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175 lines
9.3 KiB
175 lines
9.3 KiB
# This is the configuration file for CSMSC dataset.
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# This configuration is based on HiFiGAN V1, which is an official configuration.
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# But I found that the optimizer setting does not work well with my implementation.
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# So I changed optimizer settings as follows:
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# - AdamW -> Adam
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# - betas: [0.8, 0.99] -> betas: [0.5, 0.9]
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# - Scheduler: ExponentialLR -> MultiStepLR
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# To match the shift size difference, the upsample scales is also modified from the original 256 shift setting.
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###########################################################
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# FEATURE EXTRACTION SETTING #
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###########################################################
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fs: 24000 # Sampling rate.
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n_fft: 2048 # FFT size (samples).
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n_shift: 300 # Hop size (samples). 12.5ms
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win_length: 1200 # Window length (samples). 50ms
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# If set to null, it will be the same as fft_size.
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window: "hann" # Window function.
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n_mels: 80 # Number of mel basis.
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fmin: 80 # Minimum freq in mel basis calculation. (Hz)
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fmax: 7600 # Maximum frequency in mel basis calculation. (Hz)
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###########################################################
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# GENERATOR NETWORK ARCHITECTURE SETTING #
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###########################################################
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generator_params:
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use_istft: True # Use iSTFTNet.
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istft_layer_id: 2 # Use istft after istft_layer_id layers of upsample layer if use_istft=True.
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n_fft: 2048 # FFT size (samples) in feature extraction.
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win_length: 1200 # Window length (samples) in feature extraction.
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in_channels: 80 # Number of input channels.
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out_channels: 1 # Number of output channels.
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channels: 512 # Number of initial channels.
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kernel_size: 7 # Kernel size of initial and final conv layers.
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upsample_scales: [5, 5, 4, 3] # Upsampling scales.
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upsample_kernel_sizes: [10, 10, 8, 6] # Kernel size for upsampling layers.
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resblock_kernel_sizes: [3, 7, 11] # Kernel size for residual blocks.
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resblock_dilations: # Dilations for residual blocks.
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- [1, 3, 5]
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- [1, 3, 5]
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- [1, 3, 5]
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use_additional_convs: True # Whether to use additional conv layer in residual blocks.
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bias: True # Whether to use bias parameter in conv.
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nonlinear_activation: "leakyrelu" # Nonlinear activation type.
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nonlinear_activation_params: # Nonlinear activation paramters.
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negative_slope: 0.1
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use_weight_norm: True # Whether to apply weight normalization.
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###########################################################
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# DISCRIMINATOR NETWORK ARCHITECTURE SETTING #
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###########################################################
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discriminator_params:
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scales: 3 # Number of multi-scale discriminator.
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scale_downsample_pooling: "AvgPool1D" # Pooling operation for scale discriminator.
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scale_downsample_pooling_params:
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kernel_size: 4 # Pooling kernel size.
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stride: 2 # Pooling stride.
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padding: 2 # Padding size.
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scale_discriminator_params:
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in_channels: 1 # Number of input channels.
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out_channels: 1 # Number of output channels.
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kernel_sizes: [15, 41, 5, 3] # List of kernel sizes.
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channels: 128 # Initial number of channels.
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max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
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max_groups: 16 # Maximum number of groups in downsampling conv layers.
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bias: True
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downsample_scales: [4, 4, 4, 4, 1] # Downsampling scales.
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nonlinear_activation: "leakyrelu" # Nonlinear activation.
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nonlinear_activation_params:
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negative_slope: 0.1
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follow_official_norm: True # Whether to follow the official norm setting.
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periods: [2, 3, 5, 7, 11] # List of period for multi-period discriminator.
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period_discriminator_params:
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in_channels: 1 # Number of input channels.
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out_channels: 1 # Number of output channels.
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kernel_sizes: [5, 3] # List of kernel sizes.
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channels: 32 # Initial number of channels.
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downsample_scales: [3, 3, 3, 3, 1] # Downsampling scales.
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max_downsample_channels: 1024 # Maximum number of channels in downsampling conv layers.
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bias: True # Whether to use bias parameter in conv layer."
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nonlinear_activation: "leakyrelu" # Nonlinear activation.
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nonlinear_activation_params: # Nonlinear activation paramters.
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negative_slope: 0.1
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use_weight_norm: True # Whether to apply weight normalization.
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use_spectral_norm: False # Whether to apply spectral normalization.
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###########################################################
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# STFT LOSS SETTING #
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###########################################################
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use_stft_loss: False # Whether to use multi-resolution STFT loss.
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use_mel_loss: True # Whether to use Mel-spectrogram loss.
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mel_loss_params:
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fs: 24000
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fft_size: 2048
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hop_size: 300
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win_length: 1200
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window: "hann"
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num_mels: 80
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fmin: 0
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fmax: 12000
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log_base: null
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generator_adv_loss_params:
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average_by_discriminators: False # Whether to average loss by #discriminators.
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discriminator_adv_loss_params:
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average_by_discriminators: False # Whether to average loss by #discriminators.
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use_feat_match_loss: True
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feat_match_loss_params:
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average_by_discriminators: False # Whether to average loss by #discriminators.
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average_by_layers: False # Whether to average loss by #layers in each discriminator.
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include_final_outputs: False # Whether to include final outputs in feat match loss calculation.
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###########################################################
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# ADVERSARIAL LOSS SETTING #
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###########################################################
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lambda_aux: 45.0 # Loss balancing coefficient for STFT loss.
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lambda_adv: 1.0 # Loss balancing coefficient for adversarial loss.
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lambda_feat_match: 2.0 # Loss balancing coefficient for feat match loss..
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###########################################################
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# DATA LOADER SETTING #
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###########################################################
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batch_size: 16 # Batch size.
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batch_max_steps: 8400 # Length of each audio in batch. Make sure dividable by hop_size.
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num_workers: 2 # Number of workers in DataLoader.
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###########################################################
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# OPTIMIZER & SCHEDULER SETTING #
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###########################################################
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generator_optimizer_params:
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beta1: 0.5
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beta2: 0.9
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weight_decay: 0.0 # Generator's weight decay coefficient.
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generator_scheduler_params:
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learning_rate: 2.0e-4 # Generator's learning rate.
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gamma: 0.5 # Generator's scheduler gamma.
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milestones: # At each milestone, lr will be multiplied by gamma.
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- 200000
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- 400000
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- 600000
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- 800000
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generator_grad_norm: -1 # Generator's gradient norm.
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discriminator_optimizer_params:
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beta1: 0.5
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beta2: 0.9
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weight_decay: 0.0 # Discriminator's weight decay coefficient.
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discriminator_scheduler_params:
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learning_rate: 2.0e-4 # Discriminator's learning rate.
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gamma: 0.5 # Discriminator's scheduler gamma.
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milestones: # At each milestone, lr will be multiplied by gamma.
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- 200000
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- 400000
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- 600000
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- 800000
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discriminator_grad_norm: -1 # Discriminator's gradient norm.
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###########################################################
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# INTERVAL SETTING #
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###########################################################
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generator_train_start_steps: 1 # Number of steps to start to train discriminator.
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discriminator_train_start_steps: 0 # Number of steps to start to train discriminator.
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train_max_steps: 2500000 # Number of training steps.
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save_interval_steps: 5000 # Interval steps to save checkpoint.
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eval_interval_steps: 1000 # Interval steps to evaluate the network.
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###########################################################
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# OTHER SETTING #
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###########################################################
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num_snapshots: 10 # max number of snapshots to keep while training
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seed: 42 # random seed for paddle, random, and np.random
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