add docstrings for save and load

torchaudio/__init__.py

@@ -23,6 +23,26 @@ def check_input(src):
         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
+
+    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
+       - int: the sample-rate of the audio (as listed in the metadata of the file)
+
+    Example::
+
+        >>> data, sample_rate = torchaudio.load('foo.mp3')
+        >>> print(data.size())
+        torch.Size([278756, 2])
+        >>> print(sample_rate)
+        44100
+        
+    """
     # check if valid file
     if not os.path.isfile(filepath):
         raise OSError("{} not found or is a directory".format(filepath))
@@ -45,6 +65,19 @@ def load(filepath, out=None, normalization=None):
     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
+        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))
     if not os.path.isdir(abs_dirpath):