145 lines
7.5 KiB
Python
145 lines
7.5 KiB
Python
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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 MultibandMelganConfig(BaseGANVocoderConfig):
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"""Defines parameters for MultiBandMelGAN vocoder.
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Example:
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>>> from TTS.vocoder.configs import MultibandMelganConfig
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>>> config = MultibandMelganConfig()
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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 `multiband_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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'{
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"base_channels": 16,
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"max_channels": 512,
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"downsample_factors": [4, 4, 4]
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}`
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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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generator_model_param (dict):
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The generator model parameters. Defaults to `{"upsample_factors": [8, 4, 2], "num_res_blocks": 4}`.
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use_pqmf (bool):
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enable / disable PQMF modulation for multi-band training. Defaults to True.
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lr_gen (float):
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Initial learning rate for the generator model. Defaults to 0.0001.
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lr_disc (float):
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Initial learning rate for the discriminator model. Defaults to 0.0001.
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optimizer (torch.optim.Optimizer):
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Optimizer used for the training. Defaults to `AdamW`.
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optimizer_params (dict):
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Optimizer kwargs. Defaults to `{"betas": [0.8, 0.99], "weight_decay": 0.0}`
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lr_scheduler_gen (torch.optim.Scheduler):
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Learning rate scheduler for the generator. Defaults to `MultiStepLR`.
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lr_scheduler_gen_params (dict):
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Parameters for the generator learning rate scheduler. Defaults to
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`{"gamma": 0.5, "milestones": [100000, 200000, 300000, 400000, 500000, 600000]}`.
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lr_scheduler_disc (torch.optim.Scheduler):
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Learning rate scheduler for the discriminator. Defaults to `MultiStepLR`.
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lr_scheduler_dict_params (dict):
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Parameters for the discriminator learning rate scheduler. Defaults to
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`{"gamma": 0.5, "milestones": [100000, 200000, 300000, 400000, 500000, 600000]}`.
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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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steps_to_start_discriminator (int):
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Number of steps required to start training the discriminator. Defaults to 0.
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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 = "multiband_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": 512, "downsample_factors": [4, 4, 4]}
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)
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generator_model: str = "multiband_melgan_generator"
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generator_model_params: dict = field(default_factory=lambda: {"upsample_factors": [8, 4, 2], "num_res_blocks": 4})
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use_pqmf: bool = True
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# optimizer - overrides
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lr_gen: float = 0.0001 # Initial learning rate.
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lr_disc: float = 0.0001 # 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 = "MultiStepLR" # one of the schedulers from https:#pytorch.org/docs/stable/optim.html
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lr_scheduler_gen_params: dict = field(
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default_factory=lambda: {"gamma": 0.5, "milestones": [100000, 200000, 300000, 400000, 500000, 600000]}
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)
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lr_scheduler_disc: str = "MultiStepLR" # one of the schedulers from https:#pytorch.org/docs/stable/optim.html
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lr_scheduler_disc_params: dict = field(
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default_factory=lambda: {"gamma": 0.5, "milestones": [100000, 200000, 300000, 400000, 500000, 600000]}
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)
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# Training - overrides
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batch_size: int = 64
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seq_len: int = 16384
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pad_short: int = 2000
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use_noise_augment: bool = False
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use_cache: bool = True
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steps_to_start_discriminator: bool = 200000
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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 = True
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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 = False # requires MelGAN Discriminators (MelGAN and HifiGAN)
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use_l1_spec_loss: bool = False
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subband_stft_loss_params: dict = field(
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default_factory=lambda: {"n_ffts": [384, 683, 171], "hop_lengths": [30, 60, 10], "win_lengths": [150, 300, 60]}
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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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