| from abc import abstractmethod |
| from typing import Dict |
|
|
| import torch |
| from coqpit import Coqpit |
| from trainer import TrainerModel |
|
|
| |
|
|
|
|
| class BaseTrainerModel(TrainerModel): |
| """BaseTrainerModel model expanding TrainerModel with required functions by 🐸TTS. |
| |
| Every new 🐸TTS model must inherit it. |
| """ |
|
|
| @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. |
| """ |
| ... |
|
|
| @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 |
|
|
| @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. |
| """ |
| ... |
|
|