| # This is the hyperparameter configuration file for Multi-Band MelGAN. | |
| # Please make sure this is adjusted for the Baker dataset. If you want to | |
| # apply to the other dataset, you might need to carefully change some parameters. | |
| # This configuration performs 1000k iters. | |
| ########################################################### | |
| # FEATURE EXTRACTION SETTING # | |
| ########################################################### | |
| sampling_rate: 24000 | |
| hop_size: 300 # Hop size. | |
| format: "npy" | |
| ########################################################### | |
| # GENERATOR NETWORK ARCHITECTURE SETTING # | |
| ########################################################### | |
| model_type: "multiband_melgan_generator" | |
| multiband_melgan_generator_params: | |
| out_channels: 4 # Number of output channels (number of subbands). | |
| kernel_size: 7 # Kernel size of initial and final conv layers. | |
| filters: 384 # Initial number of channels for conv layers. | |
| upsample_scales: [3, 5, 5] # List of Upsampling scales. | |
| stack_kernel_size: 3 # Kernel size of dilated conv layers in residual stack. | |
| stacks: 4 # Number of stacks in a single residual stack module. | |
| is_weight_norm: false # Use weight-norm or not. | |
| ########################################################### | |
| # DISCRIMINATOR NETWORK ARCHITECTURE SETTING # | |
| ########################################################### | |
| multiband_melgan_discriminator_params: | |
| out_channels: 1 # Number of output channels. | |
| scales: 3 # Number of multi-scales. | |
| downsample_pooling: "AveragePooling1D" # Pooling type for the input downsampling. | |
| downsample_pooling_params: # Parameters of the above pooling function. | |
| pool_size: 4 | |
| strides: 2 | |
| kernel_sizes: [5, 3] # List of kernel size. | |
| filters: 16 # Number of channels of the initial conv layer. | |
| max_downsample_filters: 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. | |
| alpha: 0.2 | |
| is_weight_norm: false # Use weight-norm or not. | |
| ########################################################### | |
| # STFT LOSS SETTING # | |
| ########################################################### | |
| stft_loss_params: | |
| fft_lengths: [1024, 2048, 512] # List of FFT size for STFT-based loss. | |
| frame_steps: [120, 240, 50] # List of hop size for STFT-based loss | |
| frame_lengths: [600, 1200, 240] # List of window length for STFT-based loss. | |
| subband_stft_loss_params: | |
| fft_lengths: [384, 683, 171] # List of FFT size for STFT-based loss. | |
| frame_steps: [30, 60, 10] # List of hop size for STFT-based loss | |
| frame_lengths: [150, 300, 60] # List of window length for STFT-based loss. | |
| ########################################################### | |
| # ADVERSARIAL LOSS SETTING # | |
| ########################################################### | |
| lambda_feat_match: 10.0 # Loss balancing coefficient for feature matching loss | |
| lambda_adv: 2.5 # Loss balancing coefficient for adversarial loss. | |
| ########################################################### | |
| # DATA LOADER SETTING # | |
| ########################################################### | |
| batch_size: 64 # Batch size for each GPU with assuming that gradient_accumulation_steps == 1. | |
| batch_max_steps: 9600 # Length of each audio in batch for training. Make sure dividable by hop_size. | |
| batch_max_steps_valid: 48000 # Length of each audio for validation. Make sure dividable by hope_size. | |
| 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. | |
| is_shuffle: true # shuffle dataset after each epoch. | |
| ########################################################### | |
| # OPTIMIZER & SCHEDULER SETTING # | |
| ########################################################### | |
| generator_optimizer_params: | |
| lr_fn: "PiecewiseConstantDecay" | |
| lr_params: | |
| boundaries: [100000, 200000, 300000, 400000, 500000, 600000, 700000] | |
| values: [0.001, 0.0005, 0.00025, 0.000125, 0.0000625, 0.00003125, 0.000015625, 0.000001] | |
| amsgrad: false | |
| discriminator_optimizer_params: | |
| lr_fn: "PiecewiseConstantDecay" | |
| lr_params: | |
| boundaries: [100000, 200000, 300000, 400000, 500000] | |
| values: [0.00025, 0.000125, 0.0000625, 0.00003125, 0.000015625, 0.000001] | |
| amsgrad: false | |
| gradient_accumulation_steps: 1 | |
| ########################################################### | |
| # INTERVAL SETTING # | |
| ########################################################### | |
| discriminator_train_start_steps: 200000 # steps begin training discriminator | |
| train_max_steps: 4000000 # Number of training steps. | |
| save_interval_steps: 20000 # Interval steps to save checkpoint. | |
| eval_interval_steps: 5000 # Interval steps to evaluate the network. | |
| log_interval_steps: 200 # Interval steps to record the training log. | |
| ########################################################### | |
| # OTHER SETTING # | |
| ########################################################### | |
| num_save_intermediate_results: 1 # Number of batch to be saved as intermediate results. |