mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-09 10:02:22 +00:00
2ba665abca
* add cosy token * update inference code * add extract cosy token * update results * add requirements.txt * update readme --------- Co-authored-by: yuekaiz <yuekaiz@h20-7.cm.cluster> Co-authored-by: yuekaiz <yuekaiz@mgmt1-login.cm.cluster>
108 lines
3.7 KiB
Python
108 lines
3.7 KiB
Python
from typing import Callable, Dict, List, Sequence, Union
|
|
|
|
import torch
|
|
from lhotse import validate
|
|
from lhotse.cut import CutSet
|
|
from lhotse.dataset.collation import collate_audio
|
|
from lhotse.dataset.input_strategies import BatchIO, PrecomputedFeatures
|
|
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,
|
|
) -> 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
|
|
|
|
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)
|
|
|
|
# audio, audio_lens = collate_audio(cuts)
|
|
features, features_lens = self.feature_input_strategy(cuts)
|
|
|
|
for transform in self.feature_transforms:
|
|
features = transform(features)
|
|
|
|
batch = {
|
|
# "audio": audio,
|
|
"features": features,
|
|
# "audio_lens": audio_lens,
|
|
"features_lens": features_lens,
|
|
}
|
|
|
|
if self.return_text:
|
|
# use normalized text
|
|
# text = [cut.supervisions[0].normalized_text for cut in cuts]
|
|
text = [cut.supervisions[0].text for cut in cuts]
|
|
batch["text"] = text
|
|
|
|
if self.return_tokens and "speech_tokens" in cuts[0].supervisions[0].custom:
|
|
# tokens = [cut.tokens for cut in cuts]
|
|
# tokens = [cut.supervisions[0].custom["tokens"]["text"] for cut in cuts]
|
|
tokens = [cut.supervisions[0].custom["speech_tokens"] for cut in cuts]
|
|
# change str into list
|
|
tokens = [list(map(int, token.split())) for token in tokens]
|
|
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."
|