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Merged
merged 3 commits into from
Oct 25, 2020
Merged

Updating 1.7 branch #1205

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ded4e8d
Update event tracking (#1188)
patmellon Oct 22, 2020
e96ab78
Update beginner_source/audio_preprocessing_tutorial.py (#1199)
vincentqb Oct 23, 2020
33d9583
Update audio_preprocessing_tutorial.py (#1202)
brianjo Oct 23, 2020
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5 changes: 5 additions & 0 deletions _templates/layout.html
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Original file line number Diff line number Diff line change
Expand Up @@ -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(){
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20 changes: 15 additions & 5 deletions beginner_source/audio_preprocessing_tutorial.py
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@@ -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
Expand All @@ -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

######################################################################
Expand All @@ -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()))
Expand Down Expand Up @@ -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
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