Spaces:
Sleeping
Sleeping
| from lightning.pytorch.core import LightningModule | |
| from torch import Tensor | |
| from models.config import AcousticENModelConfig | |
| from models.config import PreprocessingConfigUnivNet as PreprocessingConfig | |
| from models.tts.delightful_tts.delightful_tts import DelightfulTTS | |
| from models.vocoder.univnet import UnivNet | |
| class DelightfulUnivnet(LightningModule): | |
| def __init__( | |
| self, | |
| delightful_checkpoint_path: str, | |
| lang: str = "en", | |
| sampling_rate: int = 22050, | |
| ): | |
| super().__init__() | |
| self.sampling_rate = sampling_rate | |
| self.preprocess_config = PreprocessingConfig( | |
| "english_only", | |
| sampling_rate=sampling_rate, | |
| ) | |
| self.delightful_tts = DelightfulTTS.load_from_checkpoint( | |
| delightful_checkpoint_path, | |
| strict=False, | |
| # kwargs to be used in the model | |
| preprocess_config=self.preprocess_config, | |
| model_config=AcousticENModelConfig(), | |
| lang=lang, | |
| sampling_rate=sampling_rate, | |
| ) | |
| self.delightful_tts.freeze() | |
| # Don't need to use separated checkpoint, prev checkpoint used | |
| self.univnet = UnivNet() | |
| self.univnet.freeze() | |
| def forward( | |
| self, | |
| text: str, | |
| speaker_idx: Tensor, | |
| ) -> Tensor: | |
| r"""Performs a forward pass through the AcousticModel. | |
| This code must be run only with the loaded weights from the checkpoint! | |
| Args: | |
| text (str): The input text. | |
| speaker_idx (Tensor): The index of the speaker. | |
| Returns: | |
| Tensor: The generated waveform with hifi-gan. | |
| """ | |
| mel_pred = self.delightful_tts.forward(text, speaker_idx) | |
| wav = self.univnet.forward(mel_pred) | |
| return wav | |