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 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 also include the 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 the 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. It should be enough to pass an MyModelArgs instance to initiate the MyModel.

  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. ❤️