DOC: update links to point to Github pages

CHANGELOG.rst

@@ -116,7 +116,7 @@ Version 0.2.0 (2019-06-28)
 Version 0.1.1 (2019-05-23)
 --------------------------
 
-* Fixed: Broken API documentation on RTD_
+* Fixed: Broken API documentation on readthedocs
 
 
 Version 0.1.0 (2019-05-22)
@@ -127,4 +127,3 @@ Version 0.1.0 (2019-05-22)
 
 .. _Keep a Changelog: https://keepachangelog.com/en/1.0.0/
 .. _Semantic Versioning: https://semver.org/spec/v2.0.0.html
-.. _RTD: https://audtorch.readthedocs.io/

CONTRIBUTING.rst

@@ -52,7 +52,7 @@ When creating a new pull request, please conside the following points:
 Writing Documentation
 ---------------------
 
-The API documentation of audtorch_ is build automatically from the
+The API documentation of :mod:`audtorch` is build automatically from the
 docstrings_ of its classes and functions.
 
 docstrings_ are written in reStructuredText_ as indicated by the ``r`` at
@@ -87,7 +87,6 @@ documentation under the ``docs/`` folder in the appropriate api file. E.g.
 ``energy()`` is part of the ``utils`` module and the corresponding file in the
 documentation would be ``docs/api-utils.rst``, where it is included.
 
-.. _audtorch: https://audtorch.readthedocs.io/
 .. _docstrings: https://www.python.org/dev/peps/pep-0257/
 .. _reStructuredText:
     http://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html

docs/install.rst

@@ -1,12 +1,11 @@
 Installation
 ============
 
-audtorch_ is supported by Python 3.6 or higher. To install it run
+:mod:`audtorch` is supported by Python 3.6 or higher. To install it run
 (preferable in a `virtual environment`_):
 
 .. code-block:: bash
 
     pip install audtorch
 
-.. _audtorch: https://audtorch.readthedocs.io
 .. _virtual environment: https://docs.python-guide.org/dev/virtualenvs

docs/tutorials/introduction.ipynb

@@ -30,7 +30,7 @@
     "## Data loading\n",
     "We are going to start with loading the necessary data.  \n",
     "\n",
-    "`audtorch` offers a growing [collection of data sets](https://audtorch.readthedocs.io/en/latest/api-datasets.html). Normally, using this interface requires one to have that particular data set on their hard drive. Some of them even support downloading from their original source.  \n",
+    "`audtorch` offers a growing [collection of data sets](https://audeering.github.io/audtorch/api-datasets.html). Normally, using this interface requires one to have that particular data set on their hard drive. Some of them even support downloading from their original source.  \n",
     "\n",
     "We will be using the Berlin Database of Emotional Speech (EmoDB) for this tutorial. For convenience, we have included two of its files in a sub-directory. We recommend you to get the whole data base from its [original website ](http://www.emodb.bilderbar.info/navi.html).  "
    ]
@@ -95,7 +95,7 @@
     "## Feature extraction\n",
     "Feature extraction is the first important benefit of using `audtorch`.  \n",
     "\n",
-    "`audtorch` collects an ever growing set of [feature transformation and data pre-processing utilities](https://audtorch.readthedocs.io/en/latest/api-transforms.html#). That way you don't need to worry too much about getting your data pipeline ready, but you can quickly start with the cool modelling part.  \n",
+    "`audtorch` collects an ever growing set of [feature transformation and data pre-processing utilities](https://audeering.github.io/audtorch/api-transforms.html#). That way you don't need to worry too much about getting your data pipeline ready, but you can quickly start with the cool modelling part.  \n",
     "\n",
     "A typical kind of features used in the audio domain, are spectral features. Audio signals are analyzed with respect to their frequency content using something called a [Fourier transform](https://en.wikipedia.org/wiki/Fourier_transform).  \n",
     "\n",
@@ -183,7 +183,7 @@
     "\n",
     "In general, we recommend to always start with a preliminary data analysis before you jump into modelling to ensure you have the proper understanding of your problem.  \n",
     "\n",
-    "`audtorch` is here to help you with that, and another useful feature is that it allows you to stack multiple transforms in a [Compose transform](https://audtorch.readthedocs.io/en/latest/api-transforms.html#audtorch.transforms.Compose). Below, we stack together the spectrogram and the log transforms to form a single object."
+    "`audtorch` is here to help you with that, and another useful feature is that it allows you to stack multiple transforms in a [Compose transform](https://audeering.github.io/audtorch/api-transforms.html#audtorch.transforms.Compose). Below, we stack together the spectrogram and the log transforms to form a single object."
    ]
   },
   {
@@ -218,9 +218,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This stacking can continue *ad infinum*, as seen below with the [Standardize transform](https://audtorch.readthedocs.io/en/latest/api-transforms.html#standardize).  \n",
+    "This stacking can continue *ad infinum*, as seen below with the [Standardize transform](https://audeering.github.io/audtorch/api-transforms.html#standardize).  \n",
     "\n",
-    "Make sure to always stay up to date with [all the transforms offered by audtorch](https://audtorch.readthedocs.io/en/latest/api-transforms.html)!"
+    "Make sure to always stay up to date with [all the transforms offered by audtorch](https://audeering.github.io/audtorch/api-transforms.html)!"
    ]
   },
   {
@@ -274,9 +274,9 @@
     "* Pitch shifting\n",
     "* Etc.\n",
     "\n",
-    "An important feature of `audtorch` is making these transformations very easy to use in practice. In the following example, we will be using [RandomAdditiveMix](https://audtorch.readthedocs.io/en/latest/api-transforms.html#randomadditivemix). This transforms allows you to randomly mix audio samples with a noise data set of your choice (e.g. a large audio data set like [AudioSet](https://audtorch.readthedocs.io/en/latest/api-datasets.html#audioset)).  \n",
+    "An important feature of `audtorch` is making these transformations very easy to use in practice. In the following example, we will be using [RandomAdditiveMix](https://audeering.github.io/audtorch/api-transforms.html#randomadditivemix). This transforms allows you to randomly mix audio samples with a noise data set of your choice (e.g. a large audio data set like [AudioSet](https://audeering.github.io/audtorch/api-datasets.html#audioset)).  \n",
     "\n",
-    "In this example, we will use a built-in data set, [WhiteNoise](https://audtorch.readthedocs.io/en/latest/api-datasets.html#whitenoise), which simply creates a random white noise signal every time it is called."
+    "In this example, we will use a built-in data set, [WhiteNoise](https://audeering.github.io/audtorch/api-datasets.html#whitenoise), which simply creates a random white noise signal every time it is called."
    ]
   },
   {

docs/usage.rst

@@ -1,7 +1,7 @@
 Usage
 =====
 
-audtorch_ automates the data iteration process for deep neural
+:mod:`audtorch` automates the data iteration process for deep neural
 network training using PyTorch_. It provides a set of feature extraction
 transforms that can be implemented on-the-fly on the CPU.
 
@@ -25,5 +25,4 @@ Besides data sets and transforms the package provides standard evaluation
 metrics, samplers, and necessary collate functions for training deep neural
 networks for audio tasks.
 
-.. _audtorch: https://audtorch.readthedocs.io
 .. _PyTorch: https://pytorch.org