mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-13 20:12:24 +00:00
WIP: Add documentation.
This commit is contained in:
Fangjun Kuang
parent
16a420ec8e
commit
5f1de523c4
1
docs/requirements.txt
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docs/requirements.txt
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sphinx_rtd_theme
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BIN
docs/source/_static/logo.png
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docs/source/_static/logo.png
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@ -14,15 +14,17 @@
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# import sys
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# sys.path.insert(0, os.path.abspath('.'))
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import sphinx_rtd_theme
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# -- Project information -----------------------------------------------------
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project = 'icefall'
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copyright = '2021, icefall development team'
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author = 'icefall development team'
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project = "icefall"
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copyright = "2021, icefall development team"
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author = "icefall development team"
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# The full version, including alpha/beta/rc tags
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release = '0.1'
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release = "0.1"
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# -- General configuration ---------------------------------------------------
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@ -31,25 +33,45 @@ release = '0.1'
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# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
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# ones.
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extensions = [
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"sphinx_rtd_theme",
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]
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# Add any paths that contain templates here, relative to this directory.
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templates_path = ['_templates']
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templates_path = ["_templates"]
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# List of patterns, relative to source directory, that match files and
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# directories to ignore when looking for source files.
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# This pattern also affects html_static_path and html_extra_path.
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exclude_patterns = []
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source_suffix = {
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".rst": "restructuredtext",
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}
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master_doc = "index"
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# -- Options for HTML output -------------------------------------------------
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# The theme to use for HTML and HTML Help pages. See the documentation for
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# a list of builtin themes.
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#
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html_theme = 'alabaster'
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html_theme = "sphinx_rtd_theme"
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html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
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html_show_sourcelink = True
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# Add any paths that contain custom static files (such as style sheets) here,
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# relative to this directory. They are copied after the builtin static files,
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# so a file named "default.css" will overwrite the builtin "default.css".
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html_static_path = ['_static']
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html_static_path = ["_static", "installation/images"]
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pygments_style = "sphinx"
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numfig = True
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html_context = {
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"display_github": True,
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"github_user": "k2-fsa",
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"github_repo": "icefall",
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"github_version": "master",
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"conf_py_path": "/icefall/docs/source/",
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}
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@ -3,18 +3,21 @@
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You can adapt this file completely to your liking, but it should at least
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contain the root `toctree` directive.
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Welcome to icefall's documentation!
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===================================
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.. image:: _static/logo.png
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:alt: icefall logo
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:width: 100px
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:align: center
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:target: https://github.com/k2-fsa/icefall
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icefall
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=======
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Documentation for `icefall <https://github.com/k2-fsa/icefall>`, containing
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speech recognition recipes using `k2 <https://github.com/k2-fsa/k2>`_.
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.. toctree::
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:maxdepth: 2
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:caption: Contents:
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Indices and tables
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==================
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* :ref:`genindex`
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* :ref:`modindex`
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* :ref:`search`
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installation/index
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recipes/index
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<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="122" height="20" role="img" aria-label="device: CPU | CUDA"><title>device: CPU | CUDA</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="122" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="45" height="20" fill="#555"/><rect x="45" width="77" height="20" fill="#fe7d37"/><rect width="122" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="235" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="350">device</text><text x="235" y="140" transform="scale(.1)" fill="#fff" textLength="350">device</text><text aria-hidden="true" x="825" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="670">CPU | CUDA</text><text x="825" y="140" transform="scale(.1)" fill="#fff" textLength="670">CPU | CUDA</text></g></svg>
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<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="114" height="20" role="img" aria-label="os: Linux | macOS"><title>os: Linux | macOS</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="114" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="23" height="20" fill="#555"/><rect x="23" width="91" height="20" fill="#ff69b4"/><rect width="114" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="125" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="130">os</text><text x="125" y="140" transform="scale(.1)" fill="#fff" textLength="130">os</text><text aria-hidden="true" x="675" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="810">Linux | macOS</text><text x="675" y="140" transform="scale(.1)" fill="#fff" textLength="810">Linux | macOS</text></g></svg>
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<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="170" height="20" role="img" aria-label="python: 3.6 | 3.7 | 3.8 | 3.9"><title>python: 3.6 | 3.7 | 3.8 | 3.9</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="170" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="49" height="20" fill="#555"/><rect x="49" width="121" height="20" fill="#007ec6"/><rect width="170" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="255" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="390">python</text><text x="255" y="140" transform="scale(.1)" fill="#fff" textLength="390">python</text><text aria-hidden="true" x="1085" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="1110">3.6 | 3.7 | 3.8 | 3.9</text><text x="1085" y="140" transform="scale(.1)" fill="#fff" textLength="1110">3.6 | 3.7 | 3.8 | 3.9</text></g></svg>
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<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="286" height="20" role="img" aria-label="torch: 1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0"><title>torch: 1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="286" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="39" height="20" fill="#555"/><rect x="39" width="247" height="20" fill="#97ca00"/><rect width="286" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="205" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="290">torch</text><text x="205" y="140" transform="scale(.1)" fill="#fff" textLength="290">torch</text><text aria-hidden="true" x="1615" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="2370">1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0</text><text x="1615" y="140" transform="scale(.1)" fill="#fff" textLength="2370">1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0</text></g></svg>
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After Width: | Height: | Size: 1.3 KiB |
469
docs/source/installation/index.rst
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docs/source/installation/index.rst
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.. _install icefall:
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Installation
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============
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- |os|
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- |device|
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- |python_versions|
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- |torch_versions|
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.. |os| image:: ./images/os-Linux_macOS-ff69b4.svg
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:alt: Supported operating systems
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.. |device| image:: ./images/device-CPU_CUDA-orange.svg
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:alt: Supported devices
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.. |python_versions| image:: ./images/python-3.6_3.7_3.8_3.9-blue.svg
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:alt: Supported python versions
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.. |torch_versions| image:: ./images/torch-1.6.0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg
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:alt: Supported PyTorch versions
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icefall depends on `k2 <https://github.com/k2-fsa/k2>`_ and
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`lhotse <https://github.com/lhotse-speech/lhotse>`_.
