fix lint

test/test.py

@@ -4,14 +4,17 @@ import torchaudio
 import math
 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
         x, sr = torchaudio.load(self.test_filepath)
         self.assertEqual(sr, 44100)
-        self.assertEqual(x.size(), (278756,2))
+        self.assertEqual(x.size(), (278756, 2))
 
         # check normalizing
         x, sr = torchaudio.load(self.test_filepath, normalization=True)
@@ -23,7 +26,8 @@ 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):
@@ -43,21 +47,21 @@ class Test_LoadSave(unittest.TestCase):
         os.unlink(new_filepath)
 
         # test save 1d tensor
-        x = x[:, 0] # get mono signal
-        x.squeeze_() # remove channel dim
+        x = x[:, 0]  # get mono signal
+        x.squeeze_()  # remove channel dim
         torchaudio.save(new_filepath, x, sr)
         self.assertTrue(os.path.isfile(new_filepath))
         os.unlink(new_filepath)
 
         # don't allow invalid sizes as inputs
         with self.assertRaises(ValueError):
-            x.unsqueeze_(0) # N x L not L x N
+            x.unsqueeze_(0)  # N x L not L x N
             torchaudio.save(new_filepath, x, sr)
 
         with self.assertRaises(ValueError):
             x.squeeze_()
             x.unsqueeze_(1)
-            x.unsqueeze_(0) # 1 x L x 1
+            x.unsqueeze_(0)  # 1 x L x 1
             torchaudio.save(new_filepath, x, sr)
 
         # automatically convert sr from floating point to int
@@ -74,21 +78,24 @@ 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()
+        y = (y * volume * 2**31).long()
         torchaudio.save(sinewave_filepath, y, sr)
         self.assertTrue(os.path.isfile(sinewave_filepath))
 
+
 if __name__ == '__main__':
     unittest.main()

test/test_transforms.py

@@ -5,14 +5,15 @@ import torchaudio.transforms as transforms
 import numpy as np
 import unittest
 
+
 class Tester(unittest.TestCase):
 
     sr = 16000
     freq = 440
     volume = 0.3
-    sig = (torch.cos(2*np.pi*torch.arange(0, 4*sr) * freq/sr)).float()
+    sig = (torch.cos(2 * np.pi * torch.arange(0, 4 * sr) * freq / sr)).float()
     sig.unsqueeze_(1)
-    sig = (sig*volume*2**31).long()
+    sig = (sig * volume * 2**31).long()
 
     def test_scale(self):
 
@@ -21,7 +22,8 @@ class Tester(unittest.TestCase):
         self.assertTrue(result.min() >= -1. and result.max() <= 1.,
                         print("min: {}, max: {}".format(result.min(), result.max())))
 
-        maxminmax = np.abs([audio_orig.min(), audio_orig.max()]).max().astype(np.float)
+        maxminmax = np.abs(
+            [audio_orig.min(), audio_orig.max()]).max().astype(np.float)
         result = transforms.Scale(factor=maxminmax)(audio_orig)
         self.assertTrue((result.min() == -1. or result.max() == 1.) and
                         result.min() >= -1. and result.max() <= 1.,
@@ -47,7 +49,6 @@ class Tester(unittest.TestCase):
         self.assertTrue(result.size(0) == length_new,
                         print("old size: {}, new size: {}".format(audio_orig.size(0), result.size(0))))
 
-
     def test_downmix_mono(self):
 
         audio_L = self.sig.clone()
@@ -84,7 +85,8 @@ 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 = np.abs(
+            [audio_orig.min(), audio_orig.max()]).max().astype(np.float)
 
         tset = (transforms.Scale(factor=maxminmax),
                 transforms.PadTrim(max_len=length_new))
@@ -109,10 +111,6 @@ class Tester(unittest.TestCase):
         sig_exp = transforms.MuLawExpanding(quantization_channels)(sig_mu)
         self.assertTrue(sig_exp.min() >= -1. and sig_exp.max() <= 1.)
 
