WIP: Add documentation. (#22)

* Begin to add documentation. * WIP: Add documentation. * Fix a typo. * Add more doc for the recipe yesno. * Add more doc for the yesno recipe.
2025-08-08 09:32:20 +00:00 · 2021-08-24 14:28:08 +08:00 · 2021-08-24 14:28:08 +08:00 · 01da00dca0
commit 01da00dca0
parent 57cb611665
19 changed files with 1352 additions and 2 deletions
--- a/docs/.gitignore
+++ b/docs/.gitignore
@ -0,0 +1 @@
+build/
--- a/docs/Makefile
+++ b/docs/Makefile
@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
--- a/docs/make.bat
+++ b/docs/make.bat
@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@ -0,0 +1 @@
+sphinx_rtd_theme
--- a/docs/source/_static/logo.png
+++ b/docs/source/_static/logo.png
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@ -0,0 +1,77 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+# import os
+# import sys
+# sys.path.insert(0, os.path.abspath('.'))
+
+import sphinx_rtd_theme
+
+
+# -- Project information -----------------------------------------------------
+
+project = "icefall"
+copyright = "2021, icefall development team"
+author = "icefall development team"
+
+# The full version, including alpha/beta/rc tags
+release = "0.1"
+
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    "sphinx_rtd_theme",
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ["_templates"]
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = []
+
+source_suffix = {
+    ".rst": "restructuredtext",
+}
+master_doc = "index"
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = "sphinx_rtd_theme"
+html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+html_show_sourcelink = True
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ["_static", "installation/images"]
+
+pygments_style = "sphinx"
+
+numfig = True
+
+html_context = {
+    "display_github": True,
+    "github_user": "k2-fsa",
+    "github_repo": "icefall",
+    "github_version": "master",
+    "conf_py_path": "/icefall/docs/source/",
+}
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@ -0,0 +1,24 @@
+.. icefall documentation master file, created by
+   sphinx-quickstart on Mon Aug 23 16:07:39 2021.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+icefall
+=======
+
+.. image:: _static/logo.png
+    :alt: icefall logo
+    :width: 168px
+    :align: center
+    :target: https://github.com/k2-fsa/icefall
+
+
+Documentation for `icefall <https://github.com/k2-fsa/icefall>`_, containing
+speech recognition recipes using `k2 <https://github.com/k2-fsa/k2>`_.
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+   installation/index
+   recipes/index
--- a/docs/source/installation/images/device-CPU_CUDA-orange.svg
+++ b/docs/source/installation/images/device-CPU_CUDA-orange.svg
@ -0,0 +1 @@
+<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>
--- a/docs/source/installation/images/os-Linux_macOS-ff69b4.svg
+++ b/docs/source/installation/images/os-Linux_macOS-ff69b4.svg
@ -0,0 +1 @@
+<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>
--- a/docs/source/installation/images/python-3.6_3.7_3.8_3.9-blue.svg
+++ b/docs/source/installation/images/python-3.6_3.7_3.8_3.9-blue.svg
@ -0,0 +1 @@
+<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>
--- a/docs/source/installation/images/torch-1.6.0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg
+++ b/docs/source/installation/images/torch-1.6.0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg
@ -0,0 +1 @@
+<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>
--- a/docs/source/installation/index.rst
+++ b/docs/source/installation/index.rst
@ -0,0 +1,469 @@
+.. _install icefall:
+
+Installation
+============
+
+- |os|
+- |device|
+- |python_versions|
+- |torch_versions|
+
+.. |os| image:: ./images/os-Linux_macOS-ff69b4.svg
+  :alt: Supported operating systems
+
+.. |device| image:: ./images/device-CPU_CUDA-orange.svg
+  :alt: Supported devices
+
+.. |python_versions| image:: ./images/python-3.6_3.7_3.8_3.9-blue.svg
+  :alt: Supported python versions
+
+.. |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
+  :alt: Supported PyTorch versions
+
+icefall depends on `k2 <https://github.com/k2-fsa/k2>`_ and
+`lhotse <https://github.com/lhotse-speech/lhotse>`_.
+
+We recommend you to install ``k2`` first, as ``k2`` is bound to
+a specific version of PyTorch after compilation. Install ``k2`` also
+installs its dependency PyTorch, which can be reused by ``lhotse``.
+
+
+(1) Install k2
+--------------
+
+Please refer to `<https://k2.readthedocs.io/en/latest/installation/index.html>`_
+to install `k2`.
+
+.. HINT::
+
+  If you have already installed PyTorch and don't want to replace it,
+  please install a version of k2 that is compiled against the version
+  of PyTorch you are using.