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We recommend you to install ``k2`` first, as ``k2`` is bound to
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a specific version of PyTorch after compilation. Install ``k2`` also
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installs its dependency PyTorch, which can be reused by ``lhotse``.
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(1) Install k2
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--------------
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Please refer to `<https://k2.readthedocs.io/en/latest/installation/index.html>`_
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to install `k2`.
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.. HINT::
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If you have already installed PyTorch and don't want to replace it,
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please install a version of k2 that is compiled against the version
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of PyTorch you are using.
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(2) Install lhotse
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------------------
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Please refer to `<https://lhotse.readthedocs.io/en/latest/getting-started.html#installation>`_
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to install ``lhotse``.
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.. HINT::
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Install ``lhotse`` also installs its dependency `torchaudio <https://github.com/pytorch/audio>`_.
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(3) Download icefall
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--------------------
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icefall is a collection of Python scripts, so you don't need to install it
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and we don't provide a ``setup.py`` to install it.
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What you need is to download it and set the environment variable ``PYTHONPATH``
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to point to it.
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Assume you want to place ``icefall`` in the folder ``/tmp``. The
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following commands show you how to setup ``icefall``:
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.. code-block:: bash
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cd /tmp
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git clone https://github.com/k2-fsa/icefall
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cd icefall
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pip install -r requirements.txt
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export PYTHONPATH=/tmp/icefall:$PYTHONPATH
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.. HINT::
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You can put several versions of ``icefall`` in the same virtual environment.
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To switch among different versions of ``icefall``, just set ``PYTHONPATH``
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to point to the version you want.
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Installation example
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--------------------
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The following shows an example about setting up the environment.
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(1) Create a virtual environment
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. code-block:: bash
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$ virtualenv -p python3.8 test-icefall
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created virtual environment CPython3.8.6.final.0-64 in 1540ms
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creator CPython3Posix(dest=/ceph-fj/fangjun/test-icefall, clear=False, no_vcs_ignore=False, global=False)
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seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/fangjun/.local/share/v
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irtualenv)
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added seed packages: pip==21.1.3, setuptools==57.4.0, wheel==0.36.2
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activators BashActivator,CShellActivator,FishActivator,PowerShellActivator,PythonActivator,XonshActivator
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(2) Activate your virtual environment
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. code-block:: bash
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$ source test-icefall/bin/activate
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(3) Install k2
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~~~~~~~~~~~~~~
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.. code-block:: bash
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$ pip install k2==1.4.dev20210822+cpu.torch1.9.0 -f https://k2-fsa.org/nightly/index.html
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Looking in links: https://k2-fsa.org/nightly/index.html
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Collecting k2==1.4.dev20210822+cpu.torch1.9.0
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Downloading https://k2-fsa.org/nightly/whl/k2-1.4.dev20210822%2Bcpu.torch1.9.0-cp38-cp38-linux_x86_64.whl (1.6 MB)
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|________________________________| 1.6 MB 185 kB/s
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Collecting graphviz
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Downloading graphviz-0.17-py3-none-any.whl (18 kB)
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Collecting torch==1.9.0
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Using cached torch-1.9.0-cp38-cp38-manylinux1_x86_64.whl (831.4 MB)
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Collecting typing-extensions
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Using cached typing_extensions-3.10.0.0-py3-none-any.whl (26 kB)
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Installing collected packages: typing-extensions, torch, graphviz, k2
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Successfully installed graphviz-0.17 k2-1.4.dev20210822+cpu.torch1.9.0 torch-1.9.0 typing-extensions-3.10.0.0
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.. WARNING::
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We choose to install a CPU version of k2 for testing. You would probably want to install
|
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a CUDA version of k2.
