Spaces:
Runtime error
Runtime error
File size: 8,694 Bytes
9b2107c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 |
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.
|