from dataclasses import dataclass, field from TTS.config import BaseAudioConfig, BaseTrainingConfig @dataclass class BaseVocoderConfig(BaseTrainingConfig): """Shared parameters among all the vocoder models. Args: audio (BaseAudioConfig): Audio processor config instance. Defaultsto `BaseAudioConfig()`. use_noise_augment (bool): Augment the input audio with random noise. Defaults to False/ eval_split_size (int): Number of instances used for evaluation. Defaults to 10. data_path (str): Root path of the training data. All the audio files found recursively from this root path are used for training. Defaults to `""`. feature_path (str): Root path to the precomputed feature files. Defaults to None. seq_len (int): Length of the waveform segments used for training. Defaults to 1000. pad_short (int): Extra padding for the waveforms shorter than `seq_len`. Defaults to 0. conv_path (int): Extra padding for the feature frames against convolution of the edge frames. Defaults to MISSING. Defaults to 0. use_cache (bool): enable / disable in memory caching of the computed features. If the RAM is not enough, if may cause OOM. Defaults to False. epochs (int): Number of training epochs to. Defaults to 10000. wd (float): Weight decay. 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}` """ audio: BaseAudioConfig = field(default_factory=BaseAudioConfig) # dataloading use_noise_augment: bool = False # enable/disable random noise augmentation in spectrograms. eval_split_size: int = 10 # number of samples used for evaluation. # dataset data_path: str = "" # root data path. It finds all wav files recursively from there. feature_path: str = None # if you use precomputed features seq_len: int = 1000 # signal length used in training. pad_short: int = 0 # additional padding for short wavs conv_pad: int = 0 # additional padding against convolutions applied to spectrograms use_cache: bool = False # use in memory cache to keep the computed features. This might cause OOM. # OPTIMIZER epochs: int = 500 # total number of epochs to train. wd: float = 0.0 # Weight decay weight. optimizer: str = "AdamW" optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "weight_decay": 0.0}) @dataclass class BaseGANVocoderConfig(BaseVocoderConfig): """Base config class used among all the GAN based vocoders. Args: use_stft_loss (bool): enable / disable the use of STFT loss. Defaults to True. use_subband_stft_loss (bool): enable / disable the use of Subband STFT loss. Defaults to True. use_mse_gan_loss (bool): enable / disable the use of Mean Squared Error based GAN loss. Defaults to True. use_hinge_gan_loss (bool): enable / disable the use of Hinge GAN loss. Defaults to True. use_feat_match_loss (bool): enable / disable feature matching loss. Defaults to True. use_l1_spec_loss (bool): enable / disable L1 spectrogram loss. Defaults to True. stft_loss_weight (float): Loss weight that multiplies the computed loss value. Defaults to 0. subband_stft_loss_weight (float): Loss weight that multiplies the computed loss value. Defaults to 0. mse_G_loss_weight (float): Loss weight that multiplies the computed loss value. Defaults to 1. hinge_G_loss_weight (float): Loss weight that multiplies the computed loss value. Defaults to 0. feat_match_loss_weight (float): Loss weight that multiplies the computed loss value. Defaults to 100. l1_spec_loss_weight (float): Loss weight that multiplies the computed loss value. Defaults to 45. stft_loss_params (dict): Parameters for the STFT loss. Defaults to `{"n_ffts": [1024, 2048, 512], "hop_lengths": [120, 240, 50], "win_lengths": [600, 1200, 240]}`. l1_spec_loss_params (dict): Parameters for the L1 spectrogram loss. Defaults to `{ "use_mel": True, "sample_rate": 32000, "n_fft": 1024, "hop_length": 256, "win_length": 1024, "n_mels": 80, "mel_fmin": 0.0, "mel_fmax": None, }` target_loss (str): Target loss name that defines the quality of the model. Defaults to `G_avg_loss`. grad_clip (list): A list of gradient clipping theresholds for each optimizer. Any value less than 0 disables clipping. Defaults to [5, 5]. lr_gen (float): Generator model initial learning rate. Defaults to 0.0002. lr_disc (float): Discriminator model initial learning rate. Defaults to 0.0002. 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": 0.999, "last_epoch": -1}`. lr_scheduler_disc (torch.optim.Scheduler): Learning rate scheduler for the discriminator. Defaults to `ExponentialLR`. lr_scheduler_disc_params (dict): Parameters for the discriminator learning rate scheduler. Defaults to `{"gamma": 0.999, "last_epoch": -1}`. scheduler_after_epoch (bool): Whether to update the learning rate schedulers after each epoch. Defaults to True. use_pqmf (bool): enable / disable PQMF for subband approximation at training. Defaults to False. steps_to_start_discriminator (int): Number of steps required to start training the discriminator. Defaults to 0. diff_samples_for_G_and_D (bool): enable / disable use of different training samples for the generator and the discriminator iterations. Enabling it results in slower iterations but faster convergance in some cases. Defaults to False. """ model: str = "gan" # LOSS PARAMETERS use_stft_loss: bool = True use_subband_stft_loss: bool = True use_mse_gan_loss: bool = True use_hinge_gan_loss: bool = True use_feat_match_loss: bool = True # requires MelGAN Discriminators (MelGAN and HifiGAN) use_l1_spec_loss: bool = True # loss weights stft_loss_weight: float = 0 subband_stft_loss_weight: float = 0 mse_G_loss_weight: float = 1 hinge_G_loss_weight: float = 0 feat_match_loss_weight: float = 100 l1_spec_loss_weight: float = 45 stft_loss_params: dict = field( default_factory=lambda: { "n_ffts": [1024, 2048, 512], "hop_lengths": [120, 240, 50], "win_lengths": [600, 1200, 240], } ) l1_spec_loss_params: dict = field( default_factory=lambda: { "use_mel": True, "sample_rate": 32000, "n_fft": 1024, "hop_length": 256, "win_length": 1024, "n_mels": 80, "mel_fmin": 0.0, "mel_fmax": None, } ) target_loss: str = "loss_0" # loss value to pick the best model to save after each epoch # optimizer grad_clip: float = field(default_factory=lambda: [5, 5]) lr_gen: float = 0.0002 # Initial learning rate. lr_disc: float = 0.0002 # Initial learning rate. lr_scheduler_gen: str = "ExponentialLR" # one of the schedulers from https:#pytorch.org/docs/stable/optim.html lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999, "last_epoch": -1}) lr_scheduler_disc: str = "ExponentialLR" # one of the schedulers from https:#pytorch.org/docs/stable/optim.html lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999, "last_epoch": -1}) scheduler_after_epoch: bool = True use_pqmf: bool = False # enable/disable using pqmf for multi-band training. (Multi-band MelGAN) steps_to_start_discriminator = 0 # start training the discriminator after this number of steps. diff_samples_for_G_and_D: bool = False # use different samples for G and D training steps.