Remove deprecated transform from Dataset (#1120)

torchaudio/datasets/vctk.py

 import os
 import warnings
-from typing import Any, Tuple, Union
 from pathlib import Path
+from typing import Tuple, Union
 
-import torchaudio
 from torch import Tensor
 from torch.utils.data import Dataset
+
+import torchaudio
 from torchaudio.datasets.utils import (
     download_url,
     extract_archive,
@@ -66,8 +67,6 @@ class VCTK(Dataset):
             Giving ``download=True`` will result in error as the dataset is no longer
             publicly available.
         downsample (bool, optional): Not used.
-        transform (callable, optional): Optional transform applied on waveform. (default: ``None``)
-        target_transform (callable, optional): Optional transform applied on utterance. (default: ``None``)
     """
 
     _folder_txt = "txt"
@@ -81,9 +80,7 @@ class VCTK(Dataset):
                  url: str = URL,
                  folder_in_archive: str = FOLDER_IN_ARCHIVE,
                  download: bool = False,
-                 downsample: bool = False,
-                 transform: Any = None,
-                 target_transform: Any = None) -> None:
+                 downsample: bool = False) -> None:
 
         if downsample:
             warnings.warn(
@@ -92,17 +89,7 @@ class VCTK(Dataset):
                 "and suppress this warning."
             )
 
-        if transform is not None or target_transform is not None:
-            warnings.warn(
-                "In the next version, transforms will not be part of the dataset. "
-                "Please remove the option `transform=True` and "
-                "`target_transform=True` to suppress this warning."
-            )
-
         self.downsample = downsample
-        self.transform = transform
-        self.target_transform = target_transform
-
         # Get string representation of 'root' in case Path object is passed
         root = os.fspath(root)
 
@@ -149,10 +136,6 @@ class VCTK(Dataset):
         # return item
 
         waveform, sample_rate, utterance, speaker_id, utterance_id = item
-        if self.transform is not None:
-            waveform = self.transform(waveform)
-        if self.target_transform is not None:
-            utterance = self.target_transform(utterance)
         return waveform, sample_rate, utterance, speaker_id, utterance_id
 
     def __len__(self) -> int:

torchaudio/datasets/yesno.py

 import os
-import warnings
-from typing import Any, List, Tuple, Union
 from pathlib import Path
+from typing import List, Tuple, Union
 
-import torchaudio
 from torch import Tensor
 from torch.utils.data import Dataset
+
+import torchaudio
 from torchaudio.datasets.utils import (
     download_url,
     extract_archive,
@@ -41,8 +41,6 @@ class YESNO(Dataset):
             The top-level directory of the dataset. (default: ``"waves_yesno"``)
         download (bool, optional):
             Whether to download the dataset if it is not found at root path. (default: ``False``).
-        transform (callable, optional): Optional transform applied on waveform. (default: ``None``)
-        target_transform (callable, optional): Optional transform applied on utterance. (default: ``None``)
     """
 
     _ext_audio = ".wav"
@@ -51,19 +49,7 @@ class YESNO(Dataset):
                  root: Union[str, Path],
                  url: str = URL,
                  folder_in_archive: str = FOLDER_IN_ARCHIVE,
-                 download: bool = False,
-                 transform: Any = None,
-                 target_transform: Any = None) -> None:
-
-        if transform is not None or target_transform is not None:
-            warnings.warn(
-                "In the next version, transforms will not be part of the dataset. "
-                "Please remove the option `transform=True` and "
-                "`target_transform=True` to suppress this warning."
-            )
-
-        self.transform = transform
-        self.target_transform = target_transform
+                 download: bool = False) -> None:
 
         # Get string representation of 'root' in case Path object is passed
         root = os.fspath(root)
@@ -102,10 +88,6 @@ class YESNO(Dataset):
         # return item
 
         waveform, sample_rate, labels = item
-        if self.transform is not None:
-            waveform = self.transform(waveform)
-        if self.target_transform is not None:
-            labels = self.target_transform(labels)
         return waveform, sample_rate, labels
 
     def __len__(self) -> int: