135 lines
7.0 KiB
Python
135 lines
7.0 KiB
Python
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
|