Update QUESST14 getitem (#2435)

Summary:
update QUESST14 getitem to include docstrings and additionally return sample rate

Pull Request resolved: https://github.com/pytorch/audio/pull/2435

Reviewed By: nateanl

Differential Revision: D36864254

Pulled By: carolineechen

fbshipit-source-id: 9e68bbc5de27ad2f32f6b298414103c4f6784801

test/torchaudio_unittest/datasets/quesst14_test.py

@@ -46,7 +46,7 @@ def _save_sample(dataset_dir, folder, language, index, sample_rate, seed):
     )
     save_wav(file_path, data, sample_rate)
 
-    sample = (data, Path(file_path).with_suffix("").name)
+    sample = (data, sample_rate, Path(file_path).with_suffix("").name)
 
     # add audio files and language data to language key files
     scoring_path = os.path.join(dataset_dir, "scoring")
@@ -133,9 +133,10 @@ class TestQuesst14(TempDirMixin, TorchaudioTestCase):
 
     def _testQuesst14(self, dataset, data_samples):
         num_samples = 0
-        for i, (data, name) in enumerate(dataset):
+        for i, (data, sample_rate, name) in enumerate(dataset):
             self.assertEqual(data, data_samples[i][0])
-            assert name == data_samples[i][1]
+            assert sample_rate == data_samples[i][1]
+            assert name == data_samples[i][2]
             num_samples += 1
 
         assert num_samples == len(data_samples)

torchaudio/datasets/quesst14.py

@@ -71,10 +71,18 @@ class QUESST14(Dataset):
 
     def _load_sample(self, n: int) -> Tuple[torch.Tensor, str]:
         audio_path = self.data[n]
-        wav, _ = torchaudio.load(audio_path)
-        return wav, audio_path.with_suffix("").name
+        wav, sample_rate = torchaudio.load(audio_path)
+        return wav, sample_rate, audio_path.with_suffix("").name
 
     def __getitem__(self, n: int) -> Tuple[torch.Tensor, str]:
+        """Load the n-th sample from the dataset.
+
+        Args:
+            n (int): The index of the sample to be loaded
+
+        Returns:
+            (Tensor, int, str): ``(waveform, sample_rate, file_name)``
+        """
         return self._load_sample(n)
 
     def __len__(self) -> int: