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| # This is the hyperparameter configuration file for Parallel WaveGAN. | |
| # Please make sure this is adjusted for the LibriTTS corpus. 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: 20 # 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_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: 24000 # 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: false # 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: 1000000 # 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. | |