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from coqpit import Coqpit
from TTS.model import BaseTrainerModel
# pylint: skip-file
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.
"""
# don't use isintance not to import recursively
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
# This is for backward compatibility
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
# This is for backward compatibility
if "model_params" in config:
self.args = self.config.model_params
else:
raise ValueError("config must be either a *Config or *Args")
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