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.