-        #diff = sig - sig_exp
-        #mse = np.linalg.norm(diff) / diff.shape[0]
-        #self.assertTrue(mse, np.isclose(mse, 0., atol=1e-4)) # not always true
-
         sig = self.sig.clone()
         sig = sig / torch.abs(sig).max()
         self.assertTrue(sig.min() >= -1. and sig.max() <= 1.)
@@ -123,5 +121,6 @@ class Tester(unittest.TestCase):
         sig_exp = transforms.MuLawExpanding(quantization_channels)(sig_mu)
         self.assertTrue(sig_exp.min() >= -1. and sig_exp.max() <= 1.)
 
+
 if __name__ == '__main__':
     unittest.main()

torchaudio/__init__.py

@@ -14,7 +14,9 @@ from torchaudio import datasets
 if sys.version_info >= (3, 0):
     _bytes = bytes
 else:
-    _bytes = lambda s, e: s.encode(e)
+    def _bytes(s, e):
+        return s.encode(e)
+
 
 def check_input(src):
     if not torch.is_tensor(src):
@@ -22,13 +24,16 @@ def check_input(src):
     if not src.__module__ == 'torch':
         raise TypeError('Expected a CPU based tensor, got %s' % type(src))
 
+
 def load(filepath, out=None, normalization=None):
     """Loads an audio file from disk into a Tensor
-    
+
     Args:
         filepath (string): path to audio file
         out (Tensor, optional): an output Tensor to use instead of creating one
-        normalization (bool or number, optional): If boolean `True`, then output is divided by `1 << 31` (assumes 16-bit depth audio, and normalizes to `[0, 1]`. If `number`, then output is divided by that number
+        normalization (bool or number, optional): If boolean `True`, then output is divided by `1 << 31`
+                                                  (assumes 16-bit depth audio, and normalizes to `[0, 1]`.
+                                                  If `number`, then output is divided by that number
 
     Returns: tuple(Tensor, int)
        - Tensor: output Tensor of size `[L x C]` where L is the number of audio frames, C is the number of channels
@@ -41,7 +46,7 @@ def load(filepath, out=None, normalization=None):
         torch.Size([278756, 2])
         >>> print(sample_rate)
         44100
-        
+
     """
     # check if valid file
     if not os.path.isfile(filepath):
@@ -59,24 +64,26 @@ def load(filepath, out=None, normalization=None):
     sample_rate = sample_rate_p[0]
     # normalize if needed
     if isinstance(normalization, bool) and normalization:
-        out /= 1 << 31 # assuming 16-bit depth
+        out /= 1 << 31  # assuming 16-bit depth
     elif isinstance(normalization, (float, int)):
-        out /= normalization # normalize with custom value
+        out /= normalization  # normalize with custom value
     return out, sample_rate
 
+
 def save(filepath, src, sample_rate):
     """Saves a Tensor with audio signal to disk as a standard format like mp3, wav, etc.
-    
+
     Args:
         filepath (string): path to audio file
-        src (Tensor): an input 2D Tensor of shape `[L x C]` where L is the number of audio frames, C is the number of channels
+        src (Tensor): an input 2D Tensor of shape `[L x C]` where L is
+                      the number of audio frames, C is the number of channels
         sample_rate (int): the sample-rate of the audio to be saved
 
     Example::
 
         >>> data, sample_rate = torchaudio.load('foo.mp3')
         >>> torchaudio.save('foo.wav', data, sample_rate)
-        
+
     """
     # check if save directory exists
     abs_dirpath = os.path.dirname(os.path.abspath(filepath))
@@ -87,7 +94,8 @@ def save(filepath, src, sample_rate):
         # 1d tensors as assumed to be mono signals
         src.unsqueeze_(1)
     elif len(src.size()) > 2 or src.size(1) > 2:
-        raise ValueError("Expected format (L x N), N = 1 or 2, but found {}".format(src.size()))
+        raise ValueError(
+            "Expected format (L x N), N = 1 or 2, but found {}".format(src.size()))
     # check if sample_rate is an integer
     if not isinstance(sample_rate, int):
         if int(sample_rate) == sample_rate:
@@ -96,11 +104,12 @@ def save(filepath, src, sample_rate):
             raise TypeError('Sample rate should be a integer')
     # programs such as librosa normalize the signal, unnormalize if detected
     if src.min() >= -1.0 and src.max() <= 1.0:
-        src = src * (1 << 31) # assuming 16-bit depth
+        src = src * (1 << 31)  # assuming 16-bit depth
         src = src.long()
     # save data to file
     filename, extension = os.path.splitext(filepath)
     check_input(src)
     typename = type(src).__name__.replace('Tensor', '')
     func = getattr(th_sox, 'libthsox_{}_write_audio_file'.format(typename))
-    func(_bytes(filepath, "utf-8"), src, _bytes(extension[1:], "utf-8"), sample_rate)
+    func(_bytes(filepath, "utf-8"), src,
+         _bytes(extension[1:], "utf-8"), sample_rate)

