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137 lines
6.8 KiB
137 lines
6.8 KiB
# This is the configuration file for CSMSC dataset.This configuration is based
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# on StyleMelGAN paper but uses MSE loss instead of Hinge loss. And I found that
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# batch_size = 8 is also working good. So maybe if you want to accelerate the training,
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# you can reduce the batch size (e.g. 8 or 16). Upsampling scales is modified to
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# fit the shift size 300 pt.
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# NOTE: batch_max_steps(24000) == prod(noise_upsample_scales)(80) * prod(upsample_scales)(300)
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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. (in samples)
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n_shift: 300 # Hop size. (in samples)
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win_length: 1200 # Window length. (in samples)
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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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in_channels: 128 # Number of input channels.
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aux_channels: 80
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channels: 64 # Initial number of channels for conv layers.
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out_channels: 1 # Number of output channels.
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kernel_size: 9 # Kernel size of initial and final conv layers.
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dilation: 2
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bias: True
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noise_upsample_scales: [10, 2, 2, 2]
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noise_upsample_activation: "leakyrelu"
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noise_upsample_activation_params:
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negative_slope: 0.2
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upsample_scales: [5, 1, 5, 1, 3, 1, 2, 2, 1] # List of Upsampling scales. prod(upsample_scales) == n_shift
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upsample_mode: "nearest"
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gated_function: "softmax"
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use_weight_norm: True # Whether to use 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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repeats: 4
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window_sizes: [512, 1024, 2048, 4096]
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pqmf_params:
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- [1, None, None, None]
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- [2, 62, 0.26700, 9.0]
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- [4, 62, 0.14200, 9.0]
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- [8, 62, 0.07949, 9.0]
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discriminator_params:
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out_channels: 1 # Number of output channels.
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kernel_sizes: [5, 3] # List of kernel size.
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channels: 16 # Number of channels of the initial conv layer.
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max_downsample_channels: 512 # Maximum number of channels of downsampling layers.
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bias: True
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downsample_scales: [4, 4, 4, 1] # List of downsampling scales.
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nonlinear_activation: "leakyrelu" # Nonlinear activation function.
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nonlinear_activation_params: # Parameters of nonlinear activation function.
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negative_slope: 0.2
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use_weight_norm: True # Whether to use weight norm.
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###########################################################
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# STFT LOSS SETTING #
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###########################################################
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use_stft_loss: true
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stft_loss_params:
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fft_sizes: [1024, 2048, 512] # List of FFT size for STFT-based loss.
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hop_sizes: [120, 240, 50] # List of hop size for STFT-based loss
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win_lengths: [600, 1200, 240] # List of window length for STFT-based loss.
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window: "hann" # Window function for STFT-based loss
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lambda_aux: 1.0 # Loss balancing coefficient for aux loss.
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###########################################################
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# ADVERSARIAL LOSS SETTING #
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###########################################################
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lambda_adv: 1.0 # Loss balancing coefficient for adv loss.
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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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###########################################################
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# DATA LOADER SETTING #
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###########################################################
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batch_size: 32 # Batch size.
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# batch_max_steps(24000) == prod(noise_upsample_scales)(80) * prod(upsample_scales)(300, n_shift)
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batch_max_steps: 24000 # Length of each audio in batch. Make sure dividable by n_shift.
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num_workers: 2 # Number of workers in Pytorch 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: 1.0e-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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- 100000
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- 300000
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- 500000
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- 700000
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- 900000
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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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discriminator_train_start_steps: 100000 # Number of steps to start to train discriminator.
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train_max_steps: 1500000 # 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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