| | from coqpit import Coqpit |
| |
|
| | from TTS.model import BaseTrainerModel |
| |
|
| | |
| |
|
| |
|
| | class BaseVocoder(BaseTrainerModel): |
| | """Base `vocoder` class. Every new `vocoder` model must inherit this. |
| | |
| | It defines `vocoder` specific functions on top of `Model`. |
| | |
| | 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` |
| | """ |
| |
|
| | MODEL_TYPE = "vocoder" |
| |
|
| | def __init__(self, config): |
| | super().__init__() |
| | self._set_model_args(config) |
| |
|
| | def _set_model_args(self, config: Coqpit): |
| | """Setup model args based on the config type. |
| | |
| | If the config is for training with a name like "*Config", then the model args are embeded in the |
| | config.model_args |
| | |
| | If the config is for the model with a name like "*Args", then we assign the directly. |
| | """ |
| | |
| | if "Config" in config.__class__.__name__: |
| | if "characters" in config: |
| | _, self.config, num_chars = self.get_characters(config) |
| | self.config.num_chars = num_chars |
| | if hasattr(self.config, "model_args"): |
| | config.model_args.num_chars = num_chars |
| | if "model_args" in config: |
| | self.args = self.config.model_args |
| | |
| | if "model_params" in config: |
| | self.args = self.config.model_params |
| | else: |
| | self.config = config |
| | if "model_args" in config: |
| | self.args = self.config.model_args |
| | |
| | if "model_params" in config: |
| | self.args = self.config.model_params |
| | else: |
| | raise ValueError("config must be either a *Config or *Args") |
| |
|