torchaudio/datasets/vctk.py

@@ -12,9 +12,11 @@ AUDIO_EXTENSIONS = [
     '.WAV', '.MP3', '.FLAC', '.SPH', '.OGG', '.OPUS',
 ]
 
+
 def is_audio_file(filename):
     return any(filename.endswith(extension) for extension in AUDIO_EXTENSIONS)
 
+
 def make_manifest(dir):
     audios = []
     dir = os.path.expanduser(dir)
@@ -31,6 +33,7 @@ def make_manifest(dir):
                     audios.append(item)
     return audios
 
+
 def read_audio(fp, downsample=True):
     sig, sr = torchaudio.load(fp)
     if downsample:
@@ -41,6 +44,7 @@ def read_audio(fp, downsample=True):
             sig = sig[:-(sig.size(0) % 3):3].contiguous()
     return sig, sr
 
+
 def load_txts(dir):
     """Create a dictionary with all the text of the audio transcriptions."""
     utterences = dict()
@@ -55,12 +59,12 @@ def load_txts(dir):
             for fname in fnames:
                 if fname.endswith(".txt"):
                     with open(os.path.join(root, fname), "r") as f:
-                        fname_no_ext = os.path.basename(fname).rsplit(".", 1)[0]
+                        fname_no_ext = os.path.basename(
+                            fname).rsplit(".", 1)[0]
                         utterences[fname_no_ext] = f.readline()
-                        #utterences += f.readlines()
-    #utterences = dict([tuple(u.strip().split(" ", 1)) for u in utterences])
     return utterences
 
+
 class VCTK(data.Dataset):
     """`VCTK <http://homepages.inf.ed.ac.uk/jyamagis/page3/page58/page58.html>`_ Dataset.
     `alternate url <http://datashare.is.ed.ac.uk/handle/10283/2651>`
@@ -103,7 +107,8 @@ class VCTK(data.Dataset):
             raise RuntimeError('Dataset not found.' +
                                ' You can use download=True to download it')
         self._read_info()
-        self.data, self.labels = torch.load(os.path.join(self.root, self.processed_folder, "vctk_{:04d}.pt".format(self.cached_pt)))
+        self.data, self.labels = torch.load(os.path.join(
+            self.root, self.processed_folder, "vctk_{:04d}.pt".format(self.cached_pt)))
 
     def __getitem__(self, index):
         """
@@ -115,7 +120,8 @@ class VCTK(data.Dataset):
         """
         if self.cached_pt != index // self.chunk_size:
             self.cached_pt = int(index // self.chunk_size)
-            self.data, self.labels = torch.load(os.path.join(self.root, self.processed_folder, "vctk_{:04d}.pt".format(self.cached_pt)))
+            self.data, self.labels = torch.load(os.path.join(
+                self.root, self.processed_folder, "vctk_{:04d}.pt".format(self.cached_pt)))
         index = index % self.chunk_size
         audio, target = self.data[index], self.labels[index]
 
