Refactor datasets test (#817)

test/datasets/datasets_test.py

+from torchaudio.datasets.commonvoice import COMMONVOICE
+from torchaudio.datasets.librispeech import LIBRISPEECH
+from torchaudio.datasets.speechcommands import SPEECHCOMMANDS
+from torchaudio.datasets.utils import diskcache_iterator, bg_iterator
+from torchaudio.datasets.vctk import VCTK
+from torchaudio.datasets.ljspeech import LJSPEECH
+from torchaudio.datasets.gtzan import GTZAN
+from torchaudio.datasets.cmuarctic import CMUARCTIC
+
+from ..common_utils import (
+    TorchaudioTestCase,
+    get_asset_path,
+)
+
+
+class TestDatasets(TorchaudioTestCase):
+    backend = 'default'
+    path = get_asset_path()
+
+    def test_vctk(self):
+        data = VCTK(self.path)
+        data[0]
+
+    def test_librispeech(self):
+        data = LIBRISPEECH(self.path, "dev-clean")
+        data[0]
+
+    def test_ljspeech(self):
+        data = LJSPEECH(self.path)
+        data[0]
+
+    def test_speechcommands(self):
+        data = SPEECHCOMMANDS(self.path)
+        data[0]
+
+    def test_gtzan(self):
+        data = GTZAN(self.path)
+        data[0]
+
+    def test_cmuarctic(self):
+        data = CMUARCTIC(self.path)
+        data[0]
+
+
+class TestCommonVoice(TorchaudioTestCase):
+    backend = 'default'
+    path = get_asset_path()
+
+    def test_commonvoice(self):
+        data = COMMONVOICE(self.path, url="tatar")
+        data[0]
+
+    def test_commonvoice_diskcache(self):
+        data = COMMONVOICE(self.path, url="tatar")
+        data = diskcache_iterator(data)
+        # Save
+        data[0]
+        # Load
+        data[0]
+
+    def test_commonvoice_bg(self):
+        data = COMMONVOICE(self.path, url="tatar")
+        data = bg_iterator(data, 5)
+        for _ in data:
+            pass

test/test_datasets.py → test/datasets/libritts_test.py

 import os
-import unittest
 
-from torchaudio.datasets.commonvoice import COMMONVOICE
-from torchaudio.datasets.librispeech import LIBRISPEECH
-from torchaudio.datasets.speechcommands import SPEECHCOMMANDS
-from torchaudio.datasets.utils import diskcache_iterator, bg_iterator
-from torchaudio.datasets.vctk import VCTK
-from torchaudio.datasets.yesno import YESNO
-from torchaudio.datasets.ljspeech import LJSPEECH
-from torchaudio.datasets.gtzan import GTZAN
-from torchaudio.datasets.cmuarctic import CMUARCTIC
 from torchaudio.datasets.libritts import LIBRITTS
 
-from .common_utils import (
+from ..common_utils import (
     TempDirMixin,
     TorchaudioTestCase,
-    get_asset_path,
     get_whitenoise,
     save_wav,
     normalize_wav,
 )
 
