Remove numpy dependency from test_transforms.py, skip tests with missing dependencies. (#112)

test/test_transforms.py

 from __future__ import print_function
+import math
 import os
+
 import torch
 import torchaudio
+from torchaudio.common_utils import IMPORT_LIBROSA, IMPORT_SCIPY
 import torchaudio.transforms as transforms
-import numpy as np
 import unittest
 
+if IMPORT_LIBROSA:
+    import librosa
+
+if IMPORT_SCIPY:
+    import scipy
+
 
 class Tester(unittest.TestCase):
 
@@ -13,7 +21,7 @@ class Tester(unittest.TestCase):
     sr = 16000
     freq = 440
     volume = .3
-    sig = (torch.cos(2 * np.pi * torch.arange(0, 4 * sr).float() * freq / sr))
+    sig = (torch.cos(2 * math.pi * torch.arange(0, 4 * sr).float() * freq / sr))
     sig.unsqueeze_(1)  # (64000, 1)
     sig = (sig * volume * 2**31).long()
     # file for stereo stft test
@@ -27,8 +35,7 @@ class Tester(unittest.TestCase):
         result = transforms.Scale()(audio_orig)
         self.assertTrue(result.min() >= -1. and result.max() <= 1.)
 
-        maxminmax = np.abs(
-            [audio_orig.min(), audio_orig.max()]).max().astype(np.float)
+        maxminmax = float(max(abs(audio_orig.min()), abs(audio_orig.max())))
         result = transforms.Scale(factor=maxminmax)(audio_orig)
         self.assertTrue((result.min() == -1. or result.max() == 1.) and
                         result.min() >= -1. and result.max() <= 1.)
@@ -91,14 +98,13 @@ class Tester(unittest.TestCase):
         audio_orig = self.sig.clone()
         length_orig = audio_orig.size(0)
         length_new = int(length_orig * 1.2)
-        maxminmax = np.abs(
-            [audio_orig.min(), audio_orig.max()]).max().astype(np.float)
+        maxminmax = float(max(abs(audio_orig.min()), abs(audio_orig.max())))
 
         tset = (transforms.Scale(factor=maxminmax),
                 transforms.PadTrim(max_len=length_new, channels_first=False))
         result = transforms.Compose(tset)(audio_orig)
 
-        self.assertTrue(np.abs([result.min(), result.max()]).max() == 1.)
+        self.assertTrue(max(abs(result.min()), abs(result.max())) == 1.)
 
         self.assertTrue(result.size(0) == length_new)
 
@@ -194,87 +200,107 @@ class Tester(unittest.TestCase):
         torch_mfcc_norm_none = mfcc_transform_norm_none(audio_scaled)
 
         norm_check = torch_mfcc.clone()
-        norm_check[:, :, 0] *= np.sqrt(n_mels) * 2
-        norm_check[:, :, 1:] *= np.sqrt(n_mels / 2) * 2
+        norm_check[:, :, 0] *= math.sqrt(n_mels) * 2
+        norm_check[:, :, 1:] *= math.sqrt(n_mels / 2) * 2
 
         self.assertTrue(torch_mfcc_norm_none.allclose(norm_check))
 
