| # Configuration | |
| We use 👩✈️[Coqpit] for configuration management. It provides basic static type checking and serialization capabilities on top of native Python `dataclasses`. Here is how a simple configuration looks like with Coqpit. | |
| ```python | |
| from dataclasses import asdict, dataclass, field | |
| from typing import List, Union | |
| from coqpit.coqpit import MISSING, Coqpit, check_argument | |
| @dataclass | |
| class SimpleConfig(Coqpit): | |
| val_a: int = 10 | |
| val_b: int = None | |
| val_d: float = 10.21 | |
| val_c: str = "Coqpit is great!" | |
| vol_e: bool = True | |
| # mandatory field | |
| # raise an error when accessing the value if it is not changed. It is a way to define | |
| val_k: int = MISSING | |
| # optional field | |
| val_dict: dict = field(default_factory=lambda: {"val_aa": 10, "val_ss": "This is in a dict."}) | |
| # list of list | |
| val_listoflist: List[List] = field(default_factory=lambda: [[1, 2], [3, 4]]) | |
| val_listofunion: List[List[Union[str, int, bool]]] = field( | |
| default_factory=lambda: [[1, 3], [1, "Hi!"], [True, False]] | |
| ) | |
| def check_values( | |
| self, | |
| ): # you can define explicit constraints manually or by`check_argument()` | |
| """Check config fields""" | |
| c = asdict(self) # avoid unexpected changes on `self` | |
| check_argument("val_a", c, restricted=True, min_val=10, max_val=2056) | |
| check_argument("val_b", c, restricted=True, min_val=128, max_val=4058, allow_none=True) | |
| check_argument("val_c", c, restricted=True) | |
| ``` | |
| In TTS, each model must have a configuration class that exposes all the values necessary for its lifetime. | |
| It defines model architecture, hyper-parameters, training, and inference settings. For our models, we merge all the fields in a single configuration class for ease. It may not look like a wise practice but enables easier bookkeeping and reproducible experiments. | |
| The general configuration hierarchy looks like below: | |
| ``` | |
| ModelConfig() | |
| | | |
| | -> ... # model specific configurations | |
| | -> ModelArgs() # model class arguments | |
| | -> BaseDatasetConfig() # only for tts models | |
| | -> BaseXModelConfig() # Generic fields for `tts` and `vocoder` models. | |
| | | |
| | -> BaseTrainingConfig() # trainer fields | |
| | -> BaseAudioConfig() # audio processing fields | |
| ``` | |
| In the example above, ```ModelConfig()``` is the final configuration that the model receives and it has all the fields necessary for the model. | |
| We host pre-defined model configurations under ```TTS/<model_class>/configs/```. Although we recommend a unified config class, you can decompose it as you like as for your custom models as long as all the fields for the trainer, model, and inference APIs are provided. | |