class_docstr: Configure `ForwardTTS` as SpeedySpeech model.
  
  Example:
  
      >>> from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig
      >>> config = SpeedySpeechConfig()
  
   Args:
      model (str):
          Model name used for selecting the right model at initialization. Defaults to `speedy_speech`.
  
      base_model (str):
          Name of the base model being configured as this model so that 🐸 TTS knows it needs to initiate
          the base model rather than searching for the `model` implementation. Defaults to `forward_tts`.
  
      model_args (Coqpit):
          Model class arguments. Check `FastPitchArgs` for more details. Defaults to `FastPitchArgs()`.
  
      data_dep_init_steps (int):
          Number of steps used for computing normalization parameters at the beginning of the training. GlowTTS uses
          Activation Normalization that pre-computes normalization stats at the beginning and use the same values
          for the rest. Defaults to 10.
  
      speakers_file (str):
          Path to the file containing the list of speakers. Needed at inference for loading matching speaker ids to
          speaker names. Defaults to `None`.
  
      use_speaker_embedding (bool):
          enable / disable using speaker embeddings for multi-speaker models. If set True, the model is
          in the multi-speaker mode. Defaults to False.
  
      use_d_vector_file (bool):
          enable /disable using external speaker embeddings in place of the learned embeddings. Defaults to False.
  
      d_vector_file (str):
          Path to the file including pre-computed speaker embeddings. Defaults to None.
  
      d_vector_dim (int):
          Dimension of the external speaker embeddings. Defaults to 0.
  
      optimizer (str):
          Name of the model optimizer. Defaults to `RAdam`.
  
      optimizer_params (dict):
          Arguments of the model optimizer. Defaults to `{"betas": [0.9, 0.998], "weight_decay": 1e-6}`.
  
      lr_scheduler (str):
          Name of the learning rate scheduler. Defaults to `Noam`.
  
      lr_scheduler_params (dict):
          Arguments of the learning rate scheduler. Defaults to `{"warmup_steps": 4000}`.
  
      lr (float):
          Initial learning rate. Defaults to `1e-3`.
  
      grad_clip (float):
          Gradient norm clipping value. Defaults to `5.0`.
  
      spec_loss_type (str):
          Type of the spectrogram loss. Check `ForwardTTSLoss` for possible values. Defaults to `l1`.
  
      duration_loss_type (str):
          Type of the duration loss. Check `ForwardTTSLoss` for possible values. Defaults to `huber`.
  
      use_ssim_loss (bool):
          Enable/disable the use of SSIM (Structural Similarity) loss. Defaults to True.
  
      wd (float):
          Weight decay coefficient. Defaults to `1e-7`.
  
      ssim_loss_alpha (float):
          Weight for the SSIM loss. If set 0, disables the SSIM loss. Defaults to 1.0.
  
      dur_loss_alpha (float):
          Weight for the duration predictor's loss. If set 0, disables the huber loss. Defaults to 1.0.
  
      spec_loss_alpha (float):
          Weight for the L1 spectrogram loss. If set 0, disables the L1 loss. Defaults to 1.0.
  
      binary_loss_alpha (float):
          Weight for the binary loss. If set 0, disables the binary loss. Defaults to 1.0.
  
      binary_loss_warmup_epochs (float):
          Number of epochs to gradually increase the binary loss impact. Defaults to 150.
  
      min_seq_len (int):
          Minimum input sequence length to be used at training.
  
      max_seq_len (int):
          Maximum input sequence length to be used at training. Larger values result in more VRAM usage.
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code: def __post_init__(self):
      # Pass multi-speaker parameters to the model args as `model.init_multispeaker()` looks for it there.
      if self.num_speakers > 0:
          self.model_args.num_speakers = self.num_speakers
  
      # speaker embedding settings
      if self.use_speaker_embedding:
          self.model_args.use_speaker_embedding = True
      if self.speakers_file:
          self.model_args.speakers_file = self.speakers_file
  
      # d-vector settings
      if self.use_d_vector_file:
          self.model_args.use_d_vector_file = True
      if self.d_vector_dim is not None and self.d_vector_dim > 0:
          self.model_args.d_vector_dim = self.d_vector_dim
      if self.d_vector_file:
          self.model_args.d_vector_file = self.d_vector_file
  
imports: from dataclasses import dataclass, field
  from typing import List
  from TTS.tts.configs.shared_configs import BaseTTSConfig
  from TTS.tts.models.forward_tts import ForwardTTSArgs
label_desc: Use image node_id 1 for calling the SpeedySpeechConfig obj's underlying member method code with example usage: obj.__post_init__() without return types