Merge remote-tracking branch 'dan/master' into mmi-phone

.github/workflows/run-yesno-recipe.yml

@@ -56,7 +56,7 @@ jobs:
         run: |
           python3 -m pip install --upgrade pip black flake8
           python3 -m pip install -U pip
-          python3 -m pip install k2==1.4.dev20210822+cpu.torch1.7.1 -f https://k2-fsa.org/nightly/
+          python3 -m pip install k2==1.7.dev20210908+cpu.torch1.7.1 -f https://k2-fsa.org/nightly/
           python3 -m pip install torchaudio==0.7.2
           python3 -m pip install git+https://github.com/lhotse-speech/lhotse
 
@@ -69,21 +69,10 @@ jobs:
         run: |
           export PYTHONPATH=$PWD:$PYTHONPATH
           echo $PYTHONPATH
-          ls -lh
 
-          # The following three lines are for macOS
-          lib_path=$(python -c "from distutils.sysconfig import get_python_lib; print(get_python_lib())")
-          echo "lib_path: $lib_path"
-          export DYLD_LIBRARY_PATH=$lib_path:$DYLD_LIBRARY_PATH
-          ls -lh $lib_path
 
           cd egs/yesno/ASR
           ./prepare.sh
-          python3 ./tdnn/train.py --num-epochs 100
+          python3 ./tdnn/train.py
-          python3 ./tdnn/decode.py --epoch 99
+          python3 ./tdnn/decode.py
-          python3 ./tdnn/decode.py --epoch 95
-          python3 ./tdnn/decode.py --epoch 90
-          python3 ./tdnn/decode.py --epoch 80
-          python3 ./tdnn/decode.py --epoch 70
-          python3 ./tdnn/decode.py --epoch 60
           # TODO: Check that the WER is less than some value

.github/workflows/test.yml

@@ -32,7 +32,8 @@ jobs:
         os: [ubuntu-18.04, macos-10.15]
         python-version: [3.6, 3.7, 3.8, 3.9]
         torch: ["1.8.1"]
-        k2-version: ["1.4.dev20210822"]
+        k2-version: ["1.7.dev20210908"]
+
       fail-fast: false
 
     steps:

.gitignore

@@ -4,4 +4,4 @@ path.sh
 exp
 exp*/
 *.pt
-download/
+download

README.md

@@ -1,80 +1,61 @@
-
+<div align="center">
-# Table of Contents
+<img src="https://raw.githubusercontent.com/k2-fsa/icefall/master/docs/source/_static/logo.png" width=168>
-
+</div>
-- [Installation](#installation)
-  * [Install k2](#install-k2)
-  * [Install lhotse](#install-lhotse)
-  * [Install icefall](#install-icefall)
-- [Run recipes](#run-recipes)
 
 ## Installation
 
-`icefall` depends on [k2][k2] for FSA operations and [lhotse][lhotse] for
+Please refer to <https://icefall.readthedocs.io/en/latest/installation/index.html>
-data preparations. To use `icefall`, you have to install its dependencies first.
+for installation.
-The following subsections describe how to setup the environment.
-
-CAUTION: There are various ways to setup the environment. What we describe
-here is just one alternative.
-
-### Install k2
-
-Please refer to [k2's installation documentation][k2-install] to install k2.
-If you have any issues about installing k2, please open an issue at
-<https://github.com/k2-fsa/k2/issues>.
-
-### Install lhotse
-
-Please refer to [lhotse's installation documentation][lhotse-install] to install
-lhotse.
-
-### Install icefall
-
-`icefall` is a set of Python scripts. What you need to do is just to set
-the environment variable `PYTHONPATH`:
-
-```bash
-cd $HOME/open-source
-git clone https://github.com/k2-fsa/icefall
-cd icefall
-pip install -r requirements.txt
-export PYTHONPATH=$HOME/open-source/icefall:$PYTHONPATHON
-```
-
-To verify `icefall` was installed successfully, you can run:
-
-```bash
-python3 -c "import icefall; print(icefall.__file__)"
-```
-
-It should print the path to `icefall`.
 
 ## Recipes
 
-At present, two recipes are provided:
+Please refer to <https://icefall.readthedocs.io/en/latest/recipes/index.html>
+for more information.
 
-  - [LibriSpeech][LibriSpeech]
+We provide two recipes at present:
-  - [yesno][yesno] [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing)
 
-### Yesno
+  - [yesno][yesno]
+  - [LibriSpeech][librispeech]
 
-For the yesno recipe, training with 50 epochs takes less than 2 minutes using **CPU**.
+### yesno
 
-The WER is
+This is the simplest ASR recipe in `icefall` and can be run on CPU.
+Training takes less than 30 seconds and gives you the following WER:
 
 ```
 [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
 ```
+We do provide a Colab notebook for this recipe.
 