+
+(2) Install lhotse
+------------------
+
+Please refer to `<https://lhotse.readthedocs.io/en/latest/getting-started.html#installation>`_
+to install ``lhotse``.
+
+.. HINT::
+
+  Install ``lhotse`` also installs its dependency `torchaudio <https://github.com/pytorch/audio>`_.
+
+(3) Download icefall
+--------------------
+
+icefall is a collection of Python scripts, so you don't need to install it
+and we don't provide a ``setup.py`` to install it.
+
+What you need is to download it and set the environment variable ``PYTHONPATH``
+to point to it.
+
+Assume you want to place ``icefall`` in the folder ``/tmp``. The
+following commands show you how to setup ``icefall``:
+
+
+.. code-block:: bash
+
+  cd /tmp
+  git clone https://github.com/k2-fsa/icefall
+  cd icefall
+  pip install -r requirements.txt
+  export PYTHONPATH=/tmp/icefall:$PYTHONPATH
+
+.. HINT::
+
+  You can put several versions of ``icefall`` in the same virtual environment.
+  To switch among different versions of ``icefall``, just set ``PYTHONPATH``
+  to point to the version you want.
+
+
+Installation example
+--------------------
+
+The following shows an example about setting up the environment.
+
+
+(1) Create a virtual environment
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+  $ virtualenv -p python3.8  test-icefall
+
+  created virtual environment CPython3.8.6.final.0-64 in 1540ms
+    creator CPython3Posix(dest=/ceph-fj/fangjun/test-icefall, clear=False, no_vcs_ignore=False, global=False)
+    seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/fangjun/.local/share/v
+  irtualenv)
+      added seed packages: pip==21.1.3, setuptools==57.4.0, wheel==0.36.2
+    activators BashActivator,CShellActivator,FishActivator,PowerShellActivator,PythonActivator,XonshActivator
+
+
+(2) Activate your virtual environment
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+  $ source test-icefall/bin/activate
+
+(3) Install k2
+~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+  $ pip install k2==1.4.dev20210822+cpu.torch1.9.0 -f https://k2-fsa.org/nightly/index.html
+
+  Looking in links: https://k2-fsa.org/nightly/index.html
+  Collecting k2==1.4.dev20210822+cpu.torch1.9.0
+    Downloading https://k2-fsa.org/nightly/whl/k2-1.4.dev20210822%2Bcpu.torch1.9.0-cp38-cp38-linux_x86_64.whl (1.6 MB)
+       |________________________________| 1.6 MB 185 kB/s
+  Collecting graphviz
+    Downloading graphviz-0.17-py3-none-any.whl (18 kB)
+  Collecting torch==1.9.0
+    Using cached torch-1.9.0-cp38-cp38-manylinux1_x86_64.whl (831.4 MB)
+  Collecting typing-extensions
+    Using cached typing_extensions-3.10.0.0-py3-none-any.whl (26 kB)
+  Installing collected packages: typing-extensions, torch, graphviz, k2
+  Successfully installed graphviz-0.17 k2-1.4.dev20210822+cpu.torch1.9.0 torch-1.9.0 typing-extensions-3.10.0.0
+
+.. WARNING::
+
+  We choose to install a CPU version of k2 for testing. You would probably want to install
+  a CUDA version of k2.
+
+
+(4) Install lhotse
+~~~~~~~~~~~~~~~~~~
+
+.. code-block::
+
+  $ pip install git+https://github.com/lhotse-speech/lhotse
+
+  Collecting git+https://github.com/lhotse-speech/lhotse
+    Cloning https://github.com/lhotse-speech/lhotse to /tmp/pip-req-build-7b1b76ge
+    Running command git clone -q https://github.com/lhotse-speech/lhotse /tmp/pip-req-build-7b1b76ge
+  Collecting audioread>=2.1.9
+    Using cached audioread-2.1.9-py3-none-any.whl
+  Collecting SoundFile>=0.10
+    Using cached SoundFile-0.10.3.post1-py2.py3-none-any.whl (21 kB)
+  Collecting click>=7.1.1
+    Using cached click-8.0.1-py3-none-any.whl (97 kB)
+  Collecting cytoolz>=0.10.1
+    Using cached cytoolz-0.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.9 MB)
+  Collecting dataclasses
+    Using cached dataclasses-0.6-py3-none-any.whl (14 kB)
+  Collecting h5py>=2.10.0
+    Downloading h5py-3.4.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (4.5 MB)
+       |________________________________| 4.5 MB 684 kB/s
+  Collecting intervaltree>=3.1.0
+    Using cached intervaltree-3.1.0-py2.py3-none-any.whl
+  Collecting lilcom>=1.1.0
+    Using cached lilcom-1.1.1-cp38-cp38-linux_x86_64.whl
+  Collecting numpy>=1.18.1
+    Using cached numpy-1.21.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.8 MB)
+  Collecting packaging
+    Using cached packaging-21.0-py3-none-any.whl (40 kB)
+  Collecting pyyaml>=5.3.1
+    Using cached PyYAML-5.4.1-cp38-cp38-manylinux1_x86_64.whl (662 kB)
+  Collecting tqdm
+    Downloading tqdm-4.62.1-py2.py3-none-any.whl (76 kB)
+       |________________________________| 76 kB 2.7 MB/s
+  Collecting torchaudio==0.9.0
+    Downloading torchaudio-0.9.0-cp38-cp38-manylinux1_x86_64.whl (1.9 MB)
+       |________________________________| 1.9 MB 73.1 MB/s
+  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
+  -2a1410b-clean) (1.9.0)
+  Requirement already satisfied: typing-extensions in ./test-icefall/lib/python3.8/site-packages (from torch==1.9.0->torchaudio==0.9.0-
+  >lhotse===0.8.0.dev-2a1410b-clean) (3.10.0.0)
+  Collecting toolz>=0.8.0
+    Using cached toolz-0.11.1-py3-none-any.whl (55 kB)
+  Collecting sortedcontainers<3.0,>=2.0
+    Using cached sortedcontainers-2.4.0-py2.py3-none-any.whl (29 kB)
+  Collecting cffi>=1.0
+    Using cached cffi-1.14.6-cp38-cp38-manylinux1_x86_64.whl (411 kB)
+  Collecting pycparser
+    Using cached pycparser-2.20-py2.py3-none-any.whl (112 kB)
+  Collecting pyparsing>=2.0.2
+    Using cached pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)
+  Building wheels for collected packages: lhotse
+    Building wheel for lhotse (setup.py) ... done
+    Created wheel for lhotse: filename=lhotse-0.8.0.dev_2a1410b_clean-py3-none-any.whl size=342242 sha256=f683444afa4dc0881133206b4646a
+  9d0f774224cc84000f55d0a67f6e4a37997
+    Stored in directory: /tmp/pip-ephem-wheel-cache-ftu0qysz/wheels/7f/7a/8e/a0bf241336e2e3cb573e1e21e5600952d49f5162454f2e612f
+    WARNING: Built wheel for lhotse is invalid: Metadata 1.2 mandates PEP 440 version, but '0.8.0.dev-2a1410b-clean' is not
+  Failed to build lhotse
+  Installing collected packages: pycparser, toolz, sortedcontainers, pyparsing, numpy, cffi, tqdm, torchaudio, SoundFile, pyyaml, packa
+  ging, lilcom, intervaltree, h5py, dataclasses, cytoolz, click, audioread, lhotse
+      Running setup.py install for lhotse ... done
+    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``!
--- a/docs/source/recipes/images/yesno-tdnn-tensorboard-log.png
+++ b/docs/source/recipes/images/yesno-tdnn-tensorboard-log.png
--- a/docs/source/recipes/index.rst
+++ b/docs/source/recipes/index.rst
@ -0,0 +1,18 @@
+Recipes
+=======
+
+This page contains various recipes in ``icefall``.
+Currently, only speech recognition recipes are provided.
+
+We may add recipes for other tasks as well in the future.
+
+.. we put the yesno recipe as the first recipe since it is the simplest one.
+.. Other recipes are listed in a alphabetical order.
+
+.. toctree::
+   :maxdepth: 2
+
+   yesno
+
+   librispeech
+
--- a/docs/source/recipes/librispeech.rst
+++ b/docs/source/recipes/librispeech.rst
@ -0,0 +1,2 @@
+LibriSpeech
+===========
--- a/docs/source/recipes/yesno.rst
+++ b/docs/source/recipes/yesno.rst
@ -0,0 +1,445 @@
+yesno
+=====
+
+This page shows you how to run the ``yesno`` recipe. It contains:
+
+  - (1) Prepare data for training
+  - (2) Train a TDNN model
+
+    - (a) View text format logs and visualize TensorBoard logs
+    - (b) Select device type, i.e., CPU and GPU, for training
+    - (c) Change training options
+    - (d) Resume training from a checkpoint
+
+  - (3) Decode with a trained model
+
+    - (a) Select a checkpoint for decoding
+    - (b) Model averaging
+
+  - (4) Colab notebook
+
+    - (a) It shows you step by step how to setup the environment, how to do training,
+          and how to do decoding
+    - (b) How to use a pre-trained model
+
+  - (5) Inference with a pre-trained model
+
+    - (a) Download a pre-trained model, provided by us
+    - (b) Decode a single sound file with a pre-trained model
+    - (c) Decode multiple sound files at the same time
+
+It does **NOT** show you:
+
+  - (1) How to train with multiple GPUs
+
+    The ``yesno`` dataset is so small that CPU is more than enough
+    for training as well as for decoding.
