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# This is the hyperparameter configuration file for MelGAN.
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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 ~ 8GB memory and will finish within 7 days on Titan V.
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# This configuration is based on full-band MelGAN but the hop size and sampling
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# rate is different from the paper (16kHz vs 24kHz). The number of iteraions
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# is not shown in the paper so currently we train 1M iterations (not sure enough
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# to converge).
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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: 80 # Number of input channels.
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out_channels: 4 # Number of output channels.
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kernel_size: 7 # Kernel size of initial and final conv layers.
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channels: 384 # Initial number of channels for conv layers.
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upsample_scales: [5, 5, 3] # List of Upsampling scales. prod(upsample_scales) == n_shift
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stack_kernel_size: 3 # Kernel size of dilated conv layers in residual stack.
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stacks: 4 # Number of stacks in a single residual stack module.
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use_weight_norm: True # Whether to use weight normalization.
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use_causal_conv: False # Whether to use causal convolution.
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use_final_nonlinear_activation: True
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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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scales: 3 # Number of multi-scales.
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downsample_pooling: "AvgPool1D" # Pooling type for the input downsampling.
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downsample_pooling_params: # Parameters of the above pooling function.
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kernel_size: 4
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stride: 2
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padding: 1
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exclusive: True
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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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downsample_scales: [4, 4, 4] # 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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use_subband_stft_loss: true
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subband_stft_loss_params:
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fft_sizes: [384, 683, 171] # List of FFT size for STFT-based loss.
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hop_sizes: [30, 60, 10] # List of hop size for STFT-based loss.
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win_lengths: [150, 300, 60] # 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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use_feat_match_loss: false # Whether to use feature matching loss.
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lambda_adv: 2.5 # Loss balancing coefficient for adversarial loss.
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###########################################################
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# DATA LOADER SETTING #
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###########################################################
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batch_size: 64 # Batch size.
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batch_max_steps: 16200 # Length of each audio in batch. Make sure dividable by n_shift.
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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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epsilon: 1.0e-7 # Generator's epsilon.
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weight_decay: 0.0 # Generator's weight decay coefficient.
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generator_grad_norm: -1 # Generator's gradient norm.
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generator_scheduler_params:
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learning_rate: 1.0e-3 # 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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- 200000
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- 300000
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- 400000
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- 500000
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- 600000
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discriminator_optimizer_params:
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epsilon: 1.0e-7 # Discriminator's epsilon.
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weight_decay: 0.0 # Discriminator's weight decay coefficient.
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discriminator_grad_norm: -1 # Discriminator's gradient norm.
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discriminator_scheduler_params:
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learning_rate: 1.0e-3 # 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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- 100000
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- 200000
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- 300000
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- 400000
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- 500000
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- 600000
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###########################################################
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# INTERVAL SETTING #
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###########################################################
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discriminator_train_start_steps: 200000 # Number of steps to start to train discriminator.
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train_max_steps: 2000000 # Number of training steps.
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save_interval_steps: 1000 # 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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