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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
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