| from coqpit import Coqpit |
|
|
| from TTS.model import BaseTrainerModel |
|
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| |
|
|
|
|
| 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") |
|
|
