Datasets inline typing (#511)

* add CommonDataset Inline typing

* inline Typing librispeech

* add inline typing ljspeech

* add inline typing speechcommands

* add inline typing to vctk

* add inline typing yesno

* apply type to __getitem__
Co-authored-by: Vincent QB <vincentqb@users.noreply.github.com>

torchaudio/datasets/commonvoice.py

 import os
-
-from torch.utils.data import Dataset
+from typing import List, Dict, Tuple
 
 import torchaudio
 from torchaudio.datasets.utils import download_url, extract_archive, unicode_csv_reader
+from torch import Tensor
+from torch.utils.data import Dataset
 
 # Default TSV should be one of
 # dev.tsv
@@ -19,7 +20,10 @@ VERSION = "cv-corpus-4-2019-12-10"
 TSV = "train.tsv"
 
 
-def load_commonvoice_item(line, header, path, folder_audio):
+def load_commonvoice_item(line: List[str],
+                          header: List[str],
+                          path: str,
+                          folder_audio: str) -> Tuple[Tensor, int, Dict[str, str]]:
     # Each line as the following data:
     # client_id, path, sentence, up_votes, down_votes, age, gender, accent
 
@@ -47,12 +51,13 @@ class COMMONVOICE(Dataset):
     _ext_audio = ".mp3"
     _folder_audio = "clips"
 
-    def __init__(self, root,
-                 tsv=TSV,
-                 url=URL,
-                 folder_in_archive=FOLDER_IN_ARCHIVE,
-                 version=VERSION,
-                 download=False):
+    def __init__(self,
+                 root: str,
+                 tsv: str = TSV,
+                 url: str = URL,
+                 folder_in_archive: str = FOLDER_IN_ARCHIVE,
+                 version: str = VERSION,
+                 download: bool = False) -> None:
 
         languages = {
             "tatar": "tt",
@@ -125,9 +130,9 @@ class COMMONVOICE(Dataset):
             self._header = next(walker)
             self._walker = list(walker)
 
-    def __getitem__(self, n):
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, Dict[str, str]]:
         line = self._walker[n]
         return load_commonvoice_item(line, self._header, self._path, self._folder_audio)
 
-    def __len__(self):
+    def __len__(self) -> int:
         return len(self._walker)

torchaudio/datasets/librispeech.py

 import os
+from typing import Tuple
 
 import torchaudio
+from torch import Tensor
 from torch.utils.data import Dataset
 from torchaudio.datasets.utils import (
     download_url,
     extract_archive,
-    unicode_csv_reader,
     walk_files,
 )
 
@@ -13,8 +14,10 @@ URL = "train-clean-100"
 FOLDER_IN_ARCHIVE = "LibriSpeech"
 
 
-def load_librispeech_item(fileid, path, ext_audio, ext_txt):
-
+def load_librispeech_item(fileid: str,
+                          path: str,
+                          ext_audio: str,
+                          ext_txt: str) -> Tuple[Tensor, int, str, int, int, int]:
     speaker_id, chapter_id, utterance_id = fileid.split("-")
 
     file_text = speaker_id + "-" + chapter_id + ext_txt
@@ -56,9 +59,11 @@ class LIBRISPEECH(Dataset):
     _ext_txt = ".trans.txt"
     _ext_audio = ".flac"
 
-    def __init__(
-        self, root, url=URL, folder_in_archive=FOLDER_IN_ARCHIVE, download=False
-    ):
+    def __init__(self,
+                 root: str,
+                 url: str = URL,
+                 folder_in_archive: str = FOLDER_IN_ARCHIVE,
+                 download: bool = False) -> None:
 
         if url in [
             "dev-clean",
@@ -94,9 +99,9 @@ class LIBRISPEECH(Dataset):
         )
         self._walker = list(walker)
 
-    def __getitem__(self, n):
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, int, int, int]:
         fileid = self._walker[n]
         return load_librispeech_item(fileid, self._path, self._ext_audio, self._ext_txt)
 
-    def __len__(self):
+    def __len__(self) -> int:
         return len(self._walker)

torchaudio/datasets/ljspeech.py

 import os
 import csv
+from typing import List, Tuple
 
 import torchaudio
 from torchaudio.datasets.utils import download_url, extract_archive, unicode_csv_reader
+from torch import Tensor
 from torch.utils.data import Dataset
 
 URL = "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2"
 FOLDER_IN_ARCHIVE = "wavs"
 
 
-def load_ljspeech_item(line, path, ext_audio):
+def load_ljspeech_item(line: List[str], path: str, ext_audio: str) -> Tuple[Tensor, int, str, str]:
     assert len(line) == 3
     fileid, transcript, normalized_transcript = line
     fileid_audio = fileid + ext_audio
@@ -35,9 +37,11 @@ class LJSPEECH(Dataset):
     _ext_audio = ".wav"
     _ext_archive = '.tar.bz2'
 
-    def __init__(
-            self, root, url=URL, folder_in_archive=FOLDER_IN_ARCHIVE, download=False
-    ):
+    def __init__(self,
+                 root: str,
+                 url: str = URL,
+                 folder_in_archive: str = FOLDER_IN_ARCHIVE,
+                 download: bool = False) -> None:
 
         basename = os.path.basename(url)
         archive = os.path.join(root, basename)
@@ -58,9 +62,9 @@ class LJSPEECH(Dataset):
             walker = unicode_csv_reader(metadata, delimiter="|", quoting=csv.QUOTE_NONE)
             self._walker = list(walker)
 
-    def __getitem__(self, n):
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str]:
         line = self._walker[n]
         return load_ljspeech_item(line, self._path, self._ext_audio)
 