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(4) Install lhotse
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~~~~~~~~~~~~~~~~~~
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.. code-block::
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$ pip install git+https://github.com/lhotse-speech/lhotse
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Collecting git+https://github.com/lhotse-speech/lhotse
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Cloning https://github.com/lhotse-speech/lhotse to /tmp/pip-req-build-7b1b76ge
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Running command git clone -q https://github.com/lhotse-speech/lhotse /tmp/pip-req-build-7b1b76ge
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Collecting audioread>=2.1.9
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Using cached audioread-2.1.9-py3-none-any.whl
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Collecting SoundFile>=0.10
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Using cached SoundFile-0.10.3.post1-py2.py3-none-any.whl (21 kB)
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Collecting click>=7.1.1
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Using cached click-8.0.1-py3-none-any.whl (97 kB)
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Collecting cytoolz>=0.10.1
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Using cached cytoolz-0.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.9 MB)
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Collecting dataclasses
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Using cached dataclasses-0.6-py3-none-any.whl (14 kB)
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Collecting h5py>=2.10.0
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Downloading h5py-3.4.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (4.5 MB)
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|________________________________| 4.5 MB 684 kB/s
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Collecting intervaltree>=3.1.0
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Using cached intervaltree-3.1.0-py2.py3-none-any.whl
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Collecting lilcom>=1.1.0
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Using cached lilcom-1.1.1-cp38-cp38-linux_x86_64.whl
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Collecting numpy>=1.18.1
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Using cached numpy-1.21.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.8 MB)
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Collecting packaging
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Using cached packaging-21.0-py3-none-any.whl (40 kB)
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Collecting pyyaml>=5.3.1
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Using cached PyYAML-5.4.1-cp38-cp38-manylinux1_x86_64.whl (662 kB)
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Collecting tqdm
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Downloading tqdm-4.62.1-py2.py3-none-any.whl (76 kB)
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|________________________________| 76 kB 2.7 MB/s
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Collecting torchaudio==0.9.0
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Downloading torchaudio-0.9.0-cp38-cp38-manylinux1_x86_64.whl (1.9 MB)
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|________________________________| 1.9 MB 73.1 MB/s
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Requirement already satisfied: torch==1.9.0 in ./test-icefall/lib/python3.8/site-packages (from torchaudio==0.9.0->lhotse===0.8.0.dev
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-2a1410b-clean) (1.9.0)
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Requirement already satisfied: typing-extensions in ./test-icefall/lib/python3.8/site-packages (from torch==1.9.0->torchaudio==0.9.0-
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>lhotse===0.8.0.dev-2a1410b-clean) (3.10.0.0)
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Collecting toolz>=0.8.0
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Using cached toolz-0.11.1-py3-none-any.whl (55 kB)
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Collecting sortedcontainers<3.0,>=2.0
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Using cached sortedcontainers-2.4.0-py2.py3-none-any.whl (29 kB)
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Collecting cffi>=1.0
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Using cached cffi-1.14.6-cp38-cp38-manylinux1_x86_64.whl (411 kB)
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Collecting pycparser
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Using cached pycparser-2.20-py2.py3-none-any.whl (112 kB)
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Collecting pyparsing>=2.0.2
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Using cached pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)
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Building wheels for collected packages: lhotse
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Building wheel for lhotse (setup.py) ... done
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Created wheel for lhotse: filename=lhotse-0.8.0.dev_2a1410b_clean-py3-none-any.whl size=342242 sha256=f683444afa4dc0881133206b4646a
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9d0f774224cc84000f55d0a67f6e4a37997
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Stored in directory: /tmp/pip-ephem-wheel-cache-ftu0qysz/wheels/7f/7a/8e/a0bf241336e2e3cb573e1e21e5600952d49f5162454f2e612f
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WARNING: Built wheel for lhotse is invalid: Metadata 1.2 mandates PEP 440 version, but '0.8.0.dev-2a1410b-clean' is not
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Failed to build lhotse
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Installing collected packages: pycparser, toolz, sortedcontainers, pyparsing, numpy, cffi, tqdm, torchaudio, SoundFile, pyyaml, packa
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ging, lilcom, intervaltree, h5py, dataclasses, cytoolz, click, audioread, lhotse
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Running setup.py install for lhotse ... done
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DEPRECATION: lhotse was installed using the legacy 'setup.py install' method, because a wheel could not be built for it. A possible
|
||||
replacement is to fix the wheel build issue reported above. You can find discussion regarding this at https://github.com/pypa/pip/is
|
||||
sues/8368.
|
||||
Successfully installed SoundFile-0.10.3.post1 audioread-2.1.9 cffi-1.14.6 click-8.0.1 cytoolz-0.11.0 dataclasses-0.6 h5py-3.4.0 inter
|
||||
valtree-3.1.0 lhotse-0.8.0.dev-2a1410b-clean lilcom-1.1.1 numpy-1.21.2 packaging-21.0 pycparser-2.20 pyparsing-2.4.7 pyyaml-5.4.1 sor
|
||||
tedcontainers-2.4.0 toolz-0.11.1 torchaudio-0.9.0 tqdm-4.62.1
|
||||
|
||||
**NOTE**: After installing ``lhotse``, you will encounter the following error:
|
||||
|
||||
.. code-block::
|
||||
|
||||
$ lhotse download --help
|
||||
-bash: /ceph-fj/fangjun/test-icefall/bin/lhotse: python: bad interpreter: No such file or directory
|
||||
|
||||
The correct fix is:
|
||||
|
||||
.. code-block::
|
||||
|
||||
echo '#!/usr/bin/env python3' | cat - $(which lhotse) > /tmp/lhotse-bin
|
||||
chmod +x /tmp/lhotse-bin
|
||||
mv /tmp/lhotse-bin $(which lhotse)
|
||||
|
||||
|
||||
(5) Download icefall
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
.. code-block::
|
||||
|
||||
$ cd /tmp
|
||||
$ git clone https://github.com/k2-fsa/icefall
|
||||
|
||||
Cloning into 'icefall'...
