update save/load

.gitignore _ext/

.gitignore

@@ -3,8 +3,9 @@ __pycache__/
 *.py[cod]
 *$py.class
 
-# C extensions
+# C extensions / folders
 *.so
+_ext/
 
 # Distribution / packaging
 .Python

test/test.py

-
-import torch
-import torch.nn as nn
+import unittest
 import torchaudio
+import os
+
+class Test_LoadSave(unittest.TestCase):
+    test_dirpath = os.path.dirname(os.path.realpath(__file__))
+    test_filepath = os.path.join(test_dirpath, "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))
+
+        # check normalizing
+        x, sr = torchaudio.load(self.test_filepath, normalization=True)
+        self.assertTrue(x.min() >= -1.0)
+        self.assertTrue(x.max() <= 1.0)
+
+        # check raising errors
+        with self.assertRaises(OSError):
+            torchaudio.load("file-does-not-exist.mp3")
+
+        with self.assertRaises(OSError):
+            tdir = os.path.join(os.path.dirname(self.test_dirpath), "torchaudio")
+            torchaudio.load(tdir)
+
+    def test_save(self):
+        # load signal
+        x, sr = torchaudio.load(self.test_filepath)
+
+        # check save
+        new_filepath = os.path.join(self.test_dirpath, "test.wav")
+        torchaudio.save(new_filepath, x, sr)
+        self.assertTrue(os.path.isfile(new_filepath))
+        os.unlink(new_filepath)
+
+        # check automatic normalization
+        x /= 1 << 31
+        torchaudio.save(new_filepath, x, sr)
+        self.assertTrue(os.path.isfile(new_filepath))
+        os.unlink(new_filepath)
+
+        # test save 1d tensor
+        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
+            torchaudio.save(new_filepath, x, sr)
+
+        with self.assertRaises(ValueError):
+            x.squeeze_()
+            x.unsqueeze_(1)
+            x.unsqueeze_(0) # 1 x L x 1
+            torchaudio.save(new_filepath, x, sr)
+
+        # automatically convert sr from floating point to int
+        x.squeeze_(0)
+        torchaudio.save(new_filepath, x, float(sr))
+        self.assertTrue(os.path.isfile(new_filepath))
+        os.unlink(new_filepath)
+
+        # don't allow uneven integers
+        with self.assertRaises(TypeError):
+            torchaudio.save(new_filepath, x, float(sr) + 0.5)
+            self.assertTrue(os.path.isfile(new_filepath))
+            os.unlink(new_filepath)
+
+        # 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")
+            torchaudio.save(new_filepath, x, sr)
 
-x, sample_rate = torchaudio.load("steam-train-whistle-daniel_simon.mp3")
-print(sample_rate)
-print(x.size())
-print(x[10000])
-print(x.min(), x.max())
-print(x.mean(), x.std())
-
-x, sample_rate = torchaudio.load("steam-train-whistle-daniel_simon.mp3",
-                                 out=torch.LongTensor())
-print(sample_rate)
-print(x.size())
-print(x[10000])
-print(x.min(), x.max())
+if __name__ == '__main__':
+    unittest.main()

torchaudio/__init__.py

@@ -14,27 +14,52 @@ def check_input(src):
     if not src.__module__ == 'torch':
         raise TypeError('Expected a CPU based tensor, got %s' % type(src))
 
-
-def load(filename, out=None):
+def load(filepath, out=None, normalization=None):
+    # check if valid file
+    if not os.path.isfile(filepath):
+        raise OSError("{} not found or is a directory".format(filepath))
+    # initialize output tensor
     if out is not None:
         check_input(out)
     else:
         out = torch.FloatTensor()
+    # load audio signal
     typename = type(out).__name__.replace('Tensor', '')
     func = getattr(th_sox, 'libthsox_{}_read_audio_file'.format(typename))
     sample_rate_p = ffi.new('int*')
-    func(str(filename).encode("ascii"), out, sample_rate_p)
+    func(str(filepath).encode("utf-8"), out, sample_rate_p)
     sample_rate = sample_rate_p[0]
+    # normalize if needed
+    if isinstance(normalization, bool) and normalization:
+        out /= 1 << 31 # assuming 16-bit depth
+    elif isinstance(normalization, (float, int)):
+        out /= normalization # normalize with custom value
     return out, sample_rate
 
-
 def save(filepath, src, sample_rate):
-    filename, extension = os.path.splitext(filepath)
-    if type(sample_rate) != int:
+    # check if save directory exists
+    abs_dirpath = os.path.dirname(os.path.abspath(filepath))
+    if not os.path.isdir(abs_dirpath):
+        raise OSError("Directory does not exist: {}".format(abs_dirpath))
+    # Check/Fix shape of source data
+    if len(src.size()) == 1:
+        # 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()))
+    # check if sample_rate is an integer
+    if not isinstance(sample_rate, int):
+        if int(sample_rate) == sample_rate:
+            sample_rate = int(sample_rate)
+        else:
             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.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, "ascii"), src, extension[1:], sample_rate)
+    func(bytes(filepath, "utf-8"), src, bytes(extension[1:], "utf-8"), sample_rate)