Source code for TTS.model

from abc import abstractmethod
from typing import Dict

import torch
from coqpit import Coqpit
from trainer import TrainerModel

# pylint: skip-file


[docs] class BaseTrainerModel(TrainerModel): """BaseTrainerModel model expanding TrainerModel with required functions by 🐸TTS. Every new 🐸TTS model must inherit it. """
[docs] @staticmethod @abstractmethod def init_from_config(config: Coqpit): """Init the model and all its attributes from the given config. Override this depending on your model. """ ...
[docs] @abstractmethod def inference(self, input: torch.Tensor, aux_input={}) -> Dict: """Forward pass for inference. It must return a dictionary with the main model output and all the auxiliary outputs. The key ```model_outputs``` is considered to be the main output and you can add any other auxiliary outputs as you want. We don't use `*kwargs` since it is problematic with the TorchScript API. Args: input (torch.Tensor): [description] aux_input (Dict): Auxiliary inputs like speaker embeddings, durations etc. Returns: Dict: [description] """ outputs_dict = {"model_outputs": None} ... return outputs_dict
[docs] @abstractmethod def load_checkpoint( self, config: Coqpit, checkpoint_path: str, eval: bool = False, strict: bool = True, cache=False ) -> None: """Load a model checkpoint gile and get ready for training or inference. Args: config (Coqpit): Model configuration. checkpoint_path (str): Path to the model checkpoint file. eval (bool, optional): If true, init model for inference else for training. Defaults to False. strict (bool, optional): Match all checkpoint keys to model's keys. Defaults to True. cache (bool, optional): If True, cache the file locally for subsequent calls. It is cached under `get_user_data_dir()/tts_cache`. Defaults to False. """ ...