@@ -134,13 +140,15 @@ class VCTK(data.Dataset):
         return os.path.exists(os.path.join(self.root, self.processed_folder, "vctk_info.txt"))
 
     def _write_info(self, num_items):
-        info_path = os.path.join(self.root, self.processed_folder, "vctk_info.txt")
+        info_path = os.path.join(
+            self.root, self.processed_folder, "vctk_info.txt")
         with open(info_path, "w") as f:
             f.write("num_samples,{}\n".format(num_items))
             f.write("max_len,{}\n".format(self.max_len))
 
     def _read_info(self):
-        info_path = os.path.join(self.root, self.processed_folder, "vctk_info.txt")
+        info_path = os.path.join(
+            self.root, self.processed_folder, "vctk_info.txt")
         with open(info_path, "r") as f:
             self.num_samples = int(f.readline().split(",")[1])
             self.max_len = int(f.readline().split(",")[1])
@@ -155,7 +163,8 @@ class VCTK(data.Dataset):
 
         raw_abs_dir = os.path.join(self.root, self.raw_folder)
         processed_abs_dir = os.path.join(self.root, self.processed_folder)
-        dset_abs_path = os.path.join(self.root, self.raw_folder, self.dset_path)
+        dset_abs_path = os.path.join(
+            self.root, self.raw_folder, self.dset_path)
 
         # download files
         try:
@@ -192,7 +201,8 @@ class VCTK(data.Dataset):
         audios = make_manifest(dset_abs_path)
         utterences = load_txts(dset_abs_path)
         self.max_len = 0
-        print("Found {} audio files and {} utterences".format(len(audios), len(utterences)))
+        print("Found {} audio files and {} utterences".format(
+            len(audios), len(utterences)))
         for n in range(len(audios) // self.chunk_size + 1):
             tensors = []
             labels = []
@@ -207,9 +217,11 @@ class VCTK(data.Dataset):
                     tensors.append(sig)
                     lengths.append(sig.size(0))
                     labels.append(utterences[f_rel_no_ext])
-                    self.max_len = sig.size(0) if sig.size(0) > self.max_len else self.max_len
+                    self.max_len = sig.size(0) if sig.size(
+                        0) > self.max_len else self.max_len
             # sort sigs/labels: longest -> shortest
-            tensors, labels = zip(*[(b, c) for (a,b,c) in sorted(zip(lengths, tensors, labels), key=lambda x: x[0], reverse=True)])
+            tensors, labels = zip(*[(b, c) for (a, b, c) in sorted(
+                zip(lengths, tensors, labels), key=lambda x: x[0], reverse=True)])
             data = (tensors, labels)
             torch.save(
                 data,
@@ -219,7 +231,7 @@ class VCTK(data.Dataset):
                     "vctk_{:04d}.pt".format(n)
                 )
             )
-        self._write_info((n*self.chunk_size)+i+1)
+        self._write_info((n * self.chunk_size) + i + 1)
         if not self.dev_mode:
             shutil.rmtree(raw_abs_dir, ignore_errors=True)
 

torchaudio/datasets/yesno.py

@@ -7,6 +7,7 @@ import errno
 import torch
 import torchaudio
 
+
 class YESNO(data.Dataset):
     """`YesNo Hebrew <http://www.openslr.org/1/>`_ Dataset.
 
@@ -45,7 +46,8 @@ class YESNO(data.Dataset):
         if not self._check_exists():
             raise RuntimeError('Dataset not found.' +
                                ' You can use download=True to download it')
-        self.data, self.labels = torch.load(os.path.join(self.root, self.processed_folder, self.processed_file))
+        self.data, self.labels = torch.load(os.path.join(
+            self.root, self.processed_folder, self.processed_file))
 
     def __getitem__(self, index):
         """
@@ -81,7 +83,8 @@ class YESNO(data.Dataset):
 
         raw_abs_dir = os.path.join(self.root, self.raw_folder)
         processed_abs_dir = os.path.join(self.root, self.processed_folder)
-        dset_abs_path = os.path.join(self.root, self.raw_folder, self.dset_path)
+        dset_abs_path = os.path.join(
+            self.root, self.raw_folder, self.dset_path)
 