|
||||
remote: Enumerating objects: 500, done.
|
||||
remote: Counting objects: 100% (500/500), done.
|
||||
remote: Compressing objects: 100% (308/308), done.
|
||||
remote: Total 500 (delta 263), reused 307 (delta 102), pack-reused 0
|
||||
Receiving objects: 100% (500/500), 172.49 KiB | 385.00 KiB/s, done.
|
||||
Resolving deltas: 100% (263/263), done.
|
||||
|
||||
$ cd icefall
|
||||
$ pip install -r requirements.txt
|
||||
|
||||
Collecting kaldilm
|
||||
Downloading kaldilm-1.8.tar.gz (48 kB)
|
||||
|________________________________| 48 kB 574 kB/s
|
||||
Collecting kaldialign
|
||||
Using cached kaldialign-0.2-cp38-cp38-linux_x86_64.whl
|
||||
Collecting sentencepiece>=0.1.96
|
||||
Using cached sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)
|
||||
Collecting tensorboard
|
||||
Using cached tensorboard-2.6.0-py3-none-any.whl (5.6 MB)
|
||||
Requirement already satisfied: setuptools>=41.0.0 in /ceph-fj/fangjun/test-icefall/lib/python3.8/site-packages (from tensorboard->-r
|
||||
requirements.txt (line 4)) (57.4.0)
|
||||
Collecting absl-py>=0.4
|
||||
Using cached absl_py-0.13.0-py3-none-any.whl (132 kB)
|
||||
Collecting google-auth-oauthlib<0.5,>=0.4.1
|
||||
Using cached google_auth_oauthlib-0.4.5-py2.py3-none-any.whl (18 kB)
|
||||
Collecting grpcio>=1.24.3
|
||||
Using cached grpcio-1.39.0-cp38-cp38-manylinux2014_x86_64.whl (4.3 MB)
|
||||
Requirement already satisfied: wheel>=0.26 in /ceph-fj/fangjun/test-icefall/lib/python3.8/site-packages (from tensorboard->-r require
|
||||
ments.txt (line 4)) (0.36.2)
|
||||
Requirement already satisfied: numpy>=1.12.0 in /ceph-fj/fangjun/test-icefall/lib/python3.8/site-packages (from tensorboard->-r requi
|
||||
rements.txt (line 4)) (1.21.2)
|
||||
Collecting protobuf>=3.6.0
|
||||
Using cached protobuf-3.17.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.0 MB)
|
||||
Collecting werkzeug>=0.11.15
|
||||
Using cached Werkzeug-2.0.1-py3-none-any.whl (288 kB)
|
||||
Collecting tensorboard-data-server<0.7.0,>=0.6.0
|
||||
Using cached tensorboard_data_server-0.6.1-py3-none-manylinux2010_x86_64.whl (4.9 MB)
|
||||
Collecting google-auth<2,>=1.6.3
|
||||
Downloading google_auth-1.35.0-py2.py3-none-any.whl (152 kB)
|
||||
|________________________________| 152 kB 1.4 MB/s
|
||||
Collecting requests<3,>=2.21.0
|
||||
Using cached requests-2.26.0-py2.py3-none-any.whl (62 kB)
|
||||
Collecting tensorboard-plugin-wit>=1.6.0
|
||||
Using cached tensorboard_plugin_wit-1.8.0-py3-none-any.whl (781 kB)
|
||||
Collecting markdown>=2.6.8
|
||||
Using cached Markdown-3.3.4-py3-none-any.whl (97 kB)
|
||||
Collecting six
|
||||
Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
|
||||
Collecting cachetools<5.0,>=2.0.0
|
||||
Using cached cachetools-4.2.2-py3-none-any.whl (11 kB)
|
||||
Collecting rsa<5,>=3.1.4
|
||||
Using cached rsa-4.7.2-py3-none-any.whl (34 kB)
|
||||
Collecting pyasn1-modules>=0.2.1
|
||||
Using cached pyasn1_modules-0.2.8-py2.py3-none-any.whl (155 kB)
|
||||
Collecting requests-oauthlib>=0.7.0
|
||||
Using cached requests_oauthlib-1.3.0-py2.py3-none-any.whl (23 kB)
|
||||
Collecting pyasn1<0.5.0,>=0.4.6
|
||||
Using cached pyasn1-0.4.8-py2.py3-none-any.whl (77 kB)
|
||||
Collecting urllib3<1.27,>=1.21.1
|
||||
Using cached urllib3-1.26.6-py2.py3-none-any.whl (138 kB)
|
||||
Collecting certifi>=2017.4.17
|
||||
Using cached certifi-2021.5.30-py2.py3-none-any.whl (145 kB)
|
||||
Collecting charset-normalizer~=2.0.0
|
||||
