Address reviews

Author: mthrok

docs/source/backend.rst

@@ -20,7 +20,7 @@ There are currently three implementations available.
 
 ``sox`` backend is the original backend which is built on ``libsox``. This module is currently default but is known to have number of issues, such as wrong handling of WAV files other than 16-bit signed integer. Users are encouraged to use ``sox_io`` backend. This backend requires C++ extension module and is not available on Windows system.
 
-``sox_io`` backend is the new backend which is built on ``libsox`` and bound to Python with ``Torchscript``. This module is thoroughly tested and addresses all the known issues ``sox`` backend has. Function calls to this backend can be Torchscriptable. This backend requires C++ extension module and is not available on Windows system.
+``sox_io`` backend is the new backend which is built on ``libsox`` and bound to Python with ``Torchscript``. This module addresses all the known issues ``sox`` backend has. Function calls to this backend can be Torchscriptable. This backend requires C++ extension module and is not available on Windows system.
 
 ``soundfile`` backend is built on ``PySoundFile``. You need to install ``PySoundFile`` separately.
 

torchaudio/sox_effects/sox_effects.py

@@ -142,16 +142,16 @@ def apply_effects_tensor(
         ...     ["lowpass", "-1", "300"],  # apply single-pole lowpass filter
         ...     ["rate", "8000"],  # change sample rate to 8000
         ... ]
-        >>> trans = SoxEffectTensorTransform(effects, input_sample_rate)
+        >>> transform = SoxEffectTensorTransform(effects, input_sample_rate)
         >>>
         >>> # Dump it to file and load
         >>> path = 'sox_effect.zip'
         >>> torch.jit.script(trans).save(path)
-        >>> trans = torch.jit.load(path)
+        >>> transform = torch.jit.load(path)
         >>>
         >>>> # Run transform
         >>> waveform, input_sample_rate = torchaudio.load("input.wav")
-        >>> waveform, sample_rate = trans(waveform, input_sample_rate)
+        >>> waveform, sample_rate = transform(waveform, input_sample_rate)
         >>> assert sample_rate == 8000
     """
     in_signal = torch.classes.torchaudio.TensorSignal(tensor, sample_rate, channels_first)