         # download files
         try:
@@ -130,7 +133,8 @@ class YESNO(data.Dataset):
             lengths.append(sig.size(0))
             labels.append(os.path.basename(f).split(".", 1)[0].split("_"))
         # sort sigs/labels: longest -> shortest
-        tensors, labels = zip(*[(b, c) for (a,b,c) in sorted(zip(lengths, tensors, labels), key=lambda x: x[0], reverse=True)])
+        tensors, labels = zip(*[(b, c) for (a, b, c) in sorted(
+            zip(lengths, tensors, labels), key=lambda x: x[0], reverse=True)])
         self.max_len = tensors[0].size(0)
         torch.save(
             (tensors, labels),

torchaudio/transforms.py

@@ -6,6 +6,7 @@ try:
 except ImportError:
     librosa = None
 
+
 class Compose(object):
     """Composes several transforms together.
 
@@ -27,6 +28,7 @@ class Compose(object):
             audio = t(audio)
         return audio
 
+
 class Scale(object):
     """Scale audio tensor from a 16-bit integer (represented as a FloatTensor)
     to a floating point number between -1.0 and 1.0.  Note the 16-bit number is
@@ -55,6 +57,7 @@ class Scale(object):
 
         return tensor / self.factor
 
+
 class PadTrim(object):
     """Pad/Trim a 1d-Tensor (Signal or Labels)
 
@@ -76,7 +79,7 @@ class PadTrim(object):
 
         """
         if self.max_len > tensor.size(0):
-            pad = torch.ones((self.max_len-tensor.size(0),
+            pad = torch.ones((self.max_len - tensor.size(0),
                               tensor.size(1))) * self.fill_value
             pad = pad.type_as(tensor)
             tensor = torch.cat((tensor, pad), dim=0)
@@ -107,6 +110,7 @@ class DownmixMono(object):
             tensor = torch.mean(tensor.float(), 1, True)
         return tensor
 
+
 class LC2CL(object):
     """Permute a 2d tensor from samples (Length) x Channels to Channels x
        samples (Length)
@@ -153,14 +157,16 @@ class MEL(object):
             return tensor
         L = []
         for i in range(tensor.size(1)):
-            nparr = tensor[:, i].numpy() # (samples, )
-            sgram = librosa.feature.melspectrogram(nparr, **self.kwargs) # (n_mels, hops)
+            nparr = tensor[:, i].numpy()  # (samples, )
+            sgram = librosa.feature.melspectrogram(
+                nparr, **self.kwargs)  # (n_mels, hops)
             L.append(sgram)
-        L = np.stack(L, 2) # (n_mels, hops, channels)
+        L = np.stack(L, 2)  # (n_mels, hops, channels)
         tensor = torch.from_numpy(L).type_as(tensor)
 
         return tensor
 
+
 class BLC2CBL(object):
     """Permute a 3d tensor from Bands x samples (Length) x Channels to Channels x
        Bands x samples (Length)
@@ -179,6 +185,7 @@ class BLC2CBL(object):
 
         return tensor.permute(2, 0, 1).contiguous()
 
+
 class MuLawEncoding(object):
     """Encode signal based on mu-law companding.  For more info see the
     `Wikipedia Entry <https://en.wikipedia.org/wiki/%CE%9C-law_algorithm>`_
@@ -212,10 +219,12 @@ class MuLawEncoding(object):
             if isinstance(x, torch.LongTensor):
                 x = x.float()
             mu = torch.FloatTensor([mu])
-            x_mu = torch.sign(x) * torch.log1p(mu * torch.abs(x)) / torch.log1p(mu)
+            x_mu = torch.sign(x) * torch.log1p(mu *
+                                               torch.abs(x)) / torch.log1p(mu)
             x_mu = ((x_mu + 1) / 2 * mu + 0.5).long()
         return x_mu
 
+
 class MuLawExpanding(object):
     """Decode mu-law encoded signal.  For more info see the
     `Wikipedia Entry <https://en.wikipedia.org/wiki/%CE%9C-law_algorithm>`_

tox.ini

+[flake8]
+max-line-length = 120
+ignore = E305,E402,E721,E741,F401,F403,F405,F821,F841,F999
+exclude = docs/src,venv,torch/lib/gloo,torch/lib/pybind11,torch/lib/nanopb