Make GTZAN dataset sorted and use on-the-fly data in GTZAN test (#819)

test/datasets/datasets_test.py

@@ -4,7 +4,6 @@ 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 (
@@ -33,10 +32,6 @@ class TestDatasets(TorchaudioTestCase):
         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]

test/datasets/gtzan_test.py

+import os
+
+from torchaudio.datasets import gtzan
+
+from ..common_utils import (
+    TempDirMixin,
+    TorchaudioTestCase,
+    get_whitenoise,
+    save_wav,
+    normalize_wav,
+)
+
+
+class TestGTZAN(TempDirMixin, TorchaudioTestCase):
+    backend = 'default'
+
+    root_dir = None
+    samples = []
+    training = []
+    validation = []
+    testing = []
+
+    @classmethod
+    def setUpClass(cls):
+        cls.root_dir = cls.get_base_temp_dir()
+        sample_rate = 22050
+        seed = 0
+        for genre in gtzan.gtzan_genres:
+            base_dir = os.path.join(cls.root_dir, 'genres', genre)
+            os.makedirs(base_dir, exist_ok=True)
+            for i in range(100):
+                filename = f'{genre}.{i:05d}'
+                path = os.path.join(base_dir, f'{filename}.wav')
+                data = get_whitenoise(sample_rate=sample_rate, duration=0.01, n_channels=1, dtype='int16', seed=seed)
+                save_wav(path, data, sample_rate)
+                sample = (normalize_wav(data), sample_rate, genre)
+                cls.samples.append(sample)
+                if filename in gtzan.filtered_test:
+                    cls.testing.append(sample)
+                if filename in gtzan.filtered_train:
+                    cls.training.append(sample)
+                if filename in gtzan.filtered_valid:
+                    cls.validation.append(sample)
+                seed += 1
+
+    def test_no_subset(self):
+        dataset = gtzan.GTZAN(self.root_dir)
+
+        n_ite = 0
+        for i, (waveform, sample_rate, label) in enumerate(dataset):
+            self.assertEqual(waveform, self.samples[i][0], atol=5e-5, rtol=1e-8)
+            assert sample_rate == self.samples[i][1]
+            assert label == self.samples[i][2]
+            n_ite += 1
+        assert n_ite == len(self.samples)
+
+    def test_training(self):
+        dataset = gtzan.GTZAN(self.root_dir, subset='training')
+
+        n_ite = 0
+        for i, (waveform, sample_rate, label) in enumerate(dataset):
+            self.assertEqual(waveform, self.training[i][0], atol=5e-5, rtol=1e-8)
+            assert sample_rate == self.training[i][1]
+            assert label == self.training[i][2]
+            n_ite += 1
+        assert n_ite == len(self.training)
+
+    def test_validation(self):
+        dataset = gtzan.GTZAN(self.root_dir, subset='validation')
+
+        n_ite = 0
+        for i, (waveform, sample_rate, label) in enumerate(dataset):
+            self.assertEqual(waveform, self.validation[i][0], atol=5e-5, rtol=1e-8)
+            assert sample_rate == self.validation[i][1]
+            assert label == self.validation[i][2]
+            n_ite += 1
+        assert n_ite == len(self.validation)
+
+    def test_testing(self):
+        dataset = gtzan.GTZAN(self.root_dir, subset='testing')
+
+        n_ite = 0
+        for i, (waveform, sample_rate, label) in enumerate(dataset):
+            self.assertEqual(waveform, self.testing[i][0], atol=5e-5, rtol=1e-8)
+            assert sample_rate == self.testing[i][1]
+            assert label == self.testing[i][2]
+            n_ite += 1
+        assert n_ite == len(self.testing)

torchaudio/datasets/gtzan.py

@@ -1064,6 +1064,7 @@ class GTZAN(Dataset):
                     continue
 
                 songs_in_genre = os.listdir(fulldir)
+                songs_in_genre.sort()
                 for fname in songs_in_genre:
                     name, ext = os.path.splitext(fname)
                     if ext.lower() == ".wav" and "." in name: