Set individual backends

test/README.md

@@ -47,7 +47,7 @@ The following test modules are defined for corresponding `torchaudio` module/fun
 The following is the current practice of torchaudio test suite.
 
 1. Unless the tests are related to I/O, use synthetic data. [`common_utils`](./common_utils.py) has some data generator functions.
-1. When you add a new test case, use `common_utils.TorchaudioTestCase` as base class unless your are writing tests that are common to CPU / CUDA.
+1. When you add a new test case, use `common_utils.TorchaudioTestCase` as base class unless you are writing tests that are common to CPU / CUDA.
   - Set class memeber `dtype`, `device` and `backend` for the desired behavior.
   - If you do not set `backend` value in your test suite, then I/O functions will be unassigned and attempt to load/save file will fail.
   - For `backend` value, in addition to available backends, you can also provide the value "default" and backend will be picked automatically based on availability.

test/test_batch_consistency.py

@@ -9,6 +9,7 @@
 
 
 class TestFunctional(common_utils.TorchaudioTestCase):
+    backend = 'default'
     """Test functions defined in `functional` module"""
     def assert_batch_consistency(
             self, functional, tensor, *args, batch_size=1, atol=1e-8, rtol=1e-5, seed=42, **kwargs):
@@ -97,12 +98,15 @@ def test_sliding_window_cmn(self):
         self.assert_batch_consistencies(F.sliding_window_cmn, waveform, center=False, norm_vars=False)
 
     def test_vad(self):
+        common_utils.set_audio_backend('default')
         filepath = common_utils.get_asset_path("vad-go-mono-32000.wav")
         waveform, sample_rate = torchaudio.load(filepath)
         self.assert_batch_consistencies(F.vad, waveform, sample_rate=sample_rate)
 
 
 class TestTransforms(common_utils.TorchaudioTestCase):
+    backend = 'default'
+
     """Test suite for classes defined in `transforms` module"""
     def test_batch_AmplitudeToDB(self):
         spec = torch.rand((6, 201))

test/test_datasets.py

@@ -47,7 +47,7 @@ def test_cmuarctic(self):
 
 
 @common_utils.skipIfNoSoxBackend
-class TestCommonVoise(common_utils.TorchaudioTestCase):
+class TestCommonVoice(common_utils.TorchaudioTestCase):
     backend = 'sox'
     path = common_utils.get_asset_path()
 

test/test_librosa_compatibility.py

@@ -159,6 +159,7 @@ def _load_audio_asset(*asset_paths, **kwargs):
 class TestTransforms(common_utils.TorchaudioTestCase):
     """Test suite for functions in `transforms` module."""
     def assert_compatibilities(self, n_fft, hop_length, power, n_mels, n_mfcc, sample_rate):
+        common_utils.set_audio_backend('default')
         sound, sample_rate = _load_audio_asset('sinewave.wav')
         sound_librosa = sound.cpu().numpy().squeeze()  # (64000)
 
@@ -268,13 +269,15 @@ def test_basics4(self):
         }
         self.assert_compatibilities(**kwargs)
 
+    @unittest.skipIf(not common_utils.BACKENDS_MP3, 'no backend to read mp3')
     def test_MelScale(self):
         """MelScale transform is comparable to that of librosa"""
         n_fft = 2048
         n_mels = 256
         hop_length = n_fft // 4
 
         # Prepare spectrogram input. We use torchaudio to compute one.
+        common_utils.set_audio_backend('default')
         sound, sample_rate = _load_audio_asset('whitenoise_1min.mp3')
         sound = sound.mean(dim=0, keepdim=True)
         spec_ta = F.spectrogram(
@@ -297,6 +300,7 @@ def test_InverseMelScale(self):
         hop_length = n_fft // 4
 
         # Prepare mel spectrogram input. We use torchaudio to compute one.
+        common_utils.set_audio_backend('default')
         sound, sample_rate = _load_audio_asset(
             'steam-train-whistle-daniel_simon.wav', offset=2**10, num_frames=2**14)
         sound = sound.mean(dim=0, keepdim=True)