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Oct 23, 2020
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Update beginner_source/audio_preprocessing_tutorial.py #1199

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38e9e34
Typo in beginner_source/audio_preprocessing_tutorial.py
creduo Mar 11, 2020
c2877f3
update title.
vincentqb Oct 22, 2020
70e0c9d
fix file access.
vincentqb Oct 22, 2020
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18 changes: 13 additions & 5 deletions beginner_source/audio_preprocessing_tutorial.py
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Original file line number Diff line number Diff line change
@@ -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 @@ -10,7 +10,8 @@
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.
Expand All @@ -19,6 +20,7 @@

import torch
import torchaudio
import requests
import matplotlib.pyplot as plt

######################################################################
Expand All @@ -29,7 +31,13 @@
# 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 +215,7 @@ 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
Expand Down