# This is the hyperparameter configuration file for Parallel WaveGAN. # 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 12 GB GPU memory and takes ~3 days on RTX TITAN. ########################################################### # FEATURE EXTRACTION SETTING # ########################################################### sampling_rate: 24000 # Sampling rate. fft_size: 2048 # FFT size. hop_size: 300 # Hop size. win_length: 1200 # Window length. # If set to null, it will be the same as fft_size. window: "hann" # Window function. num_mels: 80 # Number of mel basis. fmin: 80 # Minimum freq in mel basis calculation. fmax: 7600 # Maximum frequency in mel basis calculation. global_gain_scale: 1.0 # Will be multiplied to all of waveform. trim_silence: false # Whether to trim the start and end of silence. trim_threshold_in_db: 60 # Need to tune carefully if the recording is not good. trim_frame_size: 2048 # Frame size in trimming. trim_hop_size: 512 # Hop size in trimming. format: "hdf5" # Feature file format. "npy" or "hdf5" is supported. ########################################################### # GENERATOR NETWORK ARCHITECTURE SETTING # ########################################################### generator_params: in_channels: 1 # Number of input channels. out_channels: 1 # Number of output channels. kernel_size: 3 # Kernel size of dilated convolution. layers: 30 # Number of residual block layers. stacks: 3 # Number of stacks i.e., dilation cycles. residual_channels: 64 # Number of channels in residual conv. gate_channels: 128 # Number of channels in gated conv. skip_channels: 64 # Number of channels in skip conv. aux_channels: 80 # Number of channels for auxiliary feature conv. # Must be the same as num_mels. aux_context_window: 2 # Context window size for auxiliary feature. # If set to 2, previous 2 and future 2 frames will be considered. dropout: 0.0 # Dropout rate. 0.0 means no dropout applied. use_weight_norm: true # Whether to use weight norm. # If set to true, it will be applied to all of the conv layers. upsample_net: "ConvInUpsampleNetwork" # Upsampling network architecture. upsample_params: # Upsampling network parameters. upsample_scales: [4, 5, 3, 5] # Upsampling scales. Prodcut of these must be the same as hop size. ########################################################### # DISCRIMINATOR NETWORK ARCHITECTURE SETTING # ########################################################### discriminator_params: in_channels: 1 # Number of input channels. out_channels: 1 # Number of output channels. kernel_size: 3 # Number of output channels. layers: 10 # Number of conv layers. conv_channels: 64 # Number of chnn layers. bias: true # Whether to use bias parameter in conv. use_weight_norm: true # Whether to use weight norm. # If set to true, it will be applied to all of the conv layers. nonlinear_activation: "LeakyReLU" # Nonlinear function after each conv. nonlinear_activation_params: # Nonlinear function parameters negative_slope: 0.2 # Alpha in LeakyReLU. ########################################################### # STFT LOSS SETTING # ########################################################### 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" # Window function for STFT-based loss ########################################################### # ADVERSARIAL LOSS SETTING # ########################################################### lambda_adv: 4.0 # Loss balancing coefficient. ########################################################### # DATA LOADER SETTING # ########################################################### batch_size: 6 # Batch size. batch_max_steps: 25500 # Length of each audio in batch. Make sure dividable by hop_size. pin_memory: true # Whether to pin memory in Pytorch DataLoader. num_workers: 2 # Number of workers in Pytorch DataLoader. remove_short_samples: true # Whether to remove samples the length of which are less than batch_max_steps. allow_cache: true # Whether to allow cache in dataset. If true, it requires cpu memory. ########################################################### # OPTIMIZER & SCHEDULER SETTING # ########################################################### generator_optimizer_params: lr: 0.0001 # Generator's learning rate. eps: 1.0e-6 # Generator's epsilon. weight_decay: 0.0 # Generator's weight decay coefficient. generator_scheduler_params: step_size: 200000 # Generator's scheduler step size. gamma: 0.5 # Generator's scheduler gamma. # At each step size, lr will be multiplied by this parameter. generator_grad_norm: 10 # Generator's gradient norm. discriminator_optimizer_params: lr: 0.00005 # Discriminator's learning rate. eps: 1.0e-6 # Discriminator's epsilon. weight_decay: 0.0 # Discriminator's weight decay coefficient. discriminator_scheduler_params: step_size: 200000 # Discriminator's scheduler step size. gamma: 0.5 # Discriminator's scheduler gamma. # At each step size, lr will be multiplied by this parameter. discriminator_grad_norm: 1 # Discriminator's gradient norm. ########################################################### # INTERVAL SETTING # ########################################################### discriminator_train_start_steps: 100000 # Number of steps to start to train discriminator. train_max_steps: 400000 # Number of training steps. save_interval_steps: 5000 # Interval steps to save checkpoint. eval_interval_steps: 1000 # Interval steps to evaluate the network. log_interval_steps: 100 # Interval steps to record the training log. ########################################################### # OTHER SETTING # ########################################################### num_save_intermediate_results: 4 # Number of results to be saved as intermediate results.