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