+
+  - (2) How to use LM rescoring for decoding
+
+    The dataset does not have an LM for rescoring.
+
+.. 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 recipe. It can be run on a **CPU**.
+  The training part takes less than 30 **seconds** on a CPU and you will get
+  the following WER at the end::
+
+    [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
+
+Data preparation
+----------------
+
+.. code-block:: bash
+
+  $ 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
+  $ export CUDA_VISIBLE_DEVICES=""
+  $ ./tdnn/train.py
+
+By default, it will run ``15`` epochs. Training logs and checkpoints are saved
+in ``tdnn/exp``.
+
+In ``tdnn/exp``, you will find the following files:
+
+  - ``epoch-0.pt``, ``epoch-1.pt``, ...
+
+    These are checkpoint files, containing model ``state_dict`` and optimizer ``state_dict``.
+    To resume training from some checkpoint, say ``epoch-10.pt``, you can use:
+
+      .. code-block:: bash
+
+        $ ./tdnn/train.py --start-epoch 11
+
+  - ``tensorboard/``
+
+    This folder contains TensorBoard logs. Training loss, validation loss, learning
+    rate, etc, are recorded in these logs. You can visualize them by:
+
+      .. code-block:: bash
+
+        $ cd tdnn/exp/tensorboard
+        $ tensorboard dev upload --logdir . --description "TDNN training for yesno with icefall"
+
+    It will print something like below:
+
+      .. code-block::
+
+        TensorFlow installation not found - running with reduced feature set.
+        Upload started and will continue reading any new data as it's added to the logdir.
+
+        To stop uploading, press Ctrl-C.
+
+        New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/yKUbhb5wRmOSXYkId1z9eg/
+
+        [2021-08-23T23:49:41] Started scanning logdir.
+        [2021-08-23T23:49:42] Total uploaded: 135 scalars, 0 tensors, 0 binary objects
+        Listening for new data in logdir...
+
+    Note there is a URL in the above output, click it and you will see
+    the following screenshot:
+
+      .. figure:: images/yesno-tdnn-tensorboard-log.png
+         :width: 600
+         :alt: TensorBoard screenshot
+         :align: center
+         :target: https://tensorboard.dev/experiment/yKUbhb5wRmOSXYkId1z9eg/
+
+         TensorBoard screenshot.
+
+  - ``log/log-train-xxxx``
+
+    It is the detailed training log in text format, same as the one
+    you saw printed to the console during training.
+
+
+
+.. NOTE::
+
+  By default, ``./tdnn/train.py`` uses GPU 0 for training if GPUs are available.
+  If you have two GPUs, say, GPU 0 and GPU 1, and you want to use GPU 1 for
+  training, you can run:
+
+    .. code-block:: bash
+
+      $ export CUDA_VISIBLE_DEVICES="1"
+      $ ./tdnn/train.py
+
+  Since the ``yesno`` dataset is very small, containing only 30 sound files
+  for training, and the model in use is also very small, we use:
+
+    .. code-block:: bash
+
+      $ export CUDA_VISIBLE_DEVICES=""
+
+  so that ``./tdnn/train.py`` uses CPU during training.
+
+  If you don't have GPUs, then you don't need to
+  run ``export CUDA_VISIBLE_DEVICES=""``.
+
+To see available training options, you can use:
+
+.. code-block:: bash
+
+  $ ./tdnn/train.py --help
+
+Other training options, e.g., learning rate, results dir, etc., are
+pre-configured in the function ``get_params()``
+in `tdnn/train.py <https://github.com/k2-fsa/icefall/blob/master/egs/yesno/ASR/tdnn/train.py>`_.
+Normally, you don't need to change them. You can change them by modifying the code, if
+you want.
+
+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
+
+  $ export CUDA_VISIBLE_DEVICES=""
+  $ ./tdnn/decode.py
+
+You will see the WER in the output log.
+
+Decoded results are saved in ``tdnn/exp``.
+
+.. code-block:: bash
+
+  $ ./tdnn/decode.py --help
+
+shows you the available decoding options.
