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123 lines
7.0 KiB
123 lines
7.0 KiB
# This is the hyperparameter configuration file for Parallel WaveGAN.
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# Please make sure this is adjusted for the CSMSC dataset. If you want to
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# apply to the other dataset, you might need to carefully change some parameters.
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# This configuration requires 12 GB GPU memory and takes ~3 days on RTX TITAN.
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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: 1 # Number of input channels.
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out_channels: 1 # Number of output channels.
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kernel_size: 3 # Kernel size of dilated convolution.
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layers: 30 # Number of residual block layers.
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stacks: 3 # Number of stacks i.e., dilation cycles.
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residual_channels: 64 # Number of channels in residual conv.
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gate_channels: 128 # Number of channels in gated conv.
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skip_channels: 64 # Number of channels in skip conv.
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aux_channels: 80 # Number of channels for auxiliary feature conv.
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# Must be the same as num_mels.
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aux_context_window: 2 # Context window size for auxiliary feature.
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# If set to 2, previous 2 and future 2 frames will be considered.
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dropout: 0.0 # Dropout rate. 0.0 means no dropout applied.
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bias: true # use bias in residual blocks
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use_weight_norm: true # Whether to use weight norm.
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# If set to true, it will be applied to all of the conv layers.
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use_causal_conv: false # use causal conv in residual blocks and upsample layers
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upsample_scales: [4, 5, 3, 5] # Upsampling scales. Prodcut of these must be the same as hop size.
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interpolate_mode: "nearest" # upsample net interpolate mode
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freq_axis_kernel_size: 1 # upsamling net: convolution kernel size in frequencey axis
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nonlinear_activation: null
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nonlinear_activation_params: {}
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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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in_channels: 1 # Number of input channels.
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out_channels: 1 # Number of output channels.
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kernel_size: 3 # Number of output channels.
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layers: 10 # Number of conv layers.
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conv_channels: 64 # Number of chnn layers.
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bias: true # Whether to use bias parameter in conv.
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use_weight_norm: true # Whether to use weight norm.
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# If set to true, it will be applied to all of the conv layers.
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nonlinear_activation: "LeakyReLU" # Nonlinear function after each conv.
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nonlinear_activation_params: # Nonlinear function parameters
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negative_slope: 0.2 # Alpha in LeakyReLU.
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###########################################################
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# STFT LOSS SETTING #
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###########################################################
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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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###########################################################
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# ADVERSARIAL LOSS SETTING #
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###########################################################
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lambda_adv: 4.0 # Loss balancing coefficient.
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###########################################################
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# DATA LOADER SETTING #
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###########################################################
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batch_size: 8 # Batch size.
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batch_max_steps: 25500 # Length of each audio in batch. Make sure dividable by hop_size.
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pin_memory: true # Whether to pin memory in Pytorch DataLoader.
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num_workers: 2 # Number of workers in Pytorch DataLoader.
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remove_short_samples: true # Whether to remove samples the length of which are less than batch_max_steps.
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allow_cache: true # Whether to allow cache in dataset. If true, it requires cpu memory.
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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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epsilon: 1.0e-6 # Generator's epsilon.
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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: 0.0001 # Generator's learning rate.
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step_size: 200000 # Generator's scheduler step size.
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gamma: 0.5 # Generator's scheduler gamma.
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# At each step size, lr will be multiplied by this parameter.
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generator_grad_norm: 10 # Generator's gradient norm.
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discriminator_optimizer_params:
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epsilon: 1.0e-6 # Discriminator's epsilon.
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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: 0.00005 # Discriminator's learning rate.
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step_size: 200000 # Discriminator's scheduler step size.
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gamma: 0.5 # Discriminator's scheduler gamma.
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# At each step size, lr will be multiplied by this parameter.
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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: 400000 # 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_save_intermediate_results: 4 # Number of results to be saved as intermediate results.
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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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