| from typing import Callable, Dict, List, Sequence, Union | |
| import torch | |
| from lhotse import CutSet, validate | |
| from lhotse.dataset import PrecomputedFeatures | |
| from lhotse.dataset.collation import collate_audio | |
| from lhotse.dataset.input_strategies import BatchIO | |
| from lhotse.utils import ifnone | |
| class SpeechSynthesisDataset(torch.utils.data.Dataset): | |
| """ | |
| The PyTorch Dataset for the speech synthesis task. | |
| Each item in this dataset is a dict of: | |
| .. code-block:: | |
| { | |
| 'audio': (B x NumSamples) float tensor | |
| 'features': (B x NumFrames x NumFeatures) float tensor | |
| 'audio_lens': (B, ) int tensor | |
| 'features_lens': (B, ) int tensor | |
| 'text': List[str] of len B # when return_text=True | |
| 'tokens': List[List[str]] # when return_tokens=True | |
| 'speakers': List[str] of len B # when return_spk_ids=True | |
| 'cut': List of Cuts # when return_cuts=True | |
| } | |
| """ | |
| def __init__( | |
| self, | |
| cut_transforms: List[Callable[[CutSet], CutSet]] = None, | |
| feature_input_strategy: BatchIO = PrecomputedFeatures(), | |
| feature_transforms: Union[Sequence[Callable], Callable] = None, | |
| return_text: bool = True, | |
| return_tokens: bool = False, | |
| return_spk_ids: bool = False, | |
| return_cuts: bool = False, | |
| return_audio: bool = False, | |
| ) -> None: | |
| super().__init__() | |
| self.cut_transforms = ifnone(cut_transforms, []) | |
| self.feature_input_strategy = feature_input_strategy | |
| self.return_text = return_text | |
| self.return_tokens = return_tokens | |
| self.return_spk_ids = return_spk_ids | |
| self.return_cuts = return_cuts | |
| self.return_audio = return_audio | |
| if feature_transforms is None: | |
| feature_transforms = [] | |
| elif not isinstance(feature_transforms, Sequence): | |
| feature_transforms = [feature_transforms] | |
| assert all( | |
| isinstance(transform, Callable) for transform in feature_transforms | |
| ), "Feature transforms must be Callable" | |
| self.feature_transforms = feature_transforms | |
| def __getitem__(self, cuts: CutSet) -> Dict[str, torch.Tensor]: | |
| validate_for_tts(cuts) | |
| for transform in self.cut_transforms: | |
| cuts = transform(cuts) | |
| features, features_lens = self.feature_input_strategy(cuts) | |
| for transform in self.feature_transforms: | |
| features = transform(features) | |
| batch = { | |
| "features": features, | |
| "features_lens": features_lens, | |
| } | |
| if self.return_audio: | |
| audio, audio_lens = collate_audio(cuts) | |
| batch["audio"] = audio | |
| batch["audio_lens"] = audio_lens | |
| if self.return_text: | |
| text = [cut.supervisions[0].text for cut in cuts] | |
| batch["text"] = text | |
| if self.return_tokens: | |
| tokens = [cut.supervisions[0].tokens for cut in cuts] | |
| batch["tokens"] = tokens | |
| if self.return_spk_ids: | |
| batch["speakers"] = [cut.supervisions[0].speaker for cut in cuts] | |
| if self.return_cuts: | |
| batch["cut"] = [cut for cut in cuts] | |
| return batch | |
| def validate_for_tts(cuts: CutSet) -> None: | |
| validate(cuts) | |
| for cut in cuts: | |
| assert ( | |
| len(cut.supervisions) == 1 | |
| ), "Only the Cuts with single supervision are supported." | |