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# 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.
|