+    @unittest.skipIf(not IMPORT_LIBROSA or not IMPORT_SCIPY, 'Librosa and scipy are not available')
     def test_librosa_consistency(self):
-        try:
-            import librosa
-            import scipy
-        except ImportError:
-            return
-
-        input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav')
-        sound, sample_rate = torchaudio.load(input_path)
-        sound_librosa = sound.cpu().numpy().squeeze().T  # squeeze batch and channel first
-
-        n_fft = 400
-        hop_length = 200
-        power = 2.0
-        n_mels = 128
-        n_mfcc = 40
-        sample_rate = 16000
-
-        # test core spectrogram
-        spect_transform = torchaudio.transforms.Spectrogram(n_fft=n_fft, hop=hop_length, power=2)
-        out_librosa, _ = librosa.core.spectrum._spectrogram(y=sound_librosa,
-                                                            n_fft=n_fft,
-                                                            hop_length=hop_length,
-                                                            power=2)
-
-        out_torch = spect_transform(sound).squeeze().cpu().numpy().T
-        self.assertTrue(np.allclose(out_torch, out_librosa, atol=1e-5))
-
-        # test mel spectrogram
-        melspect_transform = torchaudio.transforms.MelSpectrogram(sr=sample_rate, window=torch.hann_window,
-                                                                  hop=hop_length, n_mels=n_mels, n_fft=n_fft)
-        librosa_mel = librosa.feature.melspectrogram(y=sound_librosa, sr=sample_rate,
-                                                     n_fft=n_fft, hop_length=hop_length, n_mels=n_mels,
-                                                     htk=True, norm=None)
-        torch_mel = melspect_transform(sound).squeeze().cpu().numpy().T
-
-        # lower tolerance, think it's double vs. float
-        self.assertTrue(np.allclose(torch_mel, librosa_mel, atol=5e-3))
-
-        # test s2db
-
-        db_transform = torchaudio.transforms.SpectrogramToDB("power", 80.)
-        db_torch = db_transform(spect_transform(sound)).squeeze().cpu().numpy().T
-        db_librosa = librosa.core.spectrum.power_to_db(out_librosa)
-        self.assertTrue(np.allclose(db_torch, db_librosa, atol=5e-3))
-
-        db_torch = db_transform(melspect_transform(sound)).squeeze().cpu().numpy().T
-        db_librosa = librosa.core.spectrum.power_to_db(librosa_mel)
-
-        self.assertTrue(np.allclose(db_torch, db_librosa, atol=5e-3))
-
-        # test MFCC
-        melkwargs = {'hop': hop_length, 'n_fft': n_fft}
-        mfcc_transform = torchaudio.transforms.MFCC(sr=sample_rate,
-                                                    n_mfcc=n_mfcc,
-                                                    norm='ortho',
-                                                    melkwargs=melkwargs)
-
-        # librosa.feature.mfcc doesn't pass kwargs properly since some of the
-        # kwargs for melspectrogram and mfcc are the same. We just follow the
-        # function body in https://librosa.github.io/librosa/_modules/librosa/feature/spectral.html#melspectrogram
-        # to mirror this function call with correct args:
-
-#         librosa_mfcc = librosa.feature.mfcc(y=sound_librosa,
-#                                             sr=sample_rate,
-#                                             n_mfcc = n_mfcc,
-#                                             hop_length=hop_length,
-#                                             n_fft=n_fft,
-#                                             htk=True,
-#                                             norm=None,
-#                                             n_mels=n_mels)
-
-        librosa_mfcc = scipy.fftpack.dct(db_librosa, axis=0, type=2, norm='ortho')[:n_mfcc]
-        torch_mfcc = mfcc_transform(sound).squeeze().cpu().numpy().T
-
-        self.assertTrue(np.allclose(torch_mfcc, librosa_mfcc, atol=5e-3))
+        def _test_librosa_consistency_helper(n_fft, hop_length, power, n_mels, n_mfcc, sample_rate):
+            input_path = os.path.join(self.test_dirpath, 'assets', 'sinewave.wav')
+            sound, sample_rate = torchaudio.load(input_path)
+            sound_librosa = sound.cpu().numpy().squeeze().T  # squeeze batch and channel first
+
+            # test core spectrogram
+            spect_transform = torchaudio.transforms.Spectrogram(n_fft=n_fft, hop=hop_length, power=2)
+            out_librosa, _ = librosa.core.spectrum._spectrogram(y=sound_librosa,
+                                                                n_fft=n_fft,
+                                                                hop_length=hop_length,
+                                                                power=2)
+
+            out_torch = spect_transform(sound).squeeze().cpu().t()
