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from coqpit import Coqpit |
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from TTS.model import BaseTrainerModel |
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class BaseVocoder(BaseTrainerModel): |
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"""Base `vocoder` class. Every new `vocoder` model must inherit this. |
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It defines `vocoder` specific functions on top of `Model`. |
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Notes on input/output tensor shapes: |
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Any input or output tensor of the model must be shaped as |
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- 3D tensors `batch x time x channels` |
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- 2D tensors `batch x channels` |
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- 1D tensors `batch x 1` |
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""" |
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MODEL_TYPE = "vocoder" |
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def __init__(self, config): |
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super().__init__() |
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self._set_model_args(config) |
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def _set_model_args(self, config: Coqpit): |
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"""Setup model args based on the config type. |
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If the config is for training with a name like "*Config", then the model args are embeded in the |
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config.model_args |
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If the config is for the model with a name like "*Args", then we assign the directly. |
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""" |
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if "Config" in config.__class__.__name__: |
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if "characters" in config: |
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_, self.config, num_chars = self.get_characters(config) |
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self.config.num_chars = num_chars |
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if hasattr(self.config, "model_args"): |
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config.model_args.num_chars = num_chars |
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if "model_args" in config: |
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self.args = self.config.model_args |
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if "model_params" in config: |
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self.args = self.config.model_params |
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else: |
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self.config = config |
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if "model_args" in config: |
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self.args = self.config.model_args |
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if "model_params" in config: |
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self.args = self.config.model_params |
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else: |
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raise ValueError("config must be either a *Config or *Args") |
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