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from dataclasses import dataclass, field
from .shared_configs import BaseGANVocoderConfig
@dataclass
class FullbandMelganConfig(BaseGANVocoderConfig):
"""Defines parameters for FullBand MelGAN vocoder.
Example:
>>> from TTS.vocoder.configs import FullbandMelganConfig
>>> config = FullbandMelganConfig()
Args:
model (str):
Model name used for selecting the right model at initialization. Defaults to `fullband_melgan`.
discriminator_model (str): One of the discriminators from `TTS.vocoder.models.*_discriminator`. Defaults to
'melgan_multiscale_discriminator`.
discriminator_model_params (dict): The discriminator model parameters. Defaults to
'{"base_channels": 16, "max_channels": 1024, "downsample_factors": [4, 4, 4, 4]}`
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 `melgan_generator`.
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.
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 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 = "fullband_melgan"
# Model specific params
discriminator_model: str = "melgan_multiscale_discriminator"
discriminator_model_params: dict = field(
default_factory=lambda: {"base_channels": 16, "max_channels": 512, "downsample_factors": [4, 4, 4]}
)
generator_model: str = "melgan_generator"
generator_model_params: dict = field(
default_factory=lambda: {"upsample_factors": [8, 8, 2, 2], "num_res_blocks": 4}
)
# Training - overrides
batch_size: int = 16
seq_len: int = 8192
pad_short: int = 2000
use_noise_augment: bool = True
use_cache: bool = True
# 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 = True # 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 = 108
l1_spec_loss_weight: float = 0.0
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