************
Installation
************

.. important::

   Kaldi and MFA are now built on :xref:`conda_forge` :fas:`party-horn`, so installation of third party binaries is wholly through conda from 2.0.0b4 onwards. Installing MFA via conda will pick up Kaldi as well.


All platforms
=============

1. Install :xref:`miniconda`/:xref:`conda_installation`
2. Create new environment and install MFA: :code:`conda create -n aligner -c conda-forge montreal-forced-aligner`

   a.  You can enable the :code:`conda-forge` channel by default by running :code:`conda config --add channels conda-forge` in order to omit the :code:`-c conda-forge` from these commands

3. Ensure you're in the new environment created (:code:`conda activate aligner`)

Installing SpeechBrain
----------------------

1. Ensure you are in the conda environment created above
2. Install PyTorch
   a. CPU: :code:`conda install pytorch torchvision torchaudio cpuonly -c pytorch`
   b. GPU: :code:`conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia`
3. Install Speechbrain via pip: :code:`pip install speechbrain`

Upgrading from non-conda version
================================

In general, it's recommend to create a new environment.  If you want to update,

1. Activate your conda environment (i.e., :code:`conda activate aligner`)
2. Upgrade all packages via :code:`conda update --all`
3. Run :code:`pip uninstall montreal-forced-aligner` (to clean up previous pip installation)
4. Run :code:`conda install -c conda-forge montreal-forced-aligner`

Installing from source
======================

If the Conda installation above does not work or the binaries don't work on your system, you can try building Kaldi and OpenFst from source, along with MFA.

1. Download/clone the :xref:`kaldi_github` and follow the installation instructions
2. If you're on Mac or Linux and want G2P functionality, install :xref:`openfst`, :xref:`opengrm_ngram`, :xref:`baumwelch`, and :xref:`pynini`
3. Make sure all Kaldi and other third party executables are on the system path
4. Download/clone the :xref:`mfa_github` and install MFA via :code:`python setup install` or :code:`pip install -e .`
5. Double check everything's working on the console with :code:`mfa -h`

.. note::

   You can also clone the conda-forge feedstocks for `OpenFst <https://github.com/conda-forge/openfst-feedstock>`_, `SoX <https://github.com/conda-forge/sox-feedstock>`_, `Kaldi <https://github.com/conda-forge/kaldi-feedstock>`_, and `MFA <https://github.com/conda-forge/montreal-forced-aligner-feedstock>`_ and run them with `conda build <https://docs.conda.io/projects/conda-build/en/latest/>`_ to build for your specific system.

Installing via pip
------------------

To install with pip and install minimal dependencies from conda:

1. Create a conda environment:

   * :fa:`fab fa-linux` Linux/:fa:`fab fa-apple` MacOSX: ``conda create -n aligner kaldi pynini``
   * :fa:`fab fa-windows` Windows: ``conda create -n aligner kaldi``

2. Activate environment via ``conda activate aligner``
3. Install MFA

   * From PyPi: ``pip install montreal-forced-aligner``
   * From :fa:`fab fa-github` GitHub: ``pip install git+https://github.com/MontrealCorpusTools/Montreal-Forced-Aligner.git``
   * From inside the MFA repository root directory, you can install a local version via:

     * ``pip install -e .``
     * ``python setup.py install``
     * ``python setup.py develop``

MFA temporary files
===================

MFA uses a temporary directory for commands that can be specified in running commands with ``--temp_directory`` (or see :ref:`configuration`), and it also uses a directory to store global configuration settings and saved models.  By default this root directory is ``~/Documents/MFA``, but if you would like to put this somewhere else, you can set the environment variable ``MFA_ROOT_DIR`` to use that.  MFA will raise an error on load if it's unable to write the specified root directory.

Supported functionality
=======================

As of version 2.0.6, all features are available on all platforms.  Prior to this version, G2P and language model training was unavailable on native Windows, but could be used with Windows Subsystem for Linux (WSL).

.. list-table::
   :header-rows: 1
   :stub-columns: 1

   * - Feature
     - Linux support
     - Windows support
     - MacOS support

   * - Alignment
     - .. raw:: html

          <span class='rst-table-cell supported'>Yes</span>
     - .. raw:: html

          <span class='rst-table-cell supported'>Yes</span>
     - .. raw:: html

          <span class='rst-table-cell supported'>Yes</span>

   * - G2P training
     - .. raw:: html

          <span class='rst-table-cell supported'>Yes</span>
     - .. raw:: html

          <span class='rst-table-cell supported'>Yes</span>
     - .. raw:: html

          <span class='rst-table-cell supported'>Yes</span>

   * - G2P generation
     - .. raw:: html

          <span class='rst-table-cell supported'>Yes</span>
     - .. raw:: html

          <span class='rst-table-cell supported'>Yes</span>
     - .. raw:: html

          <span class='rst-table-cell supported'>Yes</span>

   * - Transcription
     - .. raw:: html

          <span class='rst-table-cell supported'>Yes</span>
     - .. raw:: html

          <span class='rst-table-cell supported'>Yes</span>
     - .. raw:: html

          <span class='rst-table-cell supported'>Yes</span>

   * - Training language model
     - .. raw:: html

          <span class='rst-table-cell supported'>Yes</span>
     - .. raw:: html

          <span class='rst-table-cell supported'>Yes</span>
     - .. raw:: html

          <span class='rst-table-cell supported'>Yes</span>