# This is the hyperparameter configuration file for MelGAN. # Please make sure this is adjusted for the CSMSC dataset. If you want to # apply to the other dataset, you might need to carefully change some parameters. # This configuration requires ~ 8GB memory and will finish within 7 days on Titan V. # This configuration is based on full-band MelGAN but the hop size and sampling # rate is different from the paper (16kHz vs 24kHz). The number of iteraions # is not shown in the paper so currently we train 1M iterations (not sure enough # to converge). ########################################################### # FEATURE EXTRACTION SETTING # ########################################################### fs: 24000 # Sampling rate. n_fft: 2048 # FFT size (samples). n_shift: 300 # Hop size (samples). 12.5ms win_length: 1200 # Window length (samples). 50ms # If set to null, it will be the same as fft_size. window: "hann" # Window function. n_mels: 80 # Number of mel basis. fmin: 80 # Minimum freq in mel basis calculation. (Hz) fmax: 7600 # Maximum frequency in mel basis calculation. (Hz) ########################################################### # GENERATOR NETWORK ARCHITECTURE SETTING # ########################################################### generator_params: in_channels: 80 # Number of input channels. out_channels: 4 # Number of output channels. kernel_size: 7 # Kernel size of initial and final conv layers. channels: 384 # Initial number of channels for conv layers. upsample_scales: [5, 5, 3] # List of Upsampling scales. prod(upsample_scales) x out_channels == n_shift stack_kernel_size: 3 # Kernel size of dilated conv layers in residual stack. stacks: 4 # Number of stacks in a single residual stack module. use_weight_norm: True # Whether to use weight normalization. use_causal_conv: False # Whether to use causal convolution. use_final_nonlinear_activation: True ########################################################### # DISCRIMINATOR NETWORK ARCHITECTURE SETTING # ########################################################### discriminator_params: in_channels: 1 # Number of input channels. out_channels: 1 # Number of output channels. scales: 3 # Number of multi-scales. downsample_pooling: "AvgPool1D" # Pooling type for the input downsampling. downsample_pooling_params: # Parameters of the above pooling function. kernel_size: 4 stride: 2 padding: 1 exclusive: True kernel_sizes: [5, 3] # List of kernel size. channels: 16 # Number of channels of the initial conv layer. max_downsample_channels: 512 # Maximum number of channels of downsampling layers. downsample_scales: [4, 4, 4] # List of downsampling scales. nonlinear_activation: "leakyrelu" # Nonlinear activation function. nonlinear_activation_params: # Parameters of nonlinear activation function. negative_slope: 0.2 use_weight_norm: True # Whether to use weight norm. ########################################################### # STFT LOSS SETTING # ########################################################### use_stft_loss: True stft_loss_params: fft_sizes: [1024, 2048, 512] # List of FFT size for STFT-based loss. hop_sizes: [120, 240, 50] # List of hop size for STFT-based loss. win_lengths: [600, 1200, 240] # List of window length for STFT-based loss. window: "hann" # Window function for STFT-based loss use_subband_stft_loss: True subband_stft_loss_params: fft_sizes: [384, 683, 171] # List of FFT size for STFT-based loss. hop_sizes: [30, 60, 10] # List of hop size for STFT-based loss win_lengths: [150, 300, 60] # List of window length for STFT-based loss. window: "hann" # Window function for STFT-based loss ########################################################### # ADVERSARIAL LOSS SETTING # ########################################################### use_feat_match_loss: False # Whether to use feature matching loss. lambda_adv: 2.5 # Loss balancing coefficient for adversarial loss. ########################################################### # DATA LOADER SETTING # ########################################################### batch_size: 64 # Batch size. batch_max_steps: 16200 # Length of each audio in batch. Make sure dividable by n_shift. num_workers: 2 # Number of workers in DataLoader. ########################################################### # OPTIMIZER & SCHEDULER SETTING # ########################################################### generator_optimizer_params: epsilon: 1.0e-7 # Generator's epsilon. weight_decay: 0.0 # Generator's weight decay coefficient. generator_grad_norm: -1 # Generator's gradient norm. generator_scheduler_params: learning_rate: 1.0e-3 # Generator's learning rate. gamma: 0.5 # Generator's scheduler gamma. milestones: # At each milestone, lr will be multiplied by gamma. - 100000 - 200000 - 300000 - 400000 - 500000 - 600000 discriminator_optimizer_params: epsilon: 1.0e-7 # Discriminator's epsilon. weight_decay: 0.0 # Discriminator's weight decay coefficient. discriminator_grad_norm: -1 # Discriminator's gradient norm. discriminator_scheduler_params: learning_rate: 1.0e-3 # Discriminator's learning rate. gamma: 0.5 # Discriminator's scheduler gamma. milestones: # At each milestone, lr will be multiplied by gamma. - 100000 - 200000 - 300000 - 400000 - 500000 - 600000 ########################################################### # INTERVAL SETTING # ########################################################### discriminator_train_start_steps: 200000 # Number of steps to start to train discriminator. train_max_steps: 1000000 # Number of training steps. save_interval_steps: 5000 # Interval steps to save checkpoint. eval_interval_steps: 1000 # Interval steps to evaluate the network. ########################################################### # OTHER SETTING # ########################################################### num_snapshots: 10 # max number of snapshots to keep while training seed: 42 # random seed for paddle, random, and np.random