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from dataclasses import dataclass, field
from TTS.config import BaseAudioConfig, BaseTrainingConfig
@dataclass
class BaseVocoderConfig(BaseTrainingConfig):
"""Shared parameters among all the vocoder models.
Args:
audio (BaseAudioConfig):
Audio processor config instance. Defaultsto `BaseAudioConfig()`.
use_noise_augment (bool):
Augment the input audio with random noise. Defaults to False/
eval_split_size (int):
Number of instances used for evaluation. Defaults to 10.
data_path (str):
Root path of the training data. All the audio files found recursively from this root path are used for
training. Defaults to `""`.
feature_path (str):
Root path to the precomputed feature files. Defaults to None.
seq_len (int):
Length of the waveform segments used for training. Defaults to 1000.
pad_short (int):
Extra padding for the waveforms shorter than `seq_len`. Defaults to 0.
conv_path (int):
Extra padding for the feature frames against convolution of the edge frames. Defaults to MISSING.
Defaults to 0.
use_cache (bool):
enable / disable in memory caching of the computed features. If the RAM is not enough, if may cause OOM.
Defaults to False.
epochs (int):
Number of training epochs to. Defaults to 10000.
wd (float):
Weight decay.
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}`
"""
audio: BaseAudioConfig = field(default_factory=BaseAudioConfig)
# dataloading
use_noise_augment: bool = False # enable/disable random noise augmentation in spectrograms.
eval_split_size: int = 10 # number of samples used for evaluation.
# dataset
data_path: str = "" # root data path. It finds all wav files recursively from there.
feature_path: str = None # if you use precomputed features
seq_len: int = 1000 # signal length used in training.
pad_short: int = 0 # additional padding for short wavs
conv_pad: int = 0 # additional padding against convolutions applied to spectrograms
use_cache: bool = False # use in memory cache to keep the computed features. This might cause OOM.
# OPTIMIZER
epochs: int = 10000 # total number of epochs to train.
wd: float = 0.0 # Weight decay weight.
optimizer: str = "AdamW"
optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "weight_decay": 0.0})
@dataclass
class BaseGANVocoderConfig(BaseVocoderConfig):
"""Base config class used among all the GAN based vocoders.
Args:
use_stft_loss (bool):
enable / disable the use of STFT loss. Defaults to True.
use_subband_stft_loss (bool):
enable / disable the use of Subband STFT loss. Defaults to True.
use_mse_gan_loss (bool):
enable / disable the use of Mean Squared Error based GAN loss. Defaults to True.
use_hinge_gan_loss (bool):
enable / disable the use of Hinge GAN loss. Defaults to True.
use_feat_match_loss (bool):
enable / disable feature matching loss. Defaults to True.
use_l1_spec_loss (bool):
enable / disable L1 spectrogram loss. Defaults to True.
stft_loss_weight (float):
Loss weight that multiplies the computed loss value. Defaults to 0.
subband_stft_loss_weight (float):
Loss weight that multiplies the computed loss value. Defaults to 0.
mse_G_loss_weight (float):
Loss weight that multiplies the computed loss value. Defaults to 1.
hinge_G_loss_weight (float):
Loss weight that multiplies the computed loss value. Defaults to 0.
feat_match_loss_weight (float):
Loss weight that multiplies the computed loss value. Defaults to 100.
l1_spec_loss_weight (float):
Loss weight that multiplies the computed loss value. Defaults to 45.
stft_loss_params (dict):
Parameters for the STFT loss. Defaults to `{"n_ffts": [1024, 2048, 512], "hop_lengths": [120, 240, 50], "win_lengths": [600, 1200, 240]}`.
l1_spec_loss_params (dict):
Parameters for the L1 spectrogram loss. 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,
}`
target_loss (str):
Target loss name that defines the quality of the model. Defaults to `G_avg_loss`.
grad_clip (list):
A list of gradient clipping theresholds for each optimizer. Any value less than 0 disables clipping.
Defaults to [5, 5].
lr_gen (float):
Generator model initial learning rate. Defaults to 0.0002.
lr_disc (float):
Discriminator model initial learning rate. Defaults to 0.0002.
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": 0.999, "last_epoch": -1}`.
lr_scheduler_disc (torch.optim.Scheduler):
Learning rate scheduler for the discriminator. Defaults to `ExponentialLR`.
lr_scheduler_disc_params (dict):
Parameters for the discriminator learning rate scheduler. Defaults to `{"gamma": 0.999, "last_epoch": -1}`.
scheduler_after_epoch (bool):
Whether to update the learning rate schedulers after each epoch. Defaults to True.
use_pqmf (bool):
enable / disable PQMF for subband approximation at training. Defaults to False.
steps_to_start_discriminator (int):
Number of steps required to start training the discriminator. Defaults to 0.
diff_samples_for_G_and_D (bool):
enable / disable use of different training samples for the generator and the discriminator iterations.
Enabling it results in slower iterations but faster convergance in some cases. Defaults to False.
"""
model: str = "gan"
# LOSS PARAMETERS
use_stft_loss: bool = True
use_subband_stft_loss: bool = True
use_mse_gan_loss: bool = True
use_hinge_gan_loss: bool = True
use_feat_match_loss: bool = True # requires MelGAN Discriminators (MelGAN and HifiGAN)
use_l1_spec_loss: bool = True
# loss weights
stft_loss_weight: float = 0
subband_stft_loss_weight: float = 0
mse_G_loss_weight: float = 1
hinge_G_loss_weight: float = 0
feat_match_loss_weight: float = 100
l1_spec_loss_weight: float = 45
stft_loss_params: dict = field(
default_factory=lambda: {
"n_ffts": [1024, 2048, 512],
"hop_lengths": [120, 240, 50],
"win_lengths": [600, 1200, 240],
}
)
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,
}
)
target_loss: str = "loss_0" # loss value to pick the best model to save after each epoch
# optimizer
grad_clip: float = field(default_factory=lambda: [5, 5])
lr_gen: float = 0.0002 # Initial learning rate.
lr_disc: float = 0.0002 # Initial learning rate.
lr_scheduler_gen: str = "ExponentialLR" # 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 = "ExponentialLR" # 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})
scheduler_after_epoch: bool = True
use_pqmf: bool = False # enable/disable using pqmf for multi-band training. (Multi-band MelGAN)
steps_to_start_discriminator = 0 # start training the discriminator after this number of steps.
diff_samples_for_G_and_D: bool = False # use different samples for G and D training steps.
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