Using cached charset_normalizer-2.0.4-py3-none-any.whl (36 kB)
|
||||
Collecting idna<4,>=2.5
|
||||
Using cached idna-3.2-py3-none-any.whl (59 kB)
|
||||
Collecting oauthlib>=3.0.0
|
||||
Using cached oauthlib-3.1.1-py2.py3-none-any.whl (146 kB)
|
||||
Building wheels for collected packages: kaldilm
|
||||
Building wheel for kaldilm (setup.py) ... done
|
||||
Created wheel for kaldilm: filename=kaldilm-1.8-cp38-cp38-linux_x86_64.whl size=897233 sha256=eccb906cafcd45bf9a7e1a1718e4534254bfb
|
||||
f4c0d0cbc66eee6c88d68a63862
|
||||
Stored in directory: /root/fangjun/.cache/pip/wheels/85/7d/63/f2dd586369b8797cb36d213bf3a84a789eeb92db93d2e723c9
|
||||
Successfully built kaldilm
|
||||
Installing collected packages: urllib3, pyasn1, idna, charset-normalizer, certifi, six, rsa, requests, pyasn1-modules, oauthlib, cach
|
||||
etools, requests-oauthlib, google-auth, werkzeug, tensorboard-plugin-wit, tensorboard-data-server, protobuf, markdown, grpcio, google
|
||||
-auth-oauthlib, absl-py, tensorboard, sentencepiece, kaldilm, kaldialign
|
||||
Successfully installed absl-py-0.13.0 cachetools-4.2.2 certifi-2021.5.30 charset-normalizer-2.0.4 google-auth-1.35.0 google-auth-oaut
|
||||
hlib-0.4.5 grpcio-1.39.0 idna-3.2 kaldialign-0.2 kaldilm-1.8 markdown-3.3.4 oauthlib-3.1.1 protobuf-3.17.3 pyasn1-0.4.8 pyasn1-module
|
||||
s-0.2.8 requests-2.26.0 requests-oauthlib-1.3.0 rsa-4.7.2 sentencepiece-0.1.96 six-1.16.0 tensorboard-2.6.0 tensorboard-data-server-0
|
||||
.6.1 tensorboard-plugin-wit-1.8.0 urllib3-1.26.6 werkzeug-2.0.1
|
||||
|
||||
|
||||
Test your Installation
|
||||
----------------------
|
||||
|
||||
To test that your installation is successful, let us run
|
||||
the `yesno recipe <https://github.com/k2-fsa/icefall/tree/master/egs/yesno/ASR>`_
|
||||
on CPU.
|
||||
|
||||
Data preparation
|
||||
~~~~~~~~~~~~~~~~
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ export PYTHONPATH=/tmp/icefall:$PYTHONPATH
|
||||
$ cd /tmp/icefall
|
||||
$ cd egs/yesno/AS
|
||||
$ ./prepare.sh
|
||||
|
||||
The log of running ``./prepare.sh`` is:
|
||||
|
||||
.. code-block::
|
||||
|
||||
2021-08-23 19:27:26 (prepare.sh:24:main) dl_dir: /tmp/icefall/egs/yesno/ASR/download
|
||||
2021-08-23 19:27:26 (prepare.sh:27:main) stage 0: Download data
|
||||
Downloading waves_yesno.tar.gz: 4.49MB [00:03, 1.39MB/s]
|
||||
2021-08-23 19:27:30 (prepare.sh:36:main) Stage 1: Prepare yesno manifest
|
||||
2021-08-23 19:27:31 (prepare.sh:42:main) Stage 2: Compute fbank for yesno
|
||||
2021-08-23 19:27:32,803 INFO [compute_fbank_yesno.py:52] Processing train
|
||||
Extracting and storing features: 100%|_______________________________________________________________| 90/90 [00:01<00:00, 80.57it/s]
|
||||
2021-08-23 19:27:34,085 INFO [compute_fbank_yesno.py:52] Processing test
|
||||
Extracting and storing features: 100%|______________________________________________________________| 30/30 [00:00<00:00, 248.21it/s]
|
||||
2021-08-23 19:27:34 (prepare.sh:48:main) Stage 3: Prepare lang
|
||||
2021-08-23 19:27:35 (prepare.sh:63:main) Stage 4: Prepare G
|
||||
/tmp/pip-install-fcordre9/kaldilm_6899d26f2d684ad48f21025950cd2866/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Rea
|
||||
d(std::istream&):79
|
||||
[I] Reading \data\ section.
|
||||
/tmp/pip-install-fcordre9/kaldilm_6899d26f2d684ad48f21025950cd2866/kaldilm/csrc/arpa_file_parser.cc:void kaldilm::ArpaFileParser::Rea
|
||||
d(std::istream&):140
|
||||
[I] Reading \1-grams: section.