-    def __len__(self):
+    def __len__(self) -> int:
         return len(self._walker)

torchaudio/datasets/speechcommands.py

 import os
+from typing import Tuple
 
 import torchaudio
 from torch.utils.data import Dataset
+from torch import Tensor
 from torchaudio.datasets.utils import (
     download_url,
     extract_archive,
@@ -14,7 +16,7 @@ HASH_DIVIDER = "_nohash_"
 EXCEPT_FOLDER = "_background_noise_"
 
 
-def load_speechcommands_item(filepath, path):
+def load_speechcommands_item(filepath: str, path: str) -> Tuple[Tensor, int, str, str, int]:
     relpath = os.path.relpath(filepath, path)
     label, filename = os.path.split(relpath)
     speaker, _ = os.path.splitext(filename)
@@ -33,13 +35,11 @@ class SPEECHCOMMANDS(Dataset):
     waveform, sample_rate, label, speaker_id, utterance_number
     """
 
-    def __init__(
-            self,
-            root,
-            url=URL,
-            folder_in_archive=FOLDER_IN_ARCHIVE,
-            download=False
-    ):
+    def __init__(self,
+                 root: str,
+                 url: str = URL,
+                 folder_in_archive: str = FOLDER_IN_ARCHIVE,
+                 download: bool = False) -> None:
         if url in [
             "speech_commands_v0.01",
             "speech_commands_v0.02",
@@ -67,9 +67,9 @@ class SPEECHCOMMANDS(Dataset):
         walker = filter(lambda w: HASH_DIVIDER in w and EXCEPT_FOLDER not in w, walker)
         self._walker = list(walker)
 
-    def __getitem__(self, n):
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, int]:
         fileid = self._walker[n]
         return load_speechcommands_item(fileid, self._path)
 
-    def __len__(self):
+    def __len__(self) -> int:
         return len(self._walker)

torchaudio/datasets/vctk.py

 import os
 import warnings
+from typing import Any, Tuple
 
 import torchaudio
+from torch import Tensor
 from torch.utils.data import Dataset
 from torchaudio.datasets.utils import download_url, extract_archive, walk_files
 
@@ -9,9 +11,13 @@ URL = "http://homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz"
 FOLDER_IN_ARCHIVE = "VCTK-Corpus"
 
 
-def load_vctk_item(
-    fileid, path, ext_audio, ext_txt, folder_audio, folder_txt, downsample=False
-):
+def load_vctk_item(fileid: str,
+                   path: str,
+                   ext_audio: str,
+                   ext_txt: str,
+                   folder_audio: str,
+                   folder_txt: str,
+                   downsample: bool = False) -> Tuple[Tensor, int, str, str, str]:
     speaker_id, utterance_id = fileid.split("_")
 
     # Read text
@@ -50,16 +56,14 @@ class VCTK(Dataset):
     _ext_audio = ".wav"
     _except_folder = "p315"
 
-    def __init__(
-        self,
-        root,
-        url=URL,
-        folder_in_archive=FOLDER_IN_ARCHIVE,
-        download=False,
-        downsample=False,
-        transform=None,
-        target_transform=None,
-    ):
+    def __init__(self,
+                 root: str,
+                 url: str = URL,
+                 folder_in_archive: str = FOLDER_IN_ARCHIVE,
+                 download: bool = False,
+                 downsample: bool = False,
+                 transform: Any = None,
+                 target_transform: Any = None) -> None:
 
         if downsample:
             warnings.warn(
@@ -100,7 +104,7 @@ class VCTK(Dataset):
         walker = filter(lambda w: self._except_folder not in w, walker)
         self._walker = list(walker)
 
-    def __getitem__(self, n):
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, str, str, str]:
         fileid = self._walker[n]
         item = load_vctk_item(
             fileid,
@@ -121,5 +125,5 @@ class VCTK(Dataset):
             utterance = self.target_transform(utterance)
         return waveform, sample_rate, utterance, speaker_id, utterance_id
 
-    def __len__(self):
+    def __len__(self) -> int:
         return len(self._walker)

torchaudio/datasets/yesno.py

 import os
 import warnings
+from typing import Any, List, Tuple
 
 import torchaudio
+from torch import Tensor
 from torch.utils.data import Dataset
 from torchaudio.datasets.utils import download_url, extract_archive, walk_files
 
@@ -9,7 +11,7 @@ URL = "http://www.openslr.org/resources/1/waves_yesno.tar.gz"
 FOLDER_IN_ARCHIVE = "waves_yesno"
 
 
-def load_yesno_item(fileid, path, ext_audio):
+def load_yesno_item(fileid: str, path: str, ext_audio: str) -> Tuple[Tensor, int, List[int]]:
     # Read label
     labels = [int(c) for c in fileid.split("_")]
 
@@ -28,15 +30,13 @@ class YESNO(Dataset):
 
     _ext_audio = ".wav"
 
-    def __init__(
-        self,
-        root,
-        url=URL,
-        folder_in_archive=FOLDER_IN_ARCHIVE,
-        download=False,
-        transform=None,
-        target_transform=None,
-    ):
+    def __init__(self,
+                 root: str,
+                 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(
@@ -68,7 +68,7 @@ class YESNO(Dataset):
         )
         self._walker = list(walker)
 
-    def __getitem__(self, n):
+    def __getitem__(self, n: int) -> Tuple[Tensor, int, List[int]]:
         fileid = self._walker[n]
         item = load_yesno_item(fileid, self._path, self._ext_audio)
 
@@ -82,5 +82,5 @@ class YESNO(Dataset):
             labels = self.target_transform(labels)
         return waveform, sample_rate, labels
 
-    def __len__(self):
+    def __len__(self) -> int:
         return len(self._walker)