mirror of https://github.com/coqui-ai/TTS.git
88 lines
3.6 KiB
Python
88 lines
3.6 KiB
Python
from dataclasses import dataclass, field
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from typing import List
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from coqpit import MISSING
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from TTS.config import BaseAudioConfig, BaseDatasetConfig, BaseTrainingConfig
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@dataclass
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class BaseVocoderConfig(BaseTrainingConfig):
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"""Shared parameters among all the vocoder models."""
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audio: BaseAudioConfig = field(default_factory=BaseAudioConfig)
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# dataloading
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use_noise_augment: bool = False # enable/disable random noise augmentation in spectrograms.
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eval_split_size: int = 10 # number of samples used for evaluation.
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# dataset
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data_path: str = MISSING # root data path. It finds all wav files recursively from there.
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feature_path: str = None # if you use precomputed features
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seq_len: int = MISSING # signal length used in training.
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pad_short: int = 0 # additional padding for short wavs
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conv_pad: int = 0 # additional padding against convolutions applied to spectrograms
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use_noise_augment: bool = False # add noise to the audio signal for augmentation
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use_cache: bool = False # use in memory cache to keep the computed features. This might cause OOM.
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# OPTIMIZER
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epochs: int = 10000 # total number of epochs to train.
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wd: float = 0.0 # Weight decay weight.
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@dataclass
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class BaseGANVocoderConfig(BaseVocoderConfig):
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"""Common config interface for all the GAN based vocoder models."""
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# LOSS PARAMETERS
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use_stft_loss: bool = True
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use_subband_stft_loss: bool = True
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use_mse_gan_loss: bool = True
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use_hinge_gan_loss: bool = True
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use_feat_match_loss: bool = True # requires MelGAN Discriminators (MelGAN and HifiGAN)
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use_l1_spec_loss: bool = True
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# loss weights
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stft_loss_weight: float = 0
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subband_stft_loss_weight: float = 0
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mse_G_loss_weight: float = 1
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hinge_G_loss_weight: float = 0
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feat_match_loss_weight: float = 10
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l1_spec_loss_weight: float = 45
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stft_loss_params: dict = field(
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default_factory=lambda: {
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"n_ffts": [1024, 2048, 512],
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"hop_lengths": [120, 240, 50],
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"win_lengths": [600, 1200, 240],
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}
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)
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l1_spec_loss_params: dict = field(
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default_factory=lambda: {
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"use_mel": True,
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"sample_rate": 22050,
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"n_fft": 1024,
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"hop_length": 256,
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"win_length": 1024,
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"n_mels": 80,
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"mel_fmin": 0.0,
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"mel_fmax": None,
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}
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)
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target_loss: str = "avg_G_loss" # loss value to pick the best model to save after each epoch
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# optimizer
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gen_clip_grad: float = -1 # Generator gradient clipping threshold. Apply gradient clipping if > 0
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disc_clip_grad: float = -1 # Discriminator gradient clipping threshold.
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lr_gen: float = 0.0002 # Initial learning rate.
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lr_disc: float = 0.0002 # Initial learning rate.
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optimizer: str = "AdamW"
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optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "weight_decay": 0.0})
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lr_scheduler_gen: str = "ExponentialLR" # one of the schedulers from https:#pytorch.org/docs/stable/optim.html
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lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999, "last_epoch": -1})
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lr_scheduler_disc: str = "ExponentialLR" # one of the schedulers from https:#pytorch.org/docs/stable/optim.html
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lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999, "last_epoch": -1})
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use_pqmf: bool = False # enable/disable using pqmf for multi-band training. (Multi-band MelGAN)
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steps_to_start_discriminator = 0 # start training the discriminator after this number of steps.
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diff_samples_for_G_and_D: bool = False # use different samples for G and D training steps.
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