+
+Some commonly used options are:
+
+  - ``--epoch``
+
+    You can select which checkpoint to be used for decoding.
+    For instance, ``./tdnn/decode.py --epoch 10`` means to use
+    ``./tdnn/exp/epoch-10.pt`` for decoding.
+
+  - ``--avg``
+
+    It's related to model averaging. It specifies number of checkpoints
+    to be averaged. The averaged model is used for decoding.
+    For example, the following command:
+
+      .. code-block:: bash
+
+        $ ./tdnn/decode.py --epoch 10 --avg 3
+
+    uses the average of ``epoch-8.pt``, ``epoch-9.pt`` and ``epoch-10.pt``
+    for decoding.
+
+  - ``--export``
+
+    If it is ``True``, i.e., ``./tdnn/decode.py --export 1``, the code
+    will save the averaged model to ``tdnn/exp/pretrained.pt``.
+    See :ref:`yesno use a pre-trained model` for how to use it.
+
+
+.. _yesno use a pre-trained model:
+
+Pre-trained Model
+-----------------
+
+We have uploaded the pre-trained model to
+`<https://huggingface.co/csukuangfj/icefall_asr_yesno_tdnn>`_.
+
+The following shows you how to use the pre-trained model.
+
+Download the pre-trained model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+  $ cd egs/yesno/ASR
+  $ mkdir tmp
+  $ cd tmp
+  $ git lfs install
+  $ git clone https://huggingface.co/csukuangfj/icefall_asr_yesno_tdnn
+
+.. CAUTION::
+
+  You have to use ``git lfs`` to download the pre-trained model.
+
+After downloading, you will have the following files:
+
+.. code-block:: bash
+
+  $ cd egs/yesno/ASR
+  $ tree tmp
+
+.. code-block:: bash
+
+  tmp/
+  `-- icefall_asr_yesno_tdnn
+      |-- README.md
+      |-- lang_phone
+      |   |-- HLG.pt
+      |   |-- L.pt
+      |   |-- L_disambig.pt
+      |   |-- Linv.pt
+      |   |-- lexicon.txt
+      |   |-- lexicon_disambig.txt
+      |   |-- tokens.txt
+      |   `-- words.txt
+      |-- lm
+      |   |-- G.arpa
+      |   `-- G.fst.txt
+      |-- pretrained.pt
+      `-- test_waves
+          |-- 0_0_0_1_0_0_0_1.wav
+          |-- 0_0_1_0_0_0_1_0.wav
+          |-- 0_0_1_0_0_1_1_1.wav
+          |-- 0_0_1_0_1_0_0_1.wav
+          |-- 0_0_1_1_0_0_0_1.wav
+          |-- 0_0_1_1_0_1_1_0.wav
+          |-- 0_0_1_1_1_0_0_0.wav
+          |-- 0_0_1_1_1_1_0_0.wav
+          |-- 0_1_0_0_0_1_0_0.wav
+          |-- 0_1_0_0_1_0_1_0.wav
+          |-- 0_1_0_1_0_0_0_0.wav
+          |-- 0_1_0_1_1_1_0_0.wav
+          |-- 0_1_1_0_0_1_1_1.wav
+          |-- 0_1_1_1_0_0_1_0.wav
+          |-- 0_1_1_1_1_0_1_0.wav
+          |-- 1_0_0_0_0_0_0_0.wav
+          |-- 1_0_0_0_0_0_1_1.wav
+          |-- 1_0_0_1_0_1_1_1.wav
+          |-- 1_0_1_1_0_1_1_1.wav
+          |-- 1_0_1_1_1_1_0_1.wav
+          |-- 1_1_0_0_0_1_1_1.wav
+          |-- 1_1_0_0_1_0_1_1.wav
+          |-- 1_1_0_1_0_1_0_0.wav
+          |-- 1_1_0_1_1_0_0_1.wav
+          |-- 1_1_0_1_1_1_1_0.wav
+          |-- 1_1_1_0_0_1_0_1.wav
+          |-- 1_1_1_0_1_0_1_0.wav
+          |-- 1_1_1_1_0_0_1_0.wav
+          |-- 1_1_1_1_1_0_0_0.wav
+          `-- 1_1_1_1_1_1_1_1.wav
+
+  4 directories, 42 files
+
+.. code-block:: bash
+
+  $ soxi tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav
+
+  Input File     : 'tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav'
+  Channels       : 1
+  Sample Rate    : 8000
+  Precision      : 16-bit
+  Duration       : 00:00:06.76 = 54080 samples ~ 507 CDDA sectors
+  File Size      : 108k
+  Bit Rate       : 128k
+  Sample Encoding: 16-bit Signed Integer PCM
+
+- ``0_0_1_0_1_0_0_1.wav``
+
+    0 means No; 1 means Yes. No and Yes are not in English,
+    but in `Hebrew <https://en.wikipedia.org/wiki/Hebrew_language>`_.
