Updating 1.7 branch (#1205)

* Update event tracking (#1188)

* Update beginner_source/audio_preprocessing_tutorial.py (#1199)

* Typo in beginner_source/audio_preprocessing_tutorial.py

Typo in beginner_source/audio_preprocessing_tutorial.py

fron > from

* update title.

* fix file access.

Co-authored-by: JuHyuk Park <creduo@gmail.com>

* Update audio_preprocessing_tutorial.py (#1202)

Adds a comment for running this tutorial in Google Colab.

Co-authored-by: Pat Mellon <16585245+patmellon@users.noreply.github.com>
Co-authored-by: Vincent QB <vincentqb@users.noreply.github.com>
Co-authored-by: JuHyuk Park <creduo@gmail.com>

Author: 4 people

_templates/layout.html

@@ -51,6 +51,11 @@
       eventAction: 'click',
       eventLabel: $(this).attr("data-response")
     });
+    gtag('event', 'click', {
+      'event_category': $(this).attr("data-response"),
+      'event_label': $("h1").first().text(),
+      'tutorial_link': window.location.href
+    });
    });
 
    $("[data-behavior='was-this-helpful-event']").on('click', function(){

beginner_source/audio_preprocessing_tutorial.py

@@ -1,6 +1,6 @@
 """
-torchaudio Tutorial
-===================
+Audio I/O and Pre-Processing with torchaudio
+============================================
 
 PyTorch is an open source deep learning platform that provides a
 seamless path from research prototyping to production deployment with
@@ -10,15 +10,19 @@
 preparation. ``torchaudio`` leverages PyTorch’s GPU support, and provides
 many tools to make data loading easy and more readable. In this
 tutorial, we will see how to load and preprocess data from a simple
-dataset.
+dataset. Please visit
+`Audio I/O and Pre-Processing with torchaudio <https://pytorch.org/tutorials/beginner/audio_preprocessing_tutorial.html>`__ to learn more.
 
 For this tutorial, please make sure the ``matplotlib`` package is
 installed for easier visualization.
 
 """
 
+# Uncomment the following line to run in Google Colab
+# !pip install torchaudio 
 import torch
 import torchaudio
+import requests
 import matplotlib.pyplot as plt
 
 ######################################################################
@@ -29,7 +33,13 @@
 # call waveform the resulting raw audio signal.
 # 
 
-filename = "../_static/img/steam-train-whistle-daniel_simon-converted-from-mp3.wav"
+url = "https://pytorch.org/tutorials/_static/img/steam-train-whistle-daniel_simon-converted-from-mp3.wav"
+r = requests.get(url)
+
+with open('steam-train-whistle-daniel_simon-converted-from-mp3.wav', 'wb') as f:
+    f.write(r.content)
+
+filename = "steam-train-whistle-daniel_simon-converted-from-mp3.wav"
 waveform, sample_rate = torchaudio.load(filename)
 
 print("Shape of waveform: {}".format(waveform.size()))
@@ -207,7 +217,7 @@ def normalize(tensor):
 plt.plot(mu_law_encoding_waveform[0,:].numpy())
 
 ######################################################################
-# You can see how the output fron ``torchaudio.functional.mu_law_encoding`` is the same as 
+# You can see how the output from ``torchaudio.functional.mu_law_encoding`` is the same as 
 # the output from ``torchaudio.transforms.MuLawEncoding``.
 #
 # Now let's experiment with a few of the other functionals and visualize their output. Taking our