from dataclasses import dataclass, field from typing import Dict from TTS.vocoder.configs.shared_configs import BaseGANVocoderConfig @dataclass class UnivnetConfig(BaseGANVocoderConfig): """Defines parameters for UnivNet vocoder. Example: >>> from TTS.vocoder.configs import UnivNetConfig >>> config = UnivNetConfig() Args: model (str): Model name used for selecting the right model at initialization. Defaults to `UnivNet`. discriminator_model (str): One of the discriminators from `TTS.vocoder.models.*_discriminator`. Defaults to 'UnivNet_discriminator`. 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 `UnivNet_generator`. generator_model_params (dict): Parameters of the generator model. Defaults to ` { "use_mel": True, "sample_rate": 22050, "n_fft": 1024, "hop_length": 256, "win_length": 1024, "n_mels": 80, "mel_fmin": 0.0, "mel_fmax": None, } ` batch_size (int): Batch size used at training. Larger values use more memory. Defaults to 32. 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. 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 univnet 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] }` l1_spec_loss_params (dict): L1 spectrogram loss parameters. Default to `{ "use_mel": True, "sample_rate": 22050, "n_fft": 1024, "hop_length": 256, "win_length": 1024, "n_mels": 80, "mel_fmin": 0.0, "mel_fmax": None, }` 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 108. l1_spec_loss_weight (float): L1 spectrogram loss weight that multiplies the computed loss before summing up the total loss. Defaults to 0. """ model: str = "univnet" batch_size: int = 32 # model specific params discriminator_model: str = "univnet_discriminator" generator_model: str = "univnet_generator" generator_model_params: Dict = field( default_factory=lambda: { "in_channels": 64, "out_channels": 1, "hidden_channels": 32, "cond_channels": 80, "upsample_factors": [8, 8, 4], "lvc_layers_each_block": 4, "lvc_kernel_size": 3, "kpnet_hidden_channels": 64, "kpnet_conv_size": 3, "dropout": 0.0, } ) # 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 univnet) use_l1_spec_loss: bool = False # loss weights - overrides stft_loss_weight: float = 2.5 stft_loss_params: Dict = field( default_factory=lambda: { "n_ffts": [1024, 2048, 512], "hop_lengths": [120, 240, 50], "win_lengths": [600, 1200, 240], } ) subband_stft_loss_weight: float = 0 mse_G_loss_weight: float = 1 hinge_G_loss_weight: float = 0 feat_match_loss_weight: float = 0 l1_spec_loss_weight: float = 0 l1_spec_loss_params: Dict = field( default_factory=lambda: { "use_mel": True, "sample_rate": 22050, "n_fft": 1024, "hop_length": 256, "win_length": 1024, "n_mels": 80, "mel_fmin": 0.0, "mel_fmax": None, } ) # optimizer parameters lr_gen: float = 1e-4 # Initial learning rate. lr_disc: float = 1e-4 # Initial learning rate. lr_scheduler_gen: str = None # 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 = None # 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}) optimizer_params: Dict = field(default_factory=lambda: {"betas": [0.5, 0.9], "weight_decay": 0.0}) steps_to_start_discriminator: int = 200000 def __post_init__(self): super().__post_init__() self.generator_model_params["cond_channels"] = self.audio.num_mels