+    So this file contains ``NO NO YES NO YES NO NO YES``.
+
+Download kaldifeat
+~~~~~~~~~~~~~~~~~~
+
+`kaldifeat <https://github.com/csukuangfj/kaldifeat>`_ is used for extracting
+features from a single or multiple sound files. Please refer to
+`<https://github.com/csukuangfj/kaldifeat>`_ to install ``kaldifeat`` first.
+
+Inference with a pre-trained model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+  $ cd egs/yesno/ASR
+  $ ./tdnn/pretrained.py --help
+
+shows the usage information of ``./tdnn/pretrained.py``.
+
+To decode a single file, we can use:
+
+.. code-block:: bash
+
+  ./tdnn/pretrained.py \
+    --checkpoint ./tmp/icefall_asr_yesno_tdnn/pretrained.pt \
+    --words-file ./tmp/icefall_asr_yesno_tdnn/lang_phone/words.txt \
+    --HLG ./tmp/icefall_asr_yesno_tdnn/lang_phone/HLG.pt \
+    ./tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav
+
+The output is:
+
+.. code-block::
+
+  2021-08-24 12:22:51,621 INFO [pretrained.py:119] {'feature_dim': 23, 'num_classes': 4, 'sample_rate': 8000, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'checkpoint': './tmp/icefall_asr_yesno_tdnn/pretrained.pt', 'words_file': './tmp/icefall_asr_yesno_tdnn/lang_phone/words.txt', 'HLG': './tmp/icefall_asr_yesno_tdnn/lang_phone/HLG.pt', 'sound_files': ['./tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav']}
+  2021-08-24 12:22:51,645 INFO [pretrained.py:125] device: cpu
+  2021-08-24 12:22:51,645 INFO [pretrained.py:127] Creating model
+  2021-08-24 12:22:51,650 INFO [pretrained.py:139] Loading HLG from ./tmp/icefall_asr_yesno_tdnn/lang_phone/HLG.pt
+  2021-08-24 12:22:51,651 INFO [pretrained.py:143] Constructing Fbank computer
+  2021-08-24 12:22:51,652 INFO [pretrained.py:153] Reading sound files: ['./tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav']
+  2021-08-24 12:22:51,684 INFO [pretrained.py:159] Decoding started
+  2021-08-24 12:22:51,708 INFO [pretrained.py:198]
+  ./tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav:
+  NO NO YES NO YES NO NO YES
+
+
+  2021-08-24 12:22:51,708 INFO [pretrained.py:200] Decoding Done
+
+You can see that for the sound file ``0_0_1_0_1_0_0_1.wav``, the decoding result is
+``NO NO YES NO YES NO NO YES``.
+
+To decode **multiple** files at the same time, you can use
+
+.. code-block:: bash
+
+  ./tdnn/pretrained.py \
+    --checkpoint ./tmp/icefall_asr_yesno_tdnn/pretrained.pt \
+    --words-file ./tmp/icefall_asr_yesno_tdnn/lang_phone/words.txt \
+    --HLG ./tmp/icefall_asr_yesno_tdnn/lang_phone/HLG.pt \
+    ./tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav \
+    ./tmp/icefall_asr_yesno_tdnn/test_waves/1_0_1_1_0_1_1_1.wav
+
+The decoding output is:
+
+.. code-block::
+
+  2021-08-24 12:25:20,159 INFO [pretrained.py:119] {'feature_dim': 23, 'num_classes': 4, 'sample_rate': 8000, 'search_beam': 20, 'output_beam': 8, 'min_active_states': 30, 'max_active_states': 10000, 'use_double_scores': True, 'checkpoint': './tmp/icefall_asr_yesno_tdnn/pretrained.pt', 'words_file': './tmp/icefall_asr_yesno_tdnn/lang_phone/words.txt', 'HLG': './tmp/icefall_asr_yesno_tdnn/lang_phone/HLG.pt', 'sound_files': ['./tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav', './tmp/icefall_asr_yesno_tdnn/test_waves/1_0_1_1_0_1_1_1.wav']}
+  2021-08-24 12:25:20,181 INFO [pretrained.py:125] device: cpu
+  2021-08-24 12:25:20,181 INFO [pretrained.py:127] Creating model
+  2021-08-24 12:25:20,185 INFO [pretrained.py:139] Loading HLG from ./tmp/icefall_asr_yesno_tdnn/lang_phone/HLG.pt
+  2021-08-24 12:25:20,186 INFO [pretrained.py:143] Constructing Fbank computer
+  2021-08-24 12:25:20,187 INFO [pretrained.py:153] Reading sound files: ['./tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav',
+  './tmp/icefall_asr_yesno_tdnn/test_waves/1_0_1_1_0_1_1_1.wav']
+  2021-08-24 12:25:20,213 INFO [pretrained.py:159] Decoding started
+  2021-08-24 12:25:20,287 INFO [pretrained.py:198]
+  ./tmp/icefall_asr_yesno_tdnn/test_waves/0_0_1_0_1_0_0_1.wav:
+  NO NO YES NO YES NO NO YES
+
+  ./tmp/icefall_asr_yesno_tdnn/test_waves/1_0_1_1_0_1_1_1.wav:
+  YES NO YES YES NO YES YES YES
+
+  2021-08-24 12:25:20,287 INFO [pretrained.py:200] Decoding Done
+
+You can see again that it decodes correctly.
