Added test to make sure loading and saving gives the same file

test/test.py

@@ -7,8 +7,8 @@ import os
 
 class Test_LoadSave(unittest.TestCase):
     test_dirpath = os.path.dirname(os.path.realpath(__file__))
-    test_filepath = os.path.join(
-        test_dirpath, "assets", "steam-train-whistle-daniel_simon.mp3")
+    test_filepath = os.path.join(test_dirpath, "assets",
+                                 "steam-train-whistle-daniel_simon.mp3")
 
     def test_load(self):
         # check normal loading
@@ -16,7 +16,6 @@ class Test_LoadSave(unittest.TestCase):
         self.assertEqual(sr, 44100)
         self.assertEqual(x.size(), (278756, 2))
         self.assertGreater(x.sum(), 0)
-        print
 
         # check normalizing
         x, sr = torchaudio.load(self.test_filepath, normalization=True)
@@ -28,8 +27,8 @@ class Test_LoadSave(unittest.TestCase):
             torchaudio.load("file-does-not-exist.mp3")
 
         with self.assertRaises(OSError):
-            tdir = os.path.join(os.path.dirname(
-                self.test_dirpath), "torchaudio")
+            tdir = os.path.join(
+                os.path.dirname(self.test_dirpath), "torchaudio")
             torchaudio.load(tdir)
 
     def test_save(self):
@@ -80,24 +79,35 @@ class Test_LoadSave(unittest.TestCase):
 
         # don't save to folders that don't exist
         with self.assertRaises(OSError):
-            new_filepath = os.path.join(
-                self.test_dirpath, "no-path", "test.wav")
+            new_filepath = os.path.join(self.test_dirpath, "no-path",
+                                        "test.wav")
             torchaudio.save(new_filepath, x, sr)
 
         # save created file
-        sinewave_filepath = os.path.join(
-            self.test_dirpath, "assets", "sinewave.wav")
+        sinewave_filepath = os.path.join(self.test_dirpath, "assets",
+                                         "sinewave.wav")
         sr = 16000
         freq = 440
         volume = 0.3
 
-        y = (torch.cos(2 * math.pi * torch.arange(0, 4 * sr) * freq / sr)).float()
+        y = (torch.cos(
+            2 * math.pi * torch.arange(0, 4 * sr) * freq / sr)).float()
         y.unsqueeze_(1)
         # y is between -1 and 1, so must scale
         y = (y * volume * 2**31).long()
         torchaudio.save(sinewave_filepath, y, sr)
         self.assertTrue(os.path.isfile(sinewave_filepath))
 
+    def test_load_and_save_is_identity(self):
+        input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav')
+        tensor, sample_rate = torchaudio.load(input_path)
+        output_path = os.path.join(self.test_dirpath, 'test.wav')
+        torchaudio.save(output_path, tensor, sample_rate)
+        tensor2, sample_rate2 = torchaudio.load(output_path)
+        self.assertTrue(tensor.allclose(tensor2))
+        self.assertEqual(sample_rate, sample_rate2)
+        os.unlink(output_path)
+
 
 if __name__ == '__main__':
     unittest.main()