Fixing a test in test_transforms.py which passes in a numpy array

test/test_transforms.py

@@ -107,18 +107,7 @@ class Tester(unittest.TestCase):
 
     def test_mu_law_companding(self):
 
-        sig = self.sig.clone()
-
         quantization_channels = 256
-        sig = self.sig.numpy()
-        sig = sig / np.abs(sig).max()
-        self.assertTrue(sig.min() >= -1. and sig.max() <= 1.)
-
-        sig_mu = transforms.MuLawEncoding(quantization_channels)(sig)
-        self.assertTrue(sig_mu.min() >= 0. and sig.max() <= quantization_channels)
-
-        sig_exp = transforms.MuLawExpanding(quantization_channels)(sig_mu)
-        self.assertTrue(sig_exp.min() >= -1. and sig_exp.max() <= 1.)
 
         sig = self.sig.clone()
         sig = sig / torch.abs(sig).max()

torchaudio/functional.py

@@ -316,7 +316,7 @@ def mu_law_encoding(x, qc):
     Outputs:
         Tensor: Input after mu-law companding
     """
-    assert(isinstance(x, torch.Tensor)), 'mu_law_encoding expects a Tensor'
+    assert isinstance(x, torch.Tensor), 'mu_law_encoding expects a Tensor'
     mu = qc - 1.
     if not x.dtype.is_floating_point:
         x = x.to(torch.float)
@@ -342,7 +342,7 @@ def mu_law_expanding(x_mu, qc):
     Outputs:
         Tensor: Input after decoding
     """
-    assert(isinstance(x_mu, torch.Tensor)), 'mu_law_expanding expects a Tensor'
+    assert isinstance(x_mu, torch.Tensor), 'mu_law_expanding expects a Tensor'
     mu = qc - 1.
     if not x_mu.dtype.is_floating_point:
         x_mu = x_mu.to(torch.float)