Clean up common_utils

test/test_librosa_compatibility.py

@@ -7,9 +7,11 @@
 from torch.testing._internal.common_utils import TestCase
 import torchaudio
 import torchaudio.functional as F
-from torchaudio.common_utils import IMPORT_LIBROSA
+from torchaudio.common_utils import _check_module_exists
 
-if IMPORT_LIBROSA:
+LIBROSA_AVAILABLE = _check_module_exists('librosa')
+
+if LIBROSA_AVAILABLE:
     import numpy as np
     import librosa
     import scipy
@@ -19,7 +21,7 @@
 from . import common_utils
 
 
-@unittest.skipIf(not IMPORT_LIBROSA, "Librosa not available")
+@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available")
 class TestFunctional(TestCase):
     """Test suite for functions in `functional` module."""
     def test_griffinlim(self):
@@ -115,12 +117,8 @@ def test_amplitude_to_DB(self):
 ])
 @pytest.mark.parametrize('rate', [0.5, 1.01, 1.3])
 @pytest.mark.parametrize('hop_length', [256])
+@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available")
 def test_phase_vocoder(complex_specgrams, rate, hop_length):
-
-    # Using a decorator here causes parametrize to fail on Python 2
-    if not IMPORT_LIBROSA:
-        raise unittest.SkipTest('Librosa is not available')
-
     # Due to cummulative sum, numerical error in using torch.float32 will
     # result in bottom right values of the stretched sectrogram to not
     # match with librosa.
@@ -158,7 +156,7 @@ def _load_audio_asset(*asset_paths, **kwargs):
     return sound, sample_rate
 
 
-@unittest.skipIf(not IMPORT_LIBROSA, "Librosa not available")
+@unittest.skipIf(not LIBROSA_AVAILABLE, "Librosa not available")
 class TestTransforms(TestCase):
     """Test suite for functions in `transforms` module."""
     def assert_compatibilities(self, n_fft, hop_length, power, n_mels, n_mfcc, sample_rate):

torchaudio/common_utils.py

@@ -1,17 +1,11 @@
 import importlib.util
 
 
-def _check_module_exists(name: str) -> bool:
+def _check_module_exists(*modules: str) -> bool:
     r"""Returns if a top-level module with :attr:`name` exists *without**
     importing it. This is generally safer than try-catch block around a
     `import X`. It avoids third party libraries breaking assumptions of some of
     our tests, e.g., setting multiprocessing start method when imported
     (see librosa/#747, torchvision/#544).
     """
-    spec = importlib.util.find_spec(name)
-    return spec is not None
-
-IMPORT_NUMPY = _check_module_exists('numpy')
-IMPORT_KALDI_IO = _check_module_exists('kaldi_io')
-IMPORT_SCIPY = _check_module_exists('scipy')
-IMPORT_LIBROSA = _check_module_exists('librosa')
+    return all(importlib.util.find_spec(m) is not None for m in modules)

torchaudio/kaldi_io.py

@@ -1,16 +1,16 @@
 # To use this file, the dependency (https://github.com/vesis84/kaldi-io-for-python)
 # needs to be installed. This is a light wrapper around kaldi_io that returns
 # torch.Tensors.
-from typing import Any, Callable, Iterable, Tuple, Union
+from typing import Any, Callable, Iterable, Tuple
 
 import torch
 from torch import Tensor
-from torchaudio.common_utils import IMPORT_KALDI_IO, IMPORT_NUMPY
+from torchaudio.common_utils import _check_module_exists
 
-if IMPORT_NUMPY:
-    import numpy as np
+_KALDI_IO_AVAILABLE = _check_module_exists('kaldi_io', 'numpy')
 
-if IMPORT_KALDI_IO:
+if _KALDI_IO_AVAILABLE:
+    import numpy as np
     import kaldi_io
 
 
@@ -38,7 +38,7 @@ def _convert_method_output_to_tensor(file_or_fd: Any,
     Returns:
         Iterable[Tuple[str, Tensor]]: The string is the key and the tensor is vec/mat
     """
-    if not IMPORT_KALDI_IO:
+    if not _KALDI_IO_AVAILABLE:
         raise ImportError('Could not import kaldi_io. Did you install it?')
 
     for key, np_arr in fn(file_or_fd):