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