Implementing a Model#

  1. Implement layers.

    You can either implement the layers under TTS/tts/layers/new_model.py or in the model file TTS/tts/model/new_model.py. You can also reuse layers already implemented.

  2. Test layers.

    We keep tests under tests folder. You can add tts layers tests under tts_tests folder. Basic tests are checking input-output tensor shapes and output values for a given input. Consider testing extreme cases that are more likely to cause problems like zero tensors.

  3. Implement a loss function.

    We keep loss functions under TTS/tts/layers/losses.py. You can also mix-and-match implemented loss functions as you like.

    A loss function returns a dictionary in a format {’loss’: loss, ‘loss1’:loss1 ...} and the dictionary must at least define the loss key which is the actual value used by the optimizer. All the items in the dictionary are automatically logged on the terminal and the Tensorboard.

  4. Test the loss function.

    As we do for the layers, you need to test the loss functions too. You need to check input/output tensor shapes, expected output values for a given input tensor. For instance, certain loss functions have upper and lower limits and it is a wise practice to test with the inputs that should produce these limits.

  5. Implement MyModel.

    In 🐸TTS, a model class is a self-sufficient implementation of a model directing all the interactions with the other components. It is enough to implement the API provided by the BaseModel class to comply.

    A model interacts with the Trainer API for training, Synthesizer API for inference and testing.

    A 🐸TTS model must return a dictionary by the forward() and inference() functions. This dictionary must model_outputs key that is considered as the main model output by the Trainer and Synthesizer.

    You can place your tts model implementation under TTS/tts/models/new_model.py then inherit and implement the BaseTTS.

    There is also the callback interface by which you can manipulate both the model and the Trainer states. Callbacks give you an infinite flexibility to add custom behaviours for your model and training routines.

    For more details, see BaseTTS and :obj:TTS.utils.callbacks.

  6. Optionally, define MyModelArgs.

    MyModelArgs is a 👨‍✈️Coqpit class that sets all the class arguments of the MyModel. MyModelArgs must have all the fields neccessary to instantiate the MyModel. However, for training, you need to pass MyModelConfig to the model.

  7. Test MyModel.

    As the layers and the loss functions, it is recommended to test your model. One smart way for testing is that you create two models with the exact same weights. Then we run a training loop with one of these models and compare the weights with the other model. All the weights need to be different in a passing test. Otherwise, it is likely that a part of the model is malfunctioning or not even attached to the model’s computational graph.

  8. Define MyModelConfig.

    Place MyModelConfig file under TTS/models/configs. It is enough to inherit the BaseTTSConfig to make your config compatible with the Trainer. You should also include MyModelArgs as a field if defined. The rest of the fields should define the model specific values and parameters.

  9. Write Docstrings.

    We love you more when you document your code. ❤️

Template 🐸TTS Model implementation#

You can start implementing your model by copying the following base class.

from TTS.tts.models.base_tts import BaseTTS


class MyModel(BaseTTS):
    """
    Notes on input/output tensor shapes:
        Any input or output tensor of the model must be shaped as

        - 3D tensors `batch x time x channels`
        - 2D tensors `batch x channels`
        - 1D tensors `batch x 1`
    """

    def __init__(self, config: Coqpit):
        super().__init__()
        self._set_model_args(config)

    def _set_model_args(self, config: Coqpit):
        """Set model arguments from the config. Override this."""
        pass

    def forward(self, input: torch.Tensor, *args, aux_input={}, **kwargs) -> Dict:
        """Forward pass for the model mainly used in training.

        You can be flexible here and use different number of arguments and argument names since it is intended to be
        used by `train_step()` without exposing it out of the model.

        Args:
            input (torch.Tensor): Input tensor.
            aux_input (Dict): Auxiliary model inputs like embeddings, durations or any other sorts of inputs.

        Returns:
            Dict: Model outputs. Main model output must be named as "model_outputs".
        """
        outputs_dict = {"model_outputs": None}
        ...
        return outputs_dict

    def inference(self, input: torch.Tensor, aux_input={}) -> Dict:
        """Forward pass for inference.

        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

    def train_step(self, batch: Dict, criterion: nn.Module) -> Tuple[Dict, Dict]:
        """Perform a single training step. Run the model forward pass and compute losses.

        Args:
            batch (Dict): Input tensors.
            criterion (nn.Module): Loss layer designed for the model.

        Returns:
            Tuple[Dict, Dict]: Model ouputs and computed losses.
        """
        outputs_dict = {}
        loss_dict = {}  # this returns from the criterion
        ...
        return outputs_dict, loss_dict

    def train_log(self, batch: Dict, outputs: Dict, logger: "Logger", assets:Dict, steps:int) -> None:
        """Create visualizations and waveform examples for training.

        For example, here you can plot spectrograms and generate sample sample waveforms from these spectrograms to
        be projected onto Tensorboard.

        Args:
            ap (AudioProcessor): audio processor used at training.
            batch (Dict): Model inputs used at the previous training step.
            outputs (Dict): Model outputs generated at the previoud training step.

        Returns:
            Tuple[Dict, np.ndarray]: training plots and output waveform.
        """
        pass

    def eval_step(self, batch: Dict, criterion: nn.Module) -> Tuple[Dict, Dict]:
        """Perform a single evaluation step. Run the model forward pass and compute losses. In most cases, you can
        call `train_step()` with no changes.

        Args:
            batch (Dict): Input tensors.
            criterion (nn.Module): Loss layer designed for the model.

        Returns:
            Tuple[Dict, Dict]: Model ouputs and computed losses.
        """
        outputs_dict = {}
        loss_dict = {}  # this returns from the criterion
        ...
        return outputs_dict, loss_dict

    def eval_log(self, batch: Dict, outputs: Dict, logger: "Logger", assets:Dict, steps:int) -> None:
        """The same as `train_log()`"""
        pass

    def load_checkpoint(self, config: Coqpit, checkpoint_path: str, eval: bool = False) -> None:
        """Load a checkpoint 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.
        """
        ...

    def get_optimizer(self) -> Union["Optimizer", List["Optimizer"]]:
        """Setup an return optimizer or optimizers."""
        pass

    def get_lr(self) -> Union[float, List[float]]:
        """Return learning rate(s).

        Returns:
            Union[float, List[float]]: Model's initial learning rates.
        """
        pass

    def get_scheduler(self, optimizer: torch.optim.Optimizer):
        pass

    def get_criterion(self):
        pass

    def format_batch(self):
        pass