Data points remain tuples

test/test_datasets.py

@@ -15,11 +15,11 @@ class TestDatasets(unittest.TestCase):
     path = os.path.join(test_dirpath, "assets")
 
     def test_yesno(self):
-        data = YESNO(self.path, return_dict=True)
+        data = YESNO(self.path)
         data[0]
 
     def test_vctk(self):
-        data = VCTK(self.path, return_dict=True)
+        data = VCTK(self.path)
         data[0]
 
     def test_librispeech(self):

torchaudio/datasets/commonvoice.py

@@ -1,7 +1,8 @@
 import os
 
-import torchaudio
 from torch.utils.data import Dataset
+
+import torchaudio
 from torchaudio.datasets.utils import download_url, extract_archive, unicode_csv_reader
 
 # Default TSV should be one of
@@ -17,20 +18,28 @@
 
 
 def load_commonvoice_item(line, header, path, folder_audio):
+    # Each line as the following data:
+    # client_id, path, sentence, up_votes, down_votes, age, gender, accent
+
+    assert header[1] == "path"
     fileid = line[1]
 
     filename = os.path.join(path, folder_audio, fileid)
 
     waveform, sample_rate = torchaudio.load(filename)
 
     dic = dict(zip(header, line))
-    dic["waveform"] = waveform
-    dic["sample_rate"] = sample_rate
 
-    return dic
+    return waveform, sample_rate, dic
 
 
 class COMMONVOICE(Dataset):
+    """
+    Create a Dataset for CommonVoice. Each item is a tuple of the form:
+    (waveform, sample_rate, dictionary)
+    where dictionary is a dictionary built from the tsv file with the following keys:
+    client_id, path, sentence, up_votes, down_votes, age, gender, accent.
+    """
 
     _ext_txt = ".txt"
     _ext_audio = ".mp3"

torchaudio/datasets/librispeech.py

@@ -1,8 +1,7 @@
 import os
 
-from torch.utils.data import Dataset
-
 import torchaudio
+from torch.utils.data import Dataset
 from torchaudio.datasets.utils import (
     download_url,
     extract_archive,
@@ -16,38 +15,43 @@
 
 def load_librispeech_item(fileid, path, ext_audio, ext_txt):
 
-    speaker, chapter, utterance = fileid.split("-")
+    speaker_id, chapter_id, utterance_id = fileid.split("-")
 
-    file_text = speaker + "-" + chapter + ext_txt
-    file_text = os.path.join(path, speaker, chapter, file_text)
+    file_text = speaker_id + "-" + chapter_id + ext_txt
+    file_text = os.path.join(path, speaker_id, chapter_id, file_text)
 
-    fileid_audio = speaker + "-" + chapter + "-" + utterance
+    fileid_audio = speaker_id + "-" + chapter_id + "-" + utterance_id
     file_audio = fileid_audio + ext_audio
-    file_audio = os.path.join(path, speaker, chapter, file_audio)
+    file_audio = os.path.join(path, speaker_id, chapter_id, file_audio)
 
     # Load audio
     waveform, sample_rate = torchaudio.load(file_audio)
 
     # Load text
-    for line in open(file_text):
-        fileid_text, content = line.strip().split(" ", 1)
-        if fileid_audio == fileid_text:
-            break
-    else:
-        # Translation not found
-        raise FileNotFoundError("Translation not found for " + fileid_audio)
-
-    return {
-        "speaker_id": speaker,
-        "chapter_id": chapter,
-        "utterance_id": utterance,
-        "utterance": content,
-        "waveform": waveform,
-        "sample_rate": sample_rate,
-    }
+    with open(file_text) as ft:
+        for line in ft:
+            fileid_text, utterance = line.strip().split(" ", 1)
+            if fileid_audio == fileid_text:
+                break
+        else:
+            # Translation not found
+            raise FileNotFoundError("Translation not found for " + fileid_audio)
+
+    return (
+        waveform,
+        sample_rate,
+        utterance,
+        int(speaker_id),
+        int(chapter_id),
+        int(utterance_id),
+    )
 
 
 class LIBRISPEECH(Dataset):
+    """
+    Create a Dataset for LibriSpeech. Each item is a tuple of the form:
+    waveform, sample_rate, utterance, speaker_id, chapter_id, utterance_id
+    """
 
     _ext_txt = ".trans.txt"
     _ext_audio = ".flac"

torchaudio/datasets/vctk.py

@@ -12,15 +12,15 @@
 def load_vctk_item(
     fileid, path, ext_audio, ext_txt, folder_audio, folder_txt, downsample=False
 ):
-    speaker, utterance = fileid.split("_")
+    speaker_id, utterance_id = fileid.split("_")
 
     # Read text
-    file_txt = os.path.join(path, folder_txt, speaker, fileid + ext_txt)
+    file_txt = os.path.join(path, folder_txt, speaker_id, fileid + ext_txt)
     with open(file_txt) as file_text:
-        content = file_text.readlines()[0]
+        utterance = file_text.readlines()[0]
 
