size in trimming. | |
| format: "hdf5" # Feature file format. "npy" or "hdf5" is supported. | |
| ########################################################### | |
| # GENERATOR NETWORK ARCHITECTURE SETTING # | |
| ########################################################### | |
| generator_type: HiFiGANGenerator | |
| generator_params: | |
| in_channels: 80 # Number of input channels. | |
| out_channels: 1 # Number of output channels. | |
| channels: 512 # Number of initial channels. | |
| kernel_size: 7 # Kernel size of initial and final conv layers. | |
| upsample_scales: [5, 5, 4, 3] # Upsampling scales. | |
| upsample_kernel_sizes: [10, 10, 8, 6] # Kernel size for upsampling layers. | |
| resblock_kernel_sizes: [3, 7, 11] # Kernel size for residual blocks. | |
| resblock_dilations: # Dilations for residual blocks. | |
| - [1, 3, 5] | |
| - [1, 3, 5] | |
| - [1, 3, 5] | |
| use_additional_convs: true # Whether to use additional conv layer in residual blocks. | |
| bias: true # Whether to use bias parameter in conv. | |
| nonlinear_activation: "LeakyReLU" # Nonlinear activation type. | |
| nonlinear_activation_params: # Nonlinear activation paramters. | |
| negative_slope: 0.1 | |
| use_weight_norm: true # Whether to apply weight normalization. | |
| ########################################################### | |
| # DISCRIMINATOR NETWORK ARCHITECTURE SETTING # | |
| ########################################################### | |
| discriminator_type: HiFiGANMultiScaleMultiPeriodDiscriminator | |
| discriminator_params: | |
| scales: 3 # Number of multi-scale discriminator. | |
| scale_downsample_pooling: "AvgPool1d" # Pooling operation for scale discriminator. | |
| scale_downsample_pooling_params: | |
| kernel_size: 4 # Pooling kernel size. | |
| stride: 2 # Pooling stride. | |
| padding: 2 # Padding size. | |
| scale_discriminator_params: | |
| in_channels: 1 # Number of input channels. | |
| out_channels: 1 # Number of output channels. | |
| kernel_sizes: [15, 41, 5, 3] # List of kernel sizes. | |
| channels: 128 # Initial number of channels. | |
| max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers. | |
| max_groups: 16 # Maximum number of groups in downsampling conv layers. | |
| bias: true | |
| downsample_scales: [4, 4, 4, 4, 1] # Downsampling scales. | |
| nonlinear_activation: "LeakyReLU" # Nonlinear activation. | |
| nonlinear_activation_params: | |
| negative_slope: 0.1 | |
| follow_official_norm: true # Whether to follow the official norm setting. | |
| periods: [2, 3, 5, 7, 11] # List of period for multi-period discriminator. | |
| period_discriminator_params: | |
| in_channels: 1 # Number of input channels. | |
| out_channels: 1 # Number of output channels. | |
| kernel_sizes: [5, 3] # List of kernel sizes. | |
| channels: 32 # Initial number of channels. | |
| downsample_scales: [3, 3, 3, 3, 1] # Downsampling scales. | |
| max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers. | |
| bias: true # Whether to use bias parameter in conv layer." | |
| nonlinear_activation: "LeakyReLU" # Nonlinear activation. | |
| nonlinear_activation_params: # Nonlinear activation paramters. | |
| negative_slope: 0.1 | |
| use_weight_norm: true # Whether to apply weight normalization. | |
| use_spectral_norm: false # Whether to apply spectral normalization. | |
| ########################################################### | |
| # STFT LOSS SETTING # | |
| ########################################################### | |
| use_stft_loss: false # Whether to use multi-resolution STFT loss. | |
| use_mel_loss: true # Whether to use Mel-spectrogram loss. | |
| mel_loss_params: | |
| fs: 24000 | |
| fft_size: 2048 | |
| hop_size: 300 | |
| win_length: 1200 | |
| window: "hann" | |
| num_mels: 80 | |
| fmin: 0 | |
| fmax: 12000 | |
| log_base: null | |
| generator_adv_loss_params: | |
| average_by_discriminators: false # Whether to average loss by #discriminators. | |
| discriminator_adv_loss_params: | |
| average_by_discriminators: false # Whether to average loss by #discriminators. | |
| use_feat_match_loss: true | |
| feat_match_loss_params: | |
| average_by_discriminators: false # Whether to average loss by #discriminators. | |
| average_by_layers: false # Whether to average loss by #layers in each discriminator. | |
| include_final_outputs: false # Whether to include final outputs in feat match loss calculation. | |
| ########################################################### | |
| # ADVERSARIAL LOSS SETTING # | |
| ########################################################### | |
| lambda_aux: 45.0 # Loss balancing coefficient for STFT loss. | |
| lambda_adv: 1.0 # Loss balancing coefficient for adversarial loss. | |
| lambda_feat_match: 2.0 # Loss balancing coefficient for feat match loss.. | |
| ########################################################### | |
| # DATA LOADER SETTING # | |
| ########################################################### | |
| batch_size: 16 # Batch size. | |
| batch_max_steps: 8400 # Length of each audio in batch. Make sure dividable by hop_size. | |
| pin_memory: true # Whether to pin memory in Pytorch DataLoader. | |
| num_workers: 2 # Number of workers in Pytorch DataLoader. | |
| remove_short_samples: false # 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. | |
| ########################################################### | |
| # OPTIMIZER & SCHEDULER SETTING # | |
| ########################################################### | |
| generator_optimizer_type: Adam | |
| generator_optimizer_params: | |
| lr: 2.0e-4 | |
| betas: [0.5, 0.9] | |
| weight_decay: 0.0 | |
| generator_scheduler_type: MultiStepLR | |
| generator_scheduler_params: | |
| gamma: 0.5 | |
| milestones: | |
| - 200000 | |
| - 400000 | |
| - 600000 | |
| - 800000 | |
| generator_grad_norm: -1 | |
| discriminator_optimizer_type: Adam | |
| discriminator_optimizer_params: | |
| lr: 2.0e-4 | |
| betas: [0.5, 0.9] | |
| weight_decay: 0.0 | |
| discriminator_scheduler_type: MultiStepLR | |
| discriminator_scheduler_params: | |
| gamma: 0.5 | |
| milestones: | |
| - 200000 | |
| - 400000 | |
| - 600000 | |
| - 800000 | |
| discriminator_grad_norm: -1 | |
| ########################################################### | |
| # INTERVAL SETTING # | |
| ########################################################### | |
| generator_train_start_steps: 1 # Number of steps to start to train discriminator. | |
| discriminator_train_start_steps: 0 # Number of steps to start to train discriminator. | |
| train_max_steps: 2500000 # Number of training steps. | |
| save_interval_steps: 10000 # Interval steps to save checkpoint. | |
| eval_interval_steps: 1000 # Interval steps to evaluate the network. | |
| log_interval_steps: 100 # Interval steps to record the training log. | |
| ########################################################### | |
| # OTHER SETTING # | |
| ########################################################### | |
| num_save_intermediate_results: 4 # Number of results to be saved as intermediate results. | |