from dataclasses import dataclass, field from .shared_configs import BaseGANVocoderConfig @dataclass class ParallelWaveganConfig(BaseGANVocoderConfig): """Defines parameters for ParallelWavegan vocoder. Args: model (str): Model name used for selecting the right configuration at initialization. Defaults to `gan`. discriminator_model (str): One of the discriminators from `TTS.vocoder.models.*_discriminator`. Defaults to 'parallel_wavegan_discriminator`. discriminator_model_params (dict): The discriminator model kwargs. Defaults to '{"num_layers": 10}` generator_model (str): One of the generators from TTS.vocoder.models.*`. Every other non-GAN vocoder model is considered as a generator too. Defaults to `parallel_wavegan_generator`. generator_model_param (dict): The generator model kwargs. Defaults to `{"upsample_factors": [4, 4, 4, 4], "stacks": 3, "num_res_blocks": 30}`. batch_size (int): Batch size used at training. Larger values use more memory. Defaults to 16. seq_len (int): Audio segment length used at training. Larger values use more memory. Defaults to 8192. pad_short (int): Additional padding applied to the audio samples shorter than `seq_len`. Defaults to 0. use_noise_augment (bool): enable / disable random noise added to the input waveform. The noise is added after computing the features. Defaults to True. use_cache (bool): enable / disable in memory caching of the computed features. It can cause OOM error if the system RAM is not large enough. Defaults to True. steps_to_start_discriminator (int): Number of steps required to start training the discriminator. Defaults to 0. use_stft_loss (bool):` enable / disable use of STFT loss originally used by ParallelWaveGAN model. Defaults to True. use_subband_stft (bool): enable / disable use of subband loss computation originally used by MultiBandMelgan model. Defaults to True. use_mse_gan_loss (bool): enable / disable using Mean Squeare Error GAN loss. Defaults to True. use_hinge_gan_loss (bool): enable / disable using Hinge GAN loss. You should choose either Hinge or MSE loss for training GAN models. Defaults to False. use_feat_match_loss (bool): enable / disable using Feature Matching loss originally used by MelGAN model. Defaults to True. use_l1_spec_loss (bool): enable / disable using L1 spectrogram loss originally used by HifiGAN model. Defaults to False. stft_loss_params (dict): STFT loss parameters. Default to `{"n_ffts": [1024, 2048, 512], "hop_lengths": [120, 240, 50], "win_lengths": [600, 1200, 240]}` stft_loss_weight (float): STFT loss weight that multiplies the computed loss before summing up the total model loss. Defaults to 0.5. subband_stft_loss_weight (float): Subband STFT loss weight that multiplies the computed loss before summing up the total loss. Defaults to 0. mse_G_loss_weight (float): MSE generator loss weight that multiplies the computed loss before summing up the total loss. faults to 2.5. hinge_G_loss_weight (float): Hinge generator loss weight that multiplies the computed loss before summing up the total loss. Defaults to 0. feat_match_loss_weight (float): Feature matching loss weight that multiplies the computed loss before summing up the total loss. faults to 0. l1_spec_loss_weight (float): L1 spectrogram loss weight that multiplies the computed loss before summing up the total loss. Defaults to 0. lr_gen (float): Generator model initial learning rate. Defaults to 0.0002. lr_disc (float): Discriminator model initial learning rate. Defaults to 0.0002. optimizer (torch.optim.Optimizer): Optimizer used for the training. Defaults to `AdamW`. optimizer_params (dict): Optimizer kwargs. Defaults to `{"betas": [0.8, 0.99], "weight_decay": 0.0}` lr_scheduler_gen (torch.optim.Scheduler): Learning rate scheduler for the generator. Defaults to `ExponentialLR`. lr_scheduler_gen_params (dict): Parameters for the generator learning rate scheduler. Defaults to `{"gamma": 0.5, "step_size": 200000, "last_epoch": -1}`. lr_scheduler_disc (torch.optim.Scheduler): Learning rate scheduler for the discriminator. Defaults to `ExponentialLR`. lr_scheduler_dict_params (dict): Parameters for the discriminator learning rate scheduler. Defaults to `{"gamma": 0.5, "step_size": 200000, "last_epoch": -1}`. """ model: str = "parallel_wavegan" # Model specific params discriminator_model: str = "parallel_wavegan_discriminator" discriminator_model_params: dict = field(default_factory=lambda: {"num_layers": 10}) generator_model: str = "parallel_wavegan_generator" generator_model_params: dict = field( default_factory=lambda: {"upsample_factors": [4, 4, 4, 4], "stacks": 3, "num_res_blocks": 30} ) # Training - overrides batch_size: int = 6 seq_len: int = 25600 pad_short: int = 2000 use_noise_augment: bool = False use_cache: bool = True steps_to_start_discriminator: int = 200000 target_loss: str = "loss_1" # LOSS PARAMETERS - overrides use_stft_loss: bool = True use_subband_stft_loss: bool = False use_mse_gan_loss: bool = True use_hinge_gan_loss: bool = False use_feat_match_loss: bool = False # requires MelGAN Discriminators (MelGAN and HifiGAN) use_l1_spec_loss: bool = False stft_loss_params: dict = field( default_factory=lambda: { "n_ffts": [1024, 2048, 512], "hop_lengths": [120, 240, 50], "win_lengths": [600, 1200, 240], } ) # loss weights - overrides stft_loss_weight: float = 0.5 subband_stft_loss_weight: float = 0 mse_G_loss_weight: float = 2.5 hinge_G_loss_weight: float = 0 feat_match_loss_weight: float = 0 l1_spec_loss_weight: float = 0 # optimizer overrides lr_gen: float = 0.0002 # Initial learning rate. lr_disc: float = 0.0002 # Initial learning rate. optimizer: str = "AdamW" optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "weight_decay": 0.0}) lr_scheduler_gen: str = "StepLR" # one of the schedulers from https:#pytorch.org/docs/stable/optim.html lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.5, "step_size": 200000, "last_epoch": -1}) lr_scheduler_disc: str = "StepLR" # one of the schedulers from https:#pytorch.org/docs/stable/optim.html lr_scheduler_disc_params: dict = field( default_factory=lambda: {"gamma": 0.5, "step_size": 200000, "last_epoch": -1} ) scheduler_after_epoch: bool = False