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from abc import abstractmethod
from typing import Dict
import torch
from coqpit import Coqpit
from trainer import TrainerModel
# pylint: skip-file
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.
"""
...
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