Merge pull request mozilla#3614 from lissyx/remove-tc-docs

Fix mozilla#3607: Remove doc refs to TC

doc/BUILDING.rst

@@ -16,10 +16,12 @@ It is required to use our fork of TensorFlow since it includes fixes for common
 
 If you'd like to build the language bindings or the decoder package, you'll also need:
 
+.. _swig-dep:
 
-* `SWIG >= 3.0.12 <http://www.swig.org/>`_.
-  Unfortunately, NodeJS / ElectronJS after 10.x support on SWIG is a bit behind, and while there are pending patches proposed to upstream, it is not yet merged.
+* `SWIG >= 4.0 <http://www.swig.org/>`_.
+  Unfortunately, NodeJS / ElectronJS after 10.x support on SWIG is a bit behind, but patches have been merged and 4.1 is good.
   The proper prebuilt patched version (covering linux, windows and macOS) of SWIG should get installed under `native_client/ <native_client/>`_ as soon as you build any bindings that requires it.
+  Prebuilt versions for linux, macOS and Windows are `available (look for ds-swig*.tar.gz) <https://github.com/mozilla/DeepSpeech/releases/tag/v0.9.3>`_
 
 * `node-pre-gyp <https://github.com/mapbox/node-pre-gyp>`_ (for Node.JS bindings only)
 
@@ -141,7 +143,7 @@ This will create the package ``deepspeech-VERSION.tgz`` in ``native_client/javas
 Install the CTC decoder package
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-To build the ``ds_ctcdecoder`` package, you'll need the general requirements listed above (in particular SWIG). The command below builds the bindings using eight (8) processes for compilation. Adjust the parameter accordingly for more or less parallelism.
+To build the ``ds_ctcdecoder`` package, you'll need the general requirements listed above (in particular :ref:`SWIG <swig-dep>`). The command below builds the bindings using eight (8) processes for compilation. Adjust the parameter accordingly for more or less parallelism.
 
 .. code-block::
 
@@ -159,9 +161,7 @@ architectures, and you might find some help in our `discourse <https://discourse
 
 Feedback on improving this section or usage on other architectures is welcome.
 
-First, you need to build SWIG from scratch.
-Since `SWIG >= 3.0.12 <http://www.swig.org/>`_ does not include our patches please use
-https://github.com/lissyx/swig/tree/taskcluster for building SWIG from source.
+First, you need to build SWIG from scratch. See :ref:`SWIG dep <swig-dep>` for details.
 
 You can supply your prebuild SWIG using ``SWIG_DIST_URL``
 
@@ -357,4 +357,4 @@ Feedback on improving this is welcome: how it could be exposed in the API, how
 much performance gains do you get in your applications, how you had to change
 the model to make it work with a delegate, etc.
 
-See :ref:`the support / contact details <support>`
+See :ref:`the support / contact details <support>`

doc/TRAINING.rst

@@ -241,11 +241,9 @@ Making a mmap-able model for inference
 The ``output_graph.pb`` model file generated in the above step will be loaded in memory to be dealt with when running inference.
 This will result in extra loading time and memory consumption. One way to avoid this is to directly read data from the disk.
 
-TensorFlow has tooling to achieve this: it requires building the target ``//tensorflow/contrib/util:convert_graphdef_memmapped_format`` (binaries are produced by our TaskCluster for some systems including Linux/amd64 and macOS/amd64), use ``util/taskcluster.py`` tool to download:
+TensorFlow has tooling to achieve this: it requires building the target ``//tensorflow/contrib/util:convert_graphdef_memmapped_format``. We recommend you build it from `TensorFlow r1.15 <https://github.com/tensorflow/tensorflow/tree/r1.15/>`_.
 
-.. code-block::
-
-   $ python3 util/taskcluster.py --source tensorflow --artifact convert_graphdef_memmapped_format --branch r1.15 --target .
+For convenience, builds for Linux and macOS are `available (look for file named convert_graphdef_memmapped_format) <https://github.com/mozilla/DeepSpeech/releases/tag/v0.9.3>`_
 
 Producing a mmap-able model is as simple as:
 

doc/USING.rst

@@ -174,27 +174,7 @@ See the :ref:`TypeScript client <js-api-example>` for an example of how to use t
 Using the command-line client
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-To download the pre-built binaries for the ``deepspeech`` command-line (compiled C++) client, use ``util/taskcluster.py``\ :
-
-.. code-block:: bash
-
-   python3 util/taskcluster.py --target .
-
-or if you're on macOS:
-
-.. code-block:: bash
-
-   python3 util/taskcluster.py --arch osx --target .
-
-also, if you need some binaries different than current master, like ``v0.2.0-alpha.6``\ , you can use ``--branch``\ :
-
-.. code-block:: bash
-
-   python3 util/taskcluster.py --branch "v0.2.0-alpha.6" --target "."
-
-The script ``taskcluster.py`` will download ``native_client.tar.xz`` (which includes the ``deepspeech`` binary and associated libraries) and extract it into the current folder. Also, ``taskcluster.py`` will download binaries for Linux/x86_64 by default, but you can override that behavior with the ``--arch`` parameter. See the help info with ``python util/taskcluster.py -h`` for more details. Specific branches of DeepSpeech or TensorFlow can be specified as well.
-
-Alternatively you may manually download the ``native_client.tar.xz`` from the [releases](https://github.com/mozilla/DeepSpeech/releases).
+To download the pre-built binaries for the ``deepspeech`` command-line (compiled C++) client, use one of the ``native_client.tar.xz`` files from the [releases](https://github.com/mozilla/DeepSpeech/releases).
 
 Note: the following command assumes you `downloaded the pre-trained model <#getting-the-pre-trained-model>`_.