107 lines
5.0 KiB
Python
107 lines
5.0 KiB
Python
from dataclasses import dataclass, field
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from TTS.vocoder.configs.shared_configs import BaseGANVocoderConfig
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@dataclass
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class MelganConfig(BaseGANVocoderConfig):
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"""Defines parameters for MelGAN vocoder.
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Example:
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>>> from TTS.vocoder.configs import MelganConfig
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>>> config = MelganConfig()
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Args:
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model (str):
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Model name used for selecting the right model at initialization. Defaults to `melgan`.
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discriminator_model (str): One of the discriminators from `TTS.vocoder.models.*_discriminator`. Defaults to
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'melgan_multiscale_discriminator`.
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discriminator_model_params (dict): The discriminator model parameters. Defaults to
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'{"base_channels": 16, "max_channels": 1024, "downsample_factors": [4, 4, 4, 4]}`
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generator_model (str): One of the generators from TTS.vocoder.models.*`. Every other non-GAN vocoder model is
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considered as a generator too. Defaults to `melgan_generator`.
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batch_size (int):
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Batch size used at training. Larger values use more memory. Defaults to 16.
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seq_len (int):
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Audio segment length used at training. Larger values use more memory. Defaults to 8192.
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pad_short (int):
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Additional padding applied to the audio samples shorter than `seq_len`. Defaults to 0.
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use_noise_augment (bool):
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enable / disable random noise added to the input waveform. The noise is added after computing the
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features. Defaults to True.
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use_cache (bool):
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enable / disable in memory caching of the computed features. It can cause OOM error if the system RAM is
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not large enough. Defaults to True.
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use_stft_loss (bool):
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enable / disable use of STFT loss originally used by ParallelWaveGAN model. Defaults to True.
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use_subband_stft (bool):
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enable / disable use of subband loss computation originally used by MultiBandMelgan model. Defaults to True.
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use_mse_gan_loss (bool):
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enable / disable using Mean Squeare Error GAN loss. Defaults to True.
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use_hinge_gan_loss (bool):
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enable / disable using Hinge GAN loss. You should choose either Hinge or MSE loss for training GAN models.
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Defaults to False.
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use_feat_match_loss (bool):
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enable / disable using Feature Matching loss originally used by MelGAN model. Defaults to True.
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use_l1_spec_loss (bool):
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enable / disable using L1 spectrogram loss originally used by HifiGAN model. Defaults to False.
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stft_loss_params (dict): STFT loss parameters. Default to
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`{"n_ffts": [1024, 2048, 512], "hop_lengths": [120, 240, 50], "win_lengths": [600, 1200, 240]}`
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stft_loss_weight (float): STFT loss weight that multiplies the computed loss before summing up the total
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model loss. Defaults to 0.5.
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subband_stft_loss_weight (float):
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Subband STFT loss weight that multiplies the computed loss before summing up the total loss. Defaults to 0.
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mse_G_loss_weight (float):
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MSE generator loss weight that multiplies the computed loss before summing up the total loss. faults to 2.5.
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hinge_G_loss_weight (float):
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Hinge generator loss weight that multiplies the computed loss before summing up the total loss. Defaults to 0.
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feat_match_loss_weight (float):
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Feature matching loss weight that multiplies the computed loss before summing up the total loss. faults to 108.
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l1_spec_loss_weight (float):
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L1 spectrogram loss weight that multiplies the computed loss before summing up the total loss. Defaults to 0.
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"""
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model: str = "melgan"
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# Model specific params
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discriminator_model: str = "melgan_multiscale_discriminator"
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discriminator_model_params: dict = field(
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default_factory=lambda: {"base_channels": 16, "max_channels": 1024, "downsample_factors": [4, 4, 4, 4]}
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)
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generator_model: str = "melgan_generator"
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generator_model_params: dict = field(
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default_factory=lambda: {"upsample_factors": [8, 8, 2, 2], "num_res_blocks": 3}
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)
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# Training - overrides
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batch_size: int = 16
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seq_len: int = 8192
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pad_short: int = 2000
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use_noise_augment: bool = True
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use_cache: bool = True
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# LOSS PARAMETERS - overrides
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use_stft_loss: bool = True
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use_subband_stft_loss: bool = False
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use_mse_gan_loss: bool = True
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use_hinge_gan_loss: bool = False
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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 = False
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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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# loss weights - overrides
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stft_loss_weight: float = 0.5
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subband_stft_loss_weight: float = 0
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mse_G_loss_weight: float = 2.5
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hinge_G_loss_weight: float = 0
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feat_match_loss_weight: float = 108
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l1_spec_loss_weight: float = 0
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