|
||||
2021-08-23 19:27:35 (prepare.sh:89:main) Stage 5: Compile HLG
|
||||
2021-08-23 19:27:35,928 INFO [compile_hlg.py:120] Processing data/lang_phone
|
||||
2021-08-23 19:27:35,929 INFO [lexicon.py:116] Converting L.pt to Linv.pt
|
||||
2021-08-23 19:27:35,931 INFO [compile_hlg.py:48] Building ctc_topo. max_token_id: 3
|
||||
2021-08-23 19:27:35,932 INFO [compile_hlg.py:52] Loading G.fst.txt
|
||||
2021-08-23 19:27:35,932 INFO [compile_hlg.py:62] Intersecting L and G
|
||||
2021-08-23 19:27:35,933 INFO [compile_hlg.py:64] LG shape: (4, None)
|
||||
2021-08-23 19:27:35,933 INFO [compile_hlg.py:66] Connecting LG
|
||||
2021-08-23 19:27:35,933 INFO [compile_hlg.py:68] LG shape after k2.connect: (4, None)
|
||||
2021-08-23 19:27:35,933 INFO [compile_hlg.py:70] <class 'torch.Tensor'>
|
||||
2021-08-23 19:27:35,933 INFO [compile_hlg.py:71] Determinizing LG
|
||||
2021-08-23 19:27:35,934 INFO [compile_hlg.py:74] <class '_k2.RaggedInt'>
|
||||
2021-08-23 19:27:35,934 INFO [compile_hlg.py:76] Connecting LG after k2.determinize
|
||||
2021-08-23 19:27:35,934 INFO [compile_hlg.py:79] Removing disambiguation symbols on LG
|
||||
2021-08-23 19:27:35,934 INFO [compile_hlg.py:87] LG shape after k2.remove_epsilon: (6, None)
|
||||
2021-08-23 19:27:35,935 INFO [compile_hlg.py:92] Arc sorting LG
|
||||
2021-08-23 19:27:35,935 INFO [compile_hlg.py:95] Composing H and LG
|
||||
2021-08-23 19:27:35,935 INFO [compile_hlg.py:102] Connecting LG
|
||||
2021-08-23 19:27:35,935 INFO [compile_hlg.py:105] Arc sorting LG
|
||||
2021-08-23 19:27:35,936 INFO [compile_hlg.py:107] HLG.shape: (8, None)
|
||||
2021-08-23 19:27:35,936 INFO [compile_hlg.py:123] Saving HLG.pt to data/lang_phone
|
||||
|
||||
|
||||
Training
|
||||
~~~~~~~~
|
||||
|
||||
Now let us run the training part:
|
||||
|
||||
.. code-block::
|
||||
|
||||
$ export CUDA_VISIBLE_DEVICES=""
|
||||
$ ./tdnn/train.py
|
||||
|
||||
.. CAUTION::
|
||||
|
||||
We use ``export CUDA_VISIBLE_DEVICES=""`` so that icefall uses CPU
|
||||
even if there are GPUs available.
|
||||
|
||||
The training log is given below:
|
||||
|
||||
.. code-block::
|
||||
|
||||
2021-08-23 19:30:31,072 INFO [train.py:465] Training started
|
||||
2021-08-23 19:30:31,072 INFO [train.py:466] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lr': 0.01,
|
||||
'feature_dim': 23, 'weight_decay': 1e-06, 'start_epoch': 0, 'best_train_loss': inf, 'best_valid_loss': inf, 'best_train_epoch': -1, '
|
||||
best_valid_epoch': -1, 'batch_idx_train': 0, 'log_interval': 10, 'valid_interval': 10, 'beam_size': 10, 'reduction': 'sum', 'use_doub
|
||||
le_scores': True, 'world_size': 1, 'master_port': 12354, 'tensorboard': True, 'num_epochs': 15, 'feature_dir': PosixPath('data/fbank'
|
||||
), 'max_duration': 30.0, 'bucketing_sampler': False, 'num_buckets': 10, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0
|
||||
, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2}
|
||||
2021-08-23 19:30:31,074 INFO [lexicon.py:113] Loading pre-compiled data/lang_phone/Linv.pt
|
||||
2021-08-23 19:30:31,098 INFO [asr_datamodule.py:146] About to get train cuts
|
||||
2021-08-23 19:30:31,098 INFO [asr_datamodule.py:240] About to get train cuts
|
||||
2021-08-23 19:30:31,102 INFO [asr_datamodule.py:149] About to create train dataset
|
||||
2021-08-23 19:30:31,102 INFO [asr_datamodule.py:200] Using SingleCutSampler.
