full_band_melgan.v2.yaml

# This is the hyperparameter configuration file for MelGAN.
# Please make sure this is adjusted for the LJSpeech dataset. If you want to
# apply to the other dataset, you might need to carefully change some parameters.
# This cofiguration requires ~ 11 GB memory and takes 1 week 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 22.05kHz). The number of iteraions
# is now shown in the paper so currently we train 1M iterations (not sure enough
# to converge). The optimizer setting is based on @dathudeptrai advice.
# https://github.com/kan-bayashi/ParallelWaveGAN/issues/143#issuecomment-632539906

###########################################################
#                FEATURE EXTRACTION SETTING               #
###########################################################
sampling_rate: 22050     # 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: 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: true       # 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_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: 512                 # Initial number of channels for conv layers.
    upsample_scales: [8, 8, 4]    # 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.
    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: 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                     #
###########################################################
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: false # Whether to use feature matching loss.
lambda_adv: 2.5            # Loss balancing coefficient for adversarial loss.

###########################################################
#                  DATA LOADER SETTING                    #
###########################################################
batch_size: 48             # Batch size.
batch_max_steps: 16384     # 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_type: "Adam"       # Generator's optimizer type.
generator_optimizer_params:
    lr: 1.0e-3                          # Generator's learning rate.
    eps: 1.0e-7                         # Generator's epsilon.
    weight_decay: 0.0                   # Generator's weight decay coefficient.
    amsgrad: true
generator_grad_norm: -1                 # Generator's gradient norm.
generator_scheduler_type: "MultiStepLR" # Generator's scheduler type.
generator_scheduler_params:
    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_type: "Adam"        # Discriminator's optimizer type.
discriminator_optimizer_params:
    lr: 1.0e-3                              # Discriminator's learning rate.
    eps: 1.0e-7                             # Discriminator's epsilon.
    weight_decay: 0.0                       # Discriminator's weight decay coefficient.
    amsgrad: true
discriminator_grad_norm: -1                 # Discriminator's gradient norm.
discriminator_scheduler_type: "MultiStepLR" # Discriminator's scheduler type.
discriminator_scheduler_params:
    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: 50000              # Interval steps to save checkpoint.
eval_interval_steps: 1000               # Interval steps to evaluate the network.
log_interval_steps: 1000                # Interval steps to record the training log.

###########################################################
#                     OTHER SETTING                       #
###########################################################
num_save_intermediate_results: 4  # Number of results to be saved as intermediate results.