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Running
on
Zero
| # 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 {ref}`BaseTTS <Base tts Model>` 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. | |
| ```python | |
| 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 | |
| ``` | |