+            self.assertTrue(torch.allclose(out_torch, torch.from_numpy(out_librosa), atol=1e-5))
+
+            # test mel spectrogram
+            melspect_transform = torchaudio.transforms.MelSpectrogram(sr=sample_rate, window=torch.hann_window,
+                                                                      hop=hop_length, n_mels=n_mels, n_fft=n_fft)
+            librosa_mel = librosa.feature.melspectrogram(y=sound_librosa, sr=sample_rate,
+                                                         n_fft=n_fft, hop_length=hop_length, n_mels=n_mels,
+                                                         htk=True, norm=None)
+            torch_mel = melspect_transform(sound).squeeze().cpu().t()
+
+            # lower tolerance, think it's double vs. float
+            self.assertTrue(torch.allclose(torch_mel.type(torch.double), torch.from_numpy(librosa_mel), atol=5e-3))
+
+            # test s2db
+
+            db_transform = torchaudio.transforms.SpectrogramToDB("power", 80.)
+            db_torch = db_transform(spect_transform(sound)).squeeze().cpu().t()
+            db_librosa = librosa.core.spectrum.power_to_db(out_librosa)
+            self.assertTrue(torch.allclose(db_torch, torch.from_numpy(db_librosa), atol=5e-3))
+
+            db_torch = db_transform(melspect_transform(sound)).squeeze().cpu().t()
+            db_librosa = librosa.core.spectrum.power_to_db(librosa_mel)
+
+            self.assertTrue(torch.allclose(db_torch.type(torch.double), torch.from_numpy(db_librosa), atol=5e-3))
+
+            # test MFCC
+            melkwargs = {'hop': hop_length, 'n_fft': n_fft}
+            mfcc_transform = torchaudio.transforms.MFCC(sr=sample_rate,
+                                                        n_mfcc=n_mfcc,
+                                                        norm='ortho',
+                                                        melkwargs=melkwargs)
+
+            # librosa.feature.mfcc doesn't pass kwargs properly since some of the
+            # kwargs for melspectrogram and mfcc are the same. We just follow the
+            # function body in https://librosa.github.io/librosa/_modules/librosa/feature/spectral.html#melspectrogram
+            # to mirror this function call with correct args:
+
+    #         librosa_mfcc = librosa.feature.mfcc(y=sound_librosa,
+    #                                             sr=sample_rate,
+    #                                             n_mfcc = n_mfcc,
+    #                                             hop_length=hop_length,
+    #                                             n_fft=n_fft,
+    #                                             htk=True,
+    #                                             norm=None,
+    #                                             n_mels=n_mels)
+
+            librosa_mfcc = scipy.fftpack.dct(db_librosa, axis=0, type=2, norm='ortho')[:n_mfcc]
+            torch_mfcc = mfcc_transform(sound).squeeze().cpu().t()
+
+            self.assertTrue(torch.allclose(torch_mfcc.type(torch.double), torch.from_numpy(librosa_mfcc), atol=5e-3))
+
+        kwargs1 = {
+            'n_fft': 400,
+            'hop_length': 200,
+            'power': 2.0,
+            'n_mels': 128,
+            'n_mfcc': 40,
+            'sample_rate': 16000
+        }
+
+        kwargs2 = {
+            'n_fft': 600,
+            'hop_length': 100,
+            'power': 2.0,
+            'n_mels': 128,
+            'n_mfcc': 20,
+            'sample_rate': 16000
+        }
+
+        kwargs3 = {
+            'n_fft': 200,
+            'hop_length': 50,
+            'power': 2.0,
+            'n_mels': 128,
+            'n_mfcc': 50,
+            'sample_rate': 24000
+        }
+
+        _test_librosa_consistency_helper(**kwargs1)
+        _test_librosa_consistency_helper(**kwargs2)
+        _test_librosa_consistency_helper(**kwargs3)
 
 
 if __name__ == '__main__':

torchaudio/common_utils.py

+import sys
+
+PY3 = sys.version_info > (3, 0)
+PY34 = sys.version_info >= (3, 4)
+
+
+def _check_module_exists(name):
+    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).
+    """
+    if not PY3:  # Python 2
+        import imp
+        try:
+            imp.find_module(name)
+            return True
+        except ImportError:
+            return False
+    elif not PY34:  # Python [3, 3.4)
+        import importlib
+        loader = importlib.find_loader(name)
+        return loader is not None
+    else:  # Python >= 3.4
+        import importlib
+        import importlib.util
+        spec = importlib.util.find_spec(name)
+        return spec is not None
+
+
+IMPORT_SCIPY = _check_module_exists('scipy')
+
+# On Py2, importing librosa 0.6.1 triggers a TypeError (if using newest joblib)
+# see librosa/librosa#729.
+# TODO: allow Py2 when librosa 0.6.2 releases
+IMPORT_LIBROSA = _check_module_exists('librosa') and PY3