+
+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
+
+
+**Congratulations!** You have finished the simplest speech recognition recipe in ``icefall``.
--- a/egs/yesno/ASR/tdnn/decode.py
+++ b/egs/yesno/ASR/tdnn/decode.py
@ -20,6 +20,7 @@ from icefall.utils import (
    get_texts,
    setup_logger,
    store_transcripts,
+    str2bool,
    write_error_stats,
 )

@ -44,6 +45,17 @@ def get_parser():
        "consecutive checkpoints before the checkpoint specified by "
        "'--epoch'. ",
    )
+
+    parser.add_argument(
+        "--export",
+        type=str2bool,
+        default=False,
+        help="""When enabled, the averaged model is saved to
+        tdnn/exp/pretrained.pt. Note: only model.state_dict() is saved.
+        pretrained.pt contains a dict {"model": model.state_dict()},
+        which can be loaded by `icefall.checkpoint.load_checkpoint()`.
+        """,
+    )
    return parser


@ -279,6 +291,12 @@ def main():
        logging.info(f"averaging {filenames}")
        model.load_state_dict(average_checkpoints(filenames))

+    if params.export:
+        logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt")
+        torch.save(
+            {"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt"
+        )
+
    model.to(device)
    model.eval()

--- a/egs/yesno/ASR/tdnn/pretrained.py
+++ b/egs/yesno/ASR/tdnn/pretrained.py
@ -0,0 +1,209 @@
+#!/usr/bin/env python3
+# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import logging
+import math
+from typing import List
+
+import k2
+import kaldifeat
+import torch
+import torchaudio
+from model import Tdnn
+from torch.nn.utils.rnn import pad_sequence
+
+from icefall.decode import get_lattice, one_best_decoding
+from icefall.utils import AttributeDict, get_texts
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        required=True,
+        help="Path to the checkpoint. "
+        "The checkpoint is assumed to be saved by "
+        "icefall.checkpoint.save_checkpoint().",
+    )
+
+    parser.add_argument(
+        "--words-file",
+        type=str,
+        required=True,
+        help="Path to words.txt",
+    )
+
+    parser.add_argument(
+        "--HLG", type=str, required=True, help="Path to HLG.pt."
+    )
+
+    parser.add_argument(
+        "sound_files",
+        type=str,
+        nargs="+",
+        help="The input sound file(s) to transcribe. "
+        "Supported formats are those supported by torchaudio.load(). "
+        "For example, wav and flac are supported. "
+        "The sample rate has to be 16kHz.",
+    )
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    params = AttributeDict(
+        {
+            "feature_dim": 23,
+            "num_classes": 4,  # [<blk>, N, SIL, Y]
+            "sample_rate": 8000,
+            "search_beam": 20,
+            "output_beam": 8,
+            "min_active_states": 30,
+            "max_active_states": 10000,
+            "use_double_scores": True,
+        }
+    )
+    return params
+
+
+def read_sound_files(
+    filenames: List[str], expected_sample_rate: float
+) -> List[torch.Tensor]:
+    """Read a list of sound files into a list 1-D float32 torch tensors.
+    Args:
+      filenames:
+        A list of sound filenames.
+      expected_sample_rate:
+        The expected sample rate of the sound files.
+    Returns:
+      Return a list of 1-D float32 torch tensors.