|
||||
2021-08-23 19:30:31,102 INFO [asr_datamodule.py:206] About to create train dataloader
|
||||
2021-08-23 19:30:31,102 INFO [asr_datamodule.py:219] About to get test cuts
|
||||
2021-08-23 19:30:31,102 INFO [asr_datamodule.py:246] About to get test cuts
|
||||
2021-08-23 19:30:31,357 INFO [train.py:416] Epoch 0, batch 0, batch avg loss 1.0789, total avg loss: 1.0789, batch size: 4
|
||||
2021-08-23 19:30:31,848 INFO [train.py:416] Epoch 0, batch 10, batch avg loss 0.5356, total avg loss: 0.7556, batch size: 4
|
||||
2021-08-23 19:30:32,301 INFO [train.py:432] Epoch 0, valid loss 0.9972, best valid loss: 0.9972 best valid epoch: 0
|
||||
2021-08-23 19:30:32,805 INFO [train.py:416] Epoch 0, batch 20, batch avg loss 0.2436, total avg loss: 0.5717, batch size: 3
|
||||
2021-08-23 19:30:33,109 INFO [train.py:432] Epoch 0, valid loss 0.4167, best valid loss: 0.4167 best valid epoch: 0
|
||||
2021-08-23 19:30:33,121 INFO [checkpoint.py:62] Saving checkpoint to tdnn/exp/epoch-0.pt
|
||||
2021-08-23 19:30:33,325 INFO [train.py:416] Epoch 1, batch 0, batch avg loss 0.2214, total avg loss: 0.2214, batch size: 5
|
||||
2021-08-23 19:30:33,798 INFO [train.py:416] Epoch 1, batch 10, batch avg loss 0.0781, total avg loss: 0.1343, batch size: 5
|
||||
2021-08-23 19:30:34,065 INFO [train.py:432] Epoch 1, valid loss 0.0859, best valid loss: 0.0859 best valid epoch: 1
|
||||
2021-08-23 19:30:34,556 INFO [train.py:416] Epoch 1, batch 20, batch avg loss 0.0421, total avg loss: 0.0975, batch size: 3
|
||||
2021-08-23 19:30:34,810 INFO [train.py:432] Epoch 1, valid loss 0.0431, best valid loss: 0.0431 best valid epoch: 1
|
||||
2021-08-23 19:30:34,824 INFO [checkpoint.py:62] Saving checkpoint to tdnn/exp/epoch-1.pt
|
||||
|
||||
... ...
|
||||
|
||||
2021-08-23 19:30:49,657 INFO [train.py:416] Epoch 13, batch 0, batch avg loss 0.0109, total avg loss: 0.0109, batch size: 5
|
||||
2021-08-23 19:30:49,984 INFO [train.py:416] Epoch 13, batch 10, batch avg loss 0.0093, total avg loss: 0.0096, batch size: 4
|
||||
2021-08-23 19:30:50,239 INFO [train.py:432] Epoch 13, valid loss 0.0104, best valid loss: 0.0101 best valid epoch: 12
|
||||
2021-08-23 19:30:50,569 INFO [train.py:416] Epoch 13, batch 20, batch avg loss 0.0092, total avg loss: 0.0096, batch size: 2
|
||||
2021-08-23 19:30:50,819 INFO [train.py:432] Epoch 13, valid loss 0.0101, best valid loss: 0.0101 best valid epoch: 13
|
||||
2021-08-23 19:30:50,835 INFO [checkpoint.py:62] Saving checkpoint to tdnn/exp/epoch-13.pt
|
||||
2021-08-23 19:30:51,024 INFO [train.py:416] Epoch 14, batch 0, batch avg loss 0.0105, total avg loss: 0.0105, batch size: 5
|
||||
2021-08-23 19:30:51,317 INFO [train.py:416] Epoch 14, batch 10, batch avg loss 0.0099, total avg loss: 0.0097, batch size: 4
|
||||
2021-08-23 19:30:51,552 INFO [train.py:432] Epoch 14, valid loss 0.0108, best valid loss: 0.0101 best valid epoch: 13
|
||||
2021-08-23 19:30:51,869 INFO [train.py:416] Epoch 14, batch 20, batch avg loss 0.0096, total avg loss: 0.0097, batch size: 5
|
||||
2021-08-23 19:30:52,107 INFO [train.py:432] Epoch 14, valid loss 0.0102, best valid loss: 0.0101 best valid epoch: 13
|
||||
2021-08-23 19:30:52,126 INFO [checkpoint.py:62] Saving checkpoint to tdnn/exp/epoch-14.pt
|
||||
2021-08-23 19:30:52,128 INFO [train.py:537] Done!
|
||||
|
||||
Decoding
|
||||
~~~~~~~~
|
||||
|
||||
Let us use the trained model to decode the test set:
|
||||
|
||||
.. code-block::
|
||||
|
||||
$ ./tdnn/decode.py
|
||||
|
||||
The decoding log is:
|
||||
|
||||
.. code-block::
|
||||
|
||||
2021-08-23 19:35:30,192 INFO [decode.py:249] Decoding started
|
||||
2021-08-23 19:35:30,192 INFO [decode.py:250] {'exp_dir': PosixPath('tdnn/exp'), 'lang_dir': PosixPath('data/lang_phone'), 'lm_dir': PosixPath('data/lm'), 'feature_dim': 23, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'epoch': 14, 'avg': 2, 'feature_dir': PosixPath('data/fbank'), 'max_duration': 30.0, 'bucketing_sampler': False, 'num_buckets': 10, 'concatenate_cuts': False, 'duration_factor': 1.0, 'gap': 1.0, 'on_the_fly_feats': False, 'shuffle': True, 'return_cuts': True, 'num_workers': 2}
|
||||
2021-08-23 19:35:30,193 INFO [lexicon.py:113] Loading pre-compiled data/lang_phone/Linv.pt
|
||||
2021-08-23 19:35:30,213 INFO [decode.py:259] device: cpu
|
||||
2021-08-23 19:35:30,217 INFO [decode.py:279] averaging ['tdnn/exp/epoch-13.pt', 'tdnn/exp/epoch-14.pt']
|
||||
/tmp/icefall/icefall/checkpoint.py:146: UserWarning: floor_divide is deprecated, and will be removed in a future version of pytorch.
|
||||
It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values.