 
-class TestDatasets(TorchaudioTestCase):
-    backend = 'default'
-    path = get_asset_path()
-
-    def test_vctk(self):
-        data = VCTK(self.path)
-        data[0]
-
-    def test_librispeech(self):
-        data = LIBRISPEECH(self.path, "dev-clean")
-        data[0]
-
-    def test_ljspeech(self):
-        data = LJSPEECH(self.path)
-        data[0]
-
-    def test_speechcommands(self):
-        data = SPEECHCOMMANDS(self.path)
-        data[0]
-
-    def test_gtzan(self):
-        data = GTZAN(self.path)
-        data[0]
-
-    def test_cmuarctic(self):
-        data = CMUARCTIC(self.path)
-        data[0]
-
-
-class TestCommonVoice(TorchaudioTestCase):
-    backend = 'default'
-    path = get_asset_path()
-
-    def test_commonvoice(self):
-        data = COMMONVOICE(self.path, url="tatar")
-        data[0]
-
-    def test_commonvoice_diskcache(self):
-        data = COMMONVOICE(self.path, url="tatar")
-        data = diskcache_iterator(data)
-        # Save
-        data[0]
-        # Load
-        data[0]
-
-    def test_commonvoice_bg(self):
-        data = COMMONVOICE(self.path, url="tatar")
-        data = bg_iterator(data, 5)
-        for _ in data:
-            pass
-
-
-class TestYesNo(TempDirMixin, TorchaudioTestCase):
-    backend = 'default'
-
-    root_dir = None
-    data = []
-    labels = [
-        [0, 0, 0, 0, 0, 0, 0, 0],
-        [0, 0, 0, 0, 1, 1, 1, 1],
-        [0, 1, 0, 1, 0, 1, 1, 0],
-        [1, 1, 1, 1, 0, 0, 0, 0],
-        [1, 1, 1, 1, 1, 1, 1, 1],
-    ]
-
-    @classmethod
-    def setUpClass(cls):
-        cls.root_dir = cls.get_base_temp_dir()
-        base_dir = os.path.join(cls.root_dir, 'waves_yesno')
-        os.makedirs(base_dir, exist_ok=True)
-        for label in cls.labels:
-            filename = f'{"_".join(str(l) for l in label)}.wav'
-            path = os.path.join(base_dir, filename)
-            data = get_whitenoise(sample_rate=8000, duration=6, n_channels=1, dtype='int16')
-            save_wav(path, data, 8000)
-            cls.data.append(normalize_wav(data))
-
-    def test_yesno(self):
-        dataset = YESNO(self.root_dir)
-        samples = list(dataset)
-        samples.sort(key=lambda s: s[2])
-        for i, (waveform, sample_rate, label) in enumerate(samples):
-            expected_label = self.labels[i]
-            expected_data = self.data[i]
-            self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8)
-            assert sample_rate == 8000
-            assert label == expected_label
-
-
 class TestLibriTTS(TempDirMixin, TorchaudioTestCase):
     backend = 'default'
 
@@ -139,15 +39,13 @@ class TestLibriTTS(TempDirMixin, TorchaudioTestCase):
 
             original_text_filename = f'{"_".join(str(u) for u in utterance_id)}.original.txt'
             path_original = os.path.join(file_dir, original_text_filename)
-            f = open(path_original, 'w')
-            f.write(cls.original_text)
-            f.close()
+            with open(path_original, 'w') as file_:
+                file_.write(cls.original_text)
 
             normalized_text_filename = f'{"_".join(str(u) for u in utterance_id)}.normalized.txt'
             path_normalized = os.path.join(file_dir, normalized_text_filename)
-            f = open(path_normalized, 'w')
-            f.write(cls.normalized_text)
-            f.close()
+            with open(path_normalized, 'w') as file_:
+                file_.write(cls.normalized_text)
 
     def test_libritts(self):
         dataset = LIBRITTS(self.root_dir)

test/datasets/yesno_test.py

+import os
+
+from torchaudio.datasets import yesno
+
+from ..common_utils import (
+    TempDirMixin,
+    TorchaudioTestCase,
+    get_whitenoise,
+    save_wav,
+    normalize_wav,
+)
+
+
+class TestYesNo(TempDirMixin, TorchaudioTestCase):
+    backend = 'default'
+
+    root_dir = None
+    data = []
+    labels = [
+        [0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 1, 1, 1, 1],
+        [0, 1, 0, 1, 0, 1, 1, 0],
+        [1, 1, 1, 1, 0, 0, 0, 0],
+        [1, 1, 1, 1, 1, 1, 1, 1],
+    ]
+
+    @classmethod
+    def setUpClass(cls):
+        cls.root_dir = cls.get_base_temp_dir()
+        base_dir = os.path.join(cls.root_dir, 'waves_yesno')
+        os.makedirs(base_dir, exist_ok=True)
+        for label in cls.labels:
+            filename = f'{"_".join(str(l) for l in label)}.wav'
+            path = os.path.join(base_dir, filename)
+            data = get_whitenoise(sample_rate=8000, duration=6, n_channels=1, dtype='int16')
+            save_wav(path, data, 8000)
+            cls.data.append(normalize_wav(data))
+
+    def test_yesno(self):
+        dataset = yesno.YESNO(self.root_dir)
+        samples = list(dataset)
+        samples.sort(key=lambda s: s[2])
+        for i, (waveform, sample_rate, label) in enumerate(samples):
+            expected_label = self.labels[i]
+            expected_data = self.data[i]
+            self.assertEqual(expected_data, waveform, atol=5e-5, rtol=1e-8)
+            assert sample_rate == 8000
+            assert label == expected_label