# This is the configuration file for yesno dataset. # Note that this configuration is just for debugging. ########################################################### # FEATURE EXTRACTION SETTING # ########################################################### sampling_rate: 8000 # Sampling rate. fft_size: 1024 # FFT size. hop_size: 256 # Hop size. win_length: null # 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: 0 # Minimum freq in mel basis calculation. fmax: 4000 # Maximum frequency in mel basis calculation. global_gain_scale: 0.8 # 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: 1024 # Frame size in trimming. trim_hop_size: 256 # Hop size in trimming. format: "hdf5" # Feature file format. " npy " or " hdf5 " is supported. ########################################################### # GENERATOR NETWORK ARCHITECTURE SETTING # ########################################################### generator_type: "MelGANGenerator" # Generator type. generator_params: in_channels: 80 # Number of input channels. out_channels: 1 # Number of output channels. kernel_size: 7 # Kernel size of initial and final conv layers. channels: 256 # Initial number of channels for conv layers. upsample_scales: [8, 8, 2, 2] # List of Upsampling scales. stack_kernel_size: 3 # Kernel size of dilated conv layers in residual stack. stacks: 1 # 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. ########################################################### # DISCRIMINATOR NETWORK ARCHITECTURE SETTING # ########################################################### discriminator_type: "MelGANMultiScaleDiscriminator" # Discriminator type. 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 count_include_pad: False kernel_sizes: [5, 3] # List of kernel size. channels: 16 # Number of channels of the initial conv layer. max_downsample_channels: 64 # Maximum number of channels of downsampling layers. downsample_scales: [4, 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 # ########################################################### 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 # ########################################################### use_feat_match_loss: true # Whether to use feature matching loss. lambda_feat_match: 25.0 # Loss balancing coefficient for feature matching loss. lambda_adv: 4.0 # Loss balancing coefficient. ########################################################### # DATA LOADER SETTING # ########################################################### batch_size: 2 # Batch size. batch_max_steps: 4096 # 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: false # 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: 5 # Number of steps to start to train discriminator. train_max_steps: 10 # Number of training steps. save_interval_steps: 5 # Interval steps to save checkpoint. eval_interval_steps: 5 # Interval steps to evaluate the network. log_interval_steps: 5 # Interval steps to record the training log. ########################################################### # OTHER SETTING # ########################################################### num_save_intermediate_results: 4 # Number of results to be saved as intermediate results.