|
||||
To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor'). (Triggered internally at /pytorch/aten/src/ATen/native/BinaryOps.cpp:450.)
|
||||
avg[k] //= n
|
||||
2021-08-23 19:35:30,220 INFO [asr_datamodule.py:219] About to get test cuts
|
||||
2021-08-23 19:35:30,220 INFO [asr_datamodule.py:246] About to get test cuts
|
||||
2021-08-23 19:35:30,409 INFO [decode.py:190] batch 0/8, cuts processed until now is 4
|
||||
2021-08-23 19:35:30,571 INFO [decode.py:228] The transcripts are stored in tdnn/exp/recogs-test_set.txt
|
||||
2021-08-23 19:35:30,572 INFO [utils.py:317] [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
|
||||
2021-08-23 19:35:30,573 INFO [decode.py:236] Wrote detailed error stats to tdnn/exp/errs-test_set.txt
|
||||
2021-08-23 19:35:30,573 INFO [decode.py:299] Done!
|
||||
|
||||
Congratulations! You have successfully setup the environment and have run the first recipe in icefall.
|
||||
|
||||
Have fun with icefall!
|
||||
19
docs/source/recipes/index.rst
Normal file
19
docs/source/recipes/index.rst
Normal file
@ -0,0 +1,19 @@
|
||||
Recipes
|
||||
=======
|
||||
|
||||
This page contains various recipes in icefall.
|
||||
Currently, only speech recognition recipes are provided.
|
||||
|
||||
We may add recipes for other tasks in the future.
|
||||
|
||||
.. we put the yesno recipe as the first recipe since it is the simplest
|
||||
.. recipe.
|
||||
.. Other recipes are sorted alphabetically
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
yesno
|
||||
|
||||
librispeech
|
||||
|
||||
2
docs/source/recipes/librispeech.rst
Normal file
2
docs/source/recipes/librispeech.rst
Normal file
@ -0,0 +1,2 @@
|
||||
LibriSpeech
|
||||
===========
|
||||
108
docs/source/recipes/yesno.rst
Normal file
108
docs/source/recipes/yesno.rst
Normal file
@ -0,0 +1,108 @@
|
||||
yesno
|
||||
=====
|
||||
|
||||
This page shows you how to run the ``yesno`` recipe.
|
||||
|
||||
.. HINT::
|
||||
|
||||
We assume you have read the page :ref:`install icefall` and have setup
|
||||
the environment for ``icefall``.
|
||||
|
||||
.. HINT::
|
||||
|
||||
You **don't** need a **GPU** to run this reciped. It can be run on a **CPU**.
|
||||
The training time takes less than 30 **seconds** and you will get
|
||||
the following WER::
|
||||
|
||||
[test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
|
||||
|
||||
Data preparation
|
||||
----------------
|
||||
|
||||
.. code-block::
|
||||
|
||||
$ cd egs/yesno/ASR
|
||||
$ ./prepare.sh
|
||||
|
||||
The script ``./prepare.sh`` handles the data preparation for you, automagically.
|
||||
All you need to do is to run it.
|
||||
|
||||
The data preparation contains several stages, you can use the following two
|
||||
options:
|
||||
|
||||
- ``--stage``
|
||||
- ``--stop-stage``
|
||||
|
||||
to control which stage(s) should be run. By default, all stages are executed.
|
||||
|
||||
|
||||
For example,
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ cd egs/yesno/ASR
|
||||
$ ./prepare.sh --stage 0 --stop-stage 0
|
||||
|
||||
means to run only stage 0.
|
||||
|
||||
To run stage 2 to stage 5, use:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ ./prepare.sh --stage 2 --stop-stage 5
|
||||
|
||||
|
||||
Training
|
||||
--------
|
||||
|
||||
We provide only a TDNN model, contained in
|
||||
the `tdnn <https://github.com/k2-fsa/icefall/tree/master/egs/yesno/ASR/tdnn>`_
|
||||
folder, for ``yesno``.
|
||||
|
||||
The command to run the training part is:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ cd egs/yesno/ASR
|
||||
$ ./tdnn/train.py
|
||||
|
||||
By default, it will run ``15`` epochs. Training logs and checkpoints are saved
|
||||
in ``tdnn/exp``.
|
||||
|
||||
To see the training options, you can use:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ ./tdnn/train.py --help
|
||||
|
||||
Decoding
|
||||
--------
|
||||
|
||||
The decoding part uses checkpoints saved by the training part, so you have
|
||||
to run the training part first.
|
||||
|
||||
The command for decoding is:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ ./tdnn/decode.py
|
||||
|
||||
You will see the WER in the output log.
|
||||
Decoding results are saved in ``tdnn/exp``.
|
||||
|
||||
Colab notebook
|
||||
--------------
|
||||
|
||||
We do provide a colab notebook for this recipe.
|
||||
|
||||
|yesno colab notebook|
|
||||
|
||||
.. |yesno colab notebook| image:: https://colab.research.google.com/assets/colab-badge.svg
|
||||
:target: https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing
|
||||
|
||||
|
||||
|
||||
Use a pre-trained model
|
||||
-----------------------
|
||||
|
||||
TODO
|
||||
Loading…
x
Reference in New Issue
Block a user