+    """
+    ans = []
+    for f in filenames:
+        wave, sample_rate = torchaudio.load(f)
+        assert sample_rate == expected_sample_rate, (
+            f"expected sample rate: {expected_sample_rate}. "
+            f"Given: {sample_rate}"
+        )
+        # We use only the first channel
+        ans.append(wave[0])
+    return ans
+
+
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+    params.update(vars(args))
+    logging.info(f"{params}")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    logging.info("Creating model")
+
+    model = Tdnn(
+        num_features=params.feature_dim,
+        num_classes=params.num_classes,
+    )
+
+    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    model.load_state_dict(checkpoint["model"])
+    model.to(device)
+    model.eval()
+
+    logging.info(f"Loading HLG from {params.HLG}")
+    HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+    HLG = HLG.to(device)
+
+    logging.info("Constructing Fbank computer")
+    opts = kaldifeat.FbankOptions()
+    opts.device = device
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = params.sample_rate
+    opts.mel_opts.num_bins = params.feature_dim
+
+    fbank = kaldifeat.Fbank(opts)
+
+    logging.info(f"Reading sound files: {params.sound_files}")
+    waves = read_sound_files(
+        filenames=params.sound_files, expected_sample_rate=params.sample_rate
+    )
+    waves = [w.to(device) for w in waves]
+
+    logging.info("Decoding started")
+    features = fbank(waves)
+
+    features = pad_sequence(
+        features, batch_first=True, padding_value=math.log(1e-10)
+    )
+
+    # Note: We don't use key padding mask for attention during decoding
+    with torch.no_grad():
+        nnet_output = model(features)
+
+    batch_size = nnet_output.shape[0]
+    supervision_segments = torch.tensor(
+        [[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
+        dtype=torch.int32,
+    )
+
+    lattice = get_lattice(
+        nnet_output=nnet_output,
+        HLG=HLG,
+        supervision_segments=supervision_segments,
+        search_beam=params.search_beam,
+        output_beam=params.output_beam,
+        min_active_states=params.min_active_states,
+        max_active_states=params.max_active_states,
+    )
+
+    best_path = one_best_decoding(
+        lattice=lattice, use_double_scores=params.use_double_scores
+    )
+
+    hyps = get_texts(best_path)
+    word_sym_table = k2.SymbolTable.from_file(params.words_file)
+    hyps = [[word_sym_table[i] for i in ids] for ids in hyps]
+
+    s = "\n"
+    for filename, hyp in zip(params.sound_files, hyps):
+        words = " ".join(hyp)
+        s += f"{filename}:\n{words}\n\n"
+    logging.info(s)
+
+    logging.info("Decoding Done")
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()
--- a/egs/yesno/ASR/tdnn/train.py
+++ b/egs/yesno/ASR/tdnn/train.py
@ -60,6 +60,16 @@ def get_parser():
        help="Number of epochs to train.",
    )

+    parser.add_argument(
+        "--start-epoch",
+        type=int,
+        default=0,
+        help="""Resume training from from this epoch.
+        If it is positive, it will load checkpoint from
+        tdnn/exp/epoch-{start_epoch-1}.pt
+        """,
+    )
+
    return parser


@ -92,8 +102,6 @@ def get_params() -> AttributeDict:
        - start_epoch:  If it is not zero, load checkpoint `start_epoch-1`
                        and continue training from that checkpoint.

-        - num_epochs:  Number of epochs to train.
-
        - best_train_loss: Best training loss so far. It is used to select
                           the model that has the lowest training loss. It is
                           updated during the training.
@ -420,6 +428,19 @@ def train_one_epoch(
                f"batch size: {batch_size}"
            )

+            if tb_writer is not None:
+                tb_writer.add_scalar(
+                    "train/current_loss",
+                    loss_cpu / params.train_frames,
+                    params.batch_idx_train,
+                )
+
+                tb_writer.add_scalar(
+                    "train/tot_avg_loss",
+                    tot_avg_loss,
+                    params.batch_idx_train,
+                )
+
        if batch_idx > 0 and batch_idx % params.valid_interval == 0:
            compute_validation_loss(
                params=params,
@ -434,6 +455,12 @@ def train_one_epoch(
                f" best valid loss: {params.best_valid_loss:.4f} "
                f"best valid epoch: {params.best_valid_epoch}"
            )
+            if tb_writer is not None:
+                tb_writer.add_scalar(
+                    "train/valid_loss",
+                    params.valid_loss,
+                    params.batch_idx_train,
+                )

    params.train_loss = tot_loss / tot_frames
				`@ -0,0 +1 @@`
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