183 lines
8.5 KiB
Python
183 lines
8.5 KiB
Python
|
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.
|