     # Read wav
-    file_audio = os.path.join(path, folder_audio, speaker, fileid + ext_audio)
+    file_audio = os.path.join(path, folder_audio, speaker_id, fileid + ext_audio)
     if downsample:
         # Legacy
         E = torchaudio.sox_effects.SoxEffectsChain()
@@ -34,16 +34,14 @@ def load_vctk_item(
     else:
         waveform, sample_rate = torchaudio.load(file_audio)
 
-    return {
-        "speaker_id": speaker,
-        "utterance_id": utterance,
-        "utterance": content,
-        "waveform": waveform,
-        "sample_rate": sample_rate,
-    }
+    return waveform, sample_rate, utterance, speaker_id, utterance_id
 
 
 class VCTK(Dataset):
+    """
+    Create a Dataset for VCTK. Each item is a tuple of the form:
+    (waveform, sample_rate, utterance, speaker_id, utterance_id)
+    """
 
     _folder_txt = "txt"
     _folder_audio = "wav48"
@@ -59,17 +57,8 @@ def __init__(
         downsample=False,
         transform=None,
         target_transform=None,
-        return_dict=False,
     ):
 
-        if not return_dict:
-            warnings.warn(
-                "In the next version, the item returned will be a dictionary. "
-                "Please use `return_dict=True` to enable this behavior now, "
-                "and suppress this warning.",
-                DeprecationWarning,
-            )
-
         if downsample:
             warnings.warn(
                 "In the next version, transforms will not be part of the dataset. "
@@ -89,7 +78,6 @@ def __init__(
         self.downsample = downsample
         self.transform = transform
         self.target_transform = target_transform
-        self.return_dict = return_dict
 
         archive = os.path.basename(url)
         archive = os.path.join(root, archive)
@@ -122,23 +110,15 @@ def __getitem__(self, n):
             self._folder_txt,
         )
 
-        # Legacy
-        waveform = item["waveform"]
+        # TODO Upon deprecation, uncomment line below and remove following code
+        # return item
+
+        waveform, sample_rate, utterance, speaker_id, utterance_id = item
         if self.transform is not None:
             waveform = self.transform(waveform)
-        item["waveform"] = waveform
-
-        # Legacy
-        utterance = item["utterance"]
         if self.target_transform is not None:
             utterance = self.target_transform(utterance)
-        item["utterance"] = utterance
-
-        if self.return_dict:
-            return item
-
-        # Legacy
-        return item["waveform"], item["utterance"]
+        return waveform, sample_rate, utterance, speaker_id, utterance_id
 
     def __len__(self):
         return len(self._walker)

torchaudio/datasets/yesno.py

@@ -11,16 +11,20 @@
 
 def load_yesno_item(fileid, path, ext_audio):
     # Read label
-    label = fileid.split("_")
+    labels = [int(c) for c in fileid.split("_")]
 
     # Read wav
     file_audio = os.path.join(path, fileid + ext_audio)
     waveform, sample_rate = torchaudio.load(file_audio)
 
-    return {"label": label, "waveform": waveform, "sample_rate": sample_rate}
+    return waveform, sample_rate, labels
 
 
 class YESNO(Dataset):
+    """
+    Create a Dataset for YesNo. Each item is a tuple of the form:
+    (waveform, sample_rate, labels)
+    """
 
     _ext_audio = ".wav"
 
@@ -32,17 +36,8 @@ def __init__(
         download=False,
         transform=None,
         target_transform=None,
-        return_dict=False,
     ):
 
-        if not return_dict:
-            warnings.warn(
-                "In the next version, the item returned will be a dictionary. "
-                "Please use `return_dict=True` to enable this behavior now, "
-                "and suppress this warning.",
-                DeprecationWarning,
-            )
-
         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. "
@@ -53,7 +48,6 @@ def __init__(
 
         self.transform = transform
         self.target_transform = target_transform
-        self.return_dict = return_dict
 
         archive = os.path.basename(url)
         archive = os.path.join(root, archive)
@@ -79,20 +73,15 @@ def __getitem__(self, n):
         fileid = self._walker[n]
         item = load_yesno_item(fileid, self._path, self._ext_audio)
 
-        waveform = item["waveform"]
+        # TODO Upon deprecation, uncomment line below and remove following code
+        # return item
+
+        waveform, sample_rate, labels = item
         if self.transform is not None:
             waveform = self.transform(waveform)
-        item["waveform"] = waveform
-
-        label = item["label"]
         if self.target_transform is not None:
-            label = self.target_transform(label)
-        item["label"] = label
-
-        if self.return_dict:
-            return item
-
-        return item["waveform"], item["label"]
+            labels = self.target_transform(labels)
+        return waveform, sample_rate, labels
 
     def __len__(self):
         return len(self._walker)