class_docstr: Defines parameters for UnivNet vocoder.
  
  Example:
  
      >>> from TTS.vocoder.configs import UnivNetConfig
      >>> config = UnivNetConfig()
  
  Args:
      model (str):
          Model name used for selecting the right model at initialization. Defaults to `UnivNet`.
      discriminator_model (str): One of the discriminators from `TTS.vocoder.models.*_discriminator`. Defaults to
          'UnivNet_discriminator`.
      generator_model (str): One of the generators from TTS.vocoder.models.*`. Every other non-GAN vocoder model is
          considered as a generator too. Defaults to `UnivNet_generator`.
      generator_model_params (dict): Parameters of the generator model. 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,
          }
          `
      batch_size (int):
          Batch size used at training. Larger values use more memory. Defaults to 32.
      seq_len (int):
          Audio segment length used at training. Larger values use more memory. Defaults to 8192.
      pad_short (int):
          Additional padding applied to the audio samples shorter than `seq_len`. Defaults to 0.
      use_noise_augment (bool):
          enable / disable random noise added to the input waveform. The noise is added after computing the
          features. Defaults to True.
      use_cache (bool):
          enable / disable in memory caching of the computed features. It can cause OOM error if the system RAM is
          not large enough. Defaults to True.
      use_stft_loss (bool):
          enable / disable use of STFT loss originally used by ParallelWaveGAN model. Defaults to True.
      use_subband_stft (bool):
          enable / disable use of subband loss computation originally used by MultiBandMelgan model. Defaults to True.
      use_mse_gan_loss (bool):
          enable / disable using Mean Squeare Error GAN loss. Defaults to True.
      use_hinge_gan_loss (bool):
          enable / disable using Hinge GAN loss. You should choose either Hinge or MSE loss for training GAN models.
          Defaults to False.
      use_feat_match_loss (bool):
          enable / disable using Feature Matching loss originally used by MelGAN model. Defaults to True.
      use_l1_spec_loss (bool):
          enable / disable using L1 spectrogram loss originally used by univnet model. Defaults to False.
      stft_loss_params (dict):
          STFT loss parameters. Default to
          `{
              "n_ffts": [1024, 2048, 512],
              "hop_lengths": [120, 240, 50],
              "win_lengths": [600, 1200, 240]
          }`
      l1_spec_loss_params (dict):
          L1 spectrogram loss parameters. Default 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,
          }`
      stft_loss_weight (float): STFT loss weight that multiplies the computed loss before summing up the total
          model loss. Defaults to 0.5.
      subband_stft_loss_weight (float):
          Subband STFT loss weight that multiplies the computed loss before summing up the total loss. Defaults to 0.
      mse_G_loss_weight (float):
          MSE generator loss weight that multiplies the computed loss before summing up the total loss. faults to 2.5.
      hinge_G_loss_weight (float):
          Hinge generator loss weight that multiplies the computed loss before summing up the total loss. Defaults to 0.
      feat_match_loss_weight (float):
          Feature matching loss weight that multiplies the computed loss before summing up the total loss. faults to 108.
      l1_spec_loss_weight (float):
          L1 spectrogram loss weight that multiplies the computed loss before summing up the total loss. Defaults to 0.
class_docstr_tok: ["Defines","parameters","for","UnivNet","vocoder",".","Example",":",">",">",">","from","TTS.vocoder.configs","import","UnivNetConfig",">",">",">","config","=","UnivNetConfig","(",")","Args",":","model","(","str",")",":","Model","name","used","for","selecting","the","right","model","at","initialization",".","Defaults","to","`","UnivNet","`",".","discriminator_model","(","str",")",":","One","of","the","discriminators","from","`","TTS.vocoder.models.","*","_discriminator","`",".","Defaults","to","'UnivNet_discriminator","`",".","generator_model","(","str",")",":","One","of","the","generators","from","TTS.vocoder.models.","*","`",".","Every","other","non-GAN","vocoder","model","is","considered","as","a","generator","too",".","Defaults","to","`","UnivNet_generator","`",".","generator_model_params","(","dict",")",":","Parameters","of","the","generator","model",".","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",",","}","`","batch_size","(","int",")",":","Batch","size","used","at","training",".","Larger","values","use","more","memory",".","Defaults","to","32.","seq_len","(","int",")",":","Audio","segment","length","used","at","training",".","Larger","values","use","more","memory",".","Defaults","to","8192.","pad_short","(","int",")",":","Additional","padding","applied","to","the","audio","samples","shorter","than","`","seq_len","`",".","Defaults","to","0.","use_noise_augment","(","bool",")",":","enable","\/","disable","random","noise","added","to","the","input","waveform",".","The","noise","is","added","after","computing","the","features",".","Defaults","to","True",".","use_cache","(","bool",")",":","enable","\/","disable","in","memory","caching","of","the","computed","features",".","It","can","cause","OOM","error","if","the","system","RAM","is","not","large","enough",".","Defaults","to","True",".","use_stft_loss","(","bool",")",":","enable","\/","disable","use","of","STFT","loss","originally","used","by","ParallelWaveGAN","model",".","Defaults","to","True",".","use_subband_stft","(","bool",")",":","enable","\/","disable","use","of","subband","loss","computation","originally","used","by","MultiBandMelgan","model",".","Defaults","to","True",".","use_mse_gan_loss","(","bool",")",":","enable","\/","disable","using","Mean","Squeare","Error","GAN","loss",".","Defaults","to","True",".","use_hinge_gan_loss","(","bool",")",":","enable","\/","disable","using","Hinge","GAN","loss",".","You","should","choose","either","Hinge","or","MSE","loss","for","training","GAN","models",".","Defaults","to","False",".","use_feat_match_loss","(","bool",")",":","enable","\/","disable","using","Feature","Matching","loss","originally","used","by","MelGAN","model",".","Defaults","to","True",".","use_l1_spec_loss","(","bool",")",":","enable","\/","disable","using","L1","spectrogram","loss","originally","used","by","univnet","model",".","Defaults","to","False",".","stft_loss_params","(","dict",")",":","STFT","loss","parameters",".","Default","to","`","{","``","n_ffts","''",":","[","1024",",","2048",",","512","]",",","``","hop_lengths","''",":","[","120",",","240",",","50","]",",","``","win_lengths","''",":","[","600",",","1200",",","240","]","}","`","l1_spec_loss_params","(","dict",")",":","L1","spectrogram","loss","parameters",".","Default","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",",","}","`","stft_loss_weight","(","float",")",":","STFT","loss","weight","that","multiplies","the","computed","loss","before","summing","up","the","total","model","loss",".","Defaults","to","0.5.","subband_stft_loss_weight","(","float",")",":","Subband","STFT","loss","weight","that","multiplies","the","computed","loss","before","summing","up","the","total","loss",".","Defaults","to","0.","mse_G_loss_weight","(","float",")",":","MSE","generator","loss","weight","that","multiplies","the","computed","loss","before","summing","up","the","total","loss",".","faults","to","2.5.","hinge_G_loss_weight","(","float",")",":","Hinge","generator","loss","weight","that","multiplies","the","computed","loss","before","summing","up","the","total","loss",".","Defaults","to","0.","feat_match_loss_weight","(","float",")",":","Feature","matching","loss","weight","that","multiplies","the","computed","loss","before","summing","up","the","total","loss",".","faults","to","108.","l1_spec_loss_weight","(","float",")",":","L1","spectrogram","loss","weight","that","multiplies","the","computed","loss","before","summing","up","the","total","loss",".","Defaults","to","0","."]
code: def __post_init__(self):
      super().__post_init__()
      self.generator_model_params["cond_channels"] = self.audio.num_mels
  
imports: from dataclasses import dataclass, field
  from typing import Dict
  from TTS.vocoder.configs.shared_configs import BaseGANVocoderConfig
label_desc: Use image node_id 1 for calling the UnivnetConfig obj's underlying member method code with example usage: obj.__post_init__() without return types