update tutorials path

Author: TParcollet

tutorial_basics.html

@@ -107,7 +107,7 @@ <h2>Brain Class</h2>
                                             SpeechBrain provides a convenient framework for organizing the training loop, in the form of a class known as the "Brain" class,
                                             implemented in speechbrain/core.py. In each recipe, we sub-class this class and override the methods for which the default
                                             implementation doesn't do what is required for that particular recipe.</p>
-                                          <a href="https://colab.research.google.com/drive/1cYIsQiKlXTyfGR3j4gKs5Rq648JDaqaB?usp=sharing#scrollTo=jr2jH1sDZcml" class="blog_btn">Open in Google Colab</a>
+                                          <a href="https://colab.research.google.com/drive/1fdqTk4CTXNcrcSVFvaOKzRfLmj4fJfwa?usp=sharing" class="blog_btn">Open in Google Colab</a>
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@@ -138,7 +138,7 @@ <h2>HyperPyYAML</h2>
                                             SpeechBrain-developed format, which we call "HyperPyYAML". We chose to extend YAML since it is a highly
                                             readable format for data serialization. By extending an already useful format, we were able to create an
                                             expanded definition of hyperparameter, keeping our actual experimental code small and highly readable.</p>
-                                          <a href="https://colab.research.google.com/drive/10jZah2QHZ7xuajv9M1yIwRQdePxPV97U" class="blog_btn">Open in Google Colab</a>
+                                          <a href="https://colab.research.google.com/drive/1Pg9by4b6-8QD2iC0U7Ic3Vxq4GEwEdDz?usp=sharing" class="blog_btn">Open in Google Colab</a>
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@@ -165,7 +165,7 @@ <h2>Data Loading Pipeline</h2>
                                             defining your torch.utils.data.Dataset class as well as different data sampling and augmentations strategies.
                                             In SpeechBrain we provide efficient abstractions to simplify this time-consuming process without sacrificing
                                             flexibility. In fact our data pipeline is built around the Pytorch one.</p>
-                                          <a href="https://colab.research.google.com/drive/1NEboTfb2EIBrc0nUd9NKwcG2eqf-kv3d?usp=sharing" class="blog_btn">Open in Google Colab</a>
+                                          <a href="https://colab.research.google.com/drive/1AiVJZhZKwEI4nFGANKXEe-ffZFfvXKwH?usp=sharing" class="blog_btn">Open in Google Colab</a>
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tutorial_processing.html

@@ -106,7 +106,7 @@ <h2>Speech Augmentation</h2>
                                               and we must cleverly use the available dataset. One popular technique is called speech augmentation. The idea is to artificially
                                               corrupt the original speech signals to give the network the "illusion" that we are processing a new signal. This acts as a powerful regularizer,
                                               that normally helps neural networks improving generalization and thus achieve better performance on test data.</p>
-                                            <a href="https://colab.research.google.com/drive/16mUGH_A3UHZZOx2Vn69hy6fkPAM8CnA4?usp=sharing" class="blog_btn">Open in Google Colab</a>
+                                            <a href="https://colab.research.google.com/drive/1JJc4tBhHNXRSDM2xbQ3Z0jdDQUw4S5lr?usp=sharing" class="blog_btn">Open in Google Colab</a>
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@@ -136,7 +136,7 @@ <h2>Fourier Transform and Spectrograms</h2>
                                               Fourier Transform, which turns the time-domain signal into an equivalent representation in the frequency domain.
                                               In the following sections, we will describe the Fourier transforms along with other related transformations such as
                                               Short-Term Fourier Transform (STFT) and spectrograms.</p>
-                                            <a href="https://colab.research.google.com/drive/1dCY2XHC6NiDA9mor3jd9H9w-XEcD3M4L?usp=sharing" class="blog_btn">Open in Google Colab</a>
+                                            <a href="https://colab.research.google.com/drive/18IgBv3Ip0rWXjYoZywttSmW7Y2AIK1vJ?usp=sharing" class="blog_btn">Open in Google Colab</a>
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@@ -162,7 +162,7 @@ <h2>Speech Features (MFCC, FBANK)</h2>
                                             <p>Speech is a very high-dimensional signal. For instance, when the sampling frequency is 16 kHz,
                                               we have 16000 samples for each second. Working with such very high dimensional data can be critical from a machine learning perspective.
                                               The goal of feature extraction is to find more compact ways to represent speech.</p>
-                                            <a href="https://colab.research.google.com/drive/1iH1Tvgrsi7nD_uxh5jEj7DAXaL4gdjze?usp=sharing" class="blog_btn">Open in Google Colab</a>
+                                            <a href="https://colab.research.google.com/drive/1CI72Xyay80mmmagfLaIIeRoDgswWHT_g?usp=sharing" class="blog_btn">Open in Google Colab</a>
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