-## Use Pre-trained models
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing)
-
-See [egs/librispeech/ASR/conformer_ctc/README.md](egs/librispeech/ASR/conformer_ctc/README.md)
-for how to use pre-trained models.
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing)
 
 
-[yesno]: egs/yesno/ASR/README.md
+### LibriSpeech
-[LibriSpeech]: egs/librispeech/ASR/README.md
+
-[k2-install]: https://k2.readthedocs.io/en/latest/installation/index.html#
+We provide two models for this recipe: [conformer CTC model][LibriSpeech_conformer_ctc]
-[k2]: https://github.com/k2-fsa/k2
+and [TDNN LSTM CTC model][LibriSpeech_tdnn_lstm_ctc].
-[lhotse]: https://github.com/lhotse-speech/lhotse
+
-[lhotse-install]: https://lhotse.readthedocs.io/en/latest/getting-started.html#installation
+#### Conformer CTC Model
+
+The best WER we currently have is:
+
+||test-clean|test-other|
+|--|--|--|
+|WER| 2.57% | 5.94% |
+
+We provide a Colab notebook to run a pre-trained conformer CTC model: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing)
+
+#### TDNN LSTM CTC Model
+
+The WER for this model is:
+
+||test-clean|test-other|
+|--|--|--|
+|WER| 6.59% | 17.69% |
+
+We provide a Colab notebook to run a pre-trained TDNN LSTM CTC model:  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1kNmDXNMwREi0rZGAOIAOJo93REBuOTcd?usp=sharing)
+
+[LibriSpeech_tdnn_lstm_ctc]: egs/librispeech/ASR/tdnn_lstm_ctc
+[LibriSpeech_conformer_ctc]: egs/librispeech/ASR/conformer_ctc
+[yesno]: egs/yesno/ASR
+[librispeech]: egs/librispeech/ASR

docs/.gitignore

@@ -0,0 +1 @@
+build/

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)

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

docs/requirements.txt

@@ -0,0 +1,2 @@
+sphinx_rtd_theme
+sphinx

docs/source/conf.py

@@ -0,0 +1,76 @@
+# 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/",
+}

docs/source/contributing/code-style.rst

@@ -0,0 +1,67 @@
+.. _follow the code style:
+
+Follow the code style
+=====================
+
+We use the following tools to make the code style to be as consistent as possible:
+
+  - `black <https://github.com/psf/black>`_, to format the code
+  - `flake8 <https://github.com/PyCQA/flake8>`_, to check the style and quality of the code
+  - `isort <https://github.com/PyCQA/isort>`_, to sort ``imports``
+
+The following versions of the above tools are used:
+
+  - ``black == 12.6b0``
+  - ``flake8 == 3.9.2``
+  - ``isort == 5.9.2``
+
+After running the following commands:
+
+  .. code-block::
+
+    $ git clone https://github.com/k2-fsa/icefall
+    $ cd icefall
+    $ pip install pre-commit
+    $ pre-commit install
+
+it will run the following checks whenever you run ``git commit``, **automatically**:
+
+    .. figure:: images/pre-commit-check.png
+       :width: 600
+       :align: center
+
+       pre-commit hooks invoked by ``git commit`` (Failed).
+
+If any of the above checks failed, your ``git commit`` was not successful.
+Please fix any issues reported by the check tools.
+
+.. HINT::
+
+  Some of the check tools, i.e., ``black`` and ``isort`` will modify
+  the files to be commited **in-place**. So please run ``git status``
+  after failure to see which file has been modified by the tools
+  before you make any further changes.
+
+After fixing all the failures, run ``git commit`` again and
+it should succeed this time:
+
+    .. figure:: images/pre-commit-check-success.png
+       :width: 600
+       :align: center
+
+       pre-commit hooks invoked by ``git commit`` (Succeeded).
+
+If you want to check the style of your code before ``git commit``, you
+can do the following:
+
+  .. code-block:: bash
+
+    $ cd icefall
+    $ pip install black==21.6b0 flake8==3.9.2 isort==5.9.2
+    $ black --check your_changed_file.py
+    $ black your_changed_file.py  # modify it in-place
+    $
+    $ flake8 your_changed_file.py
+    $
+    $ isort --check your_changed_file.py  # modify it in-place
+    $ isort your_changed_file.py

docs/source/contributing/doc.rst

@@ -0,0 +1,45 @@
+Contributing to Documentation
+=============================
+
+We use `sphinx <https://www.sphinx-doc.org/en/master/>`_
+for documentation.
+
+Before writing documentation, you have to prepare the environment:
+
+  .. code-block:: bash
+
+    $ cd docs
+    $ pip install -r requirements.txt
+
+After setting up the environment, you are ready to write documentation.
+Please refer to `reStructuredText Primer <https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html>`_
+if you are not familiar with ``reStructuredText``.
+
+After writing some documentation, you can build the documentation **locally**
+to preview what it looks like if it is published:
+
+  .. code-block:: bash
+
+    $ cd docs
+    $ make html
+
+The generated documentation is in ``docs/build/html`` and can be viewed
+with the following commands:
+
+  .. code-block:: bash
+
+    $ cd docs/build/html
+    $ python3 -m http.server
+
+It will print::
+
+  Serving HTTP on 0.0.0.0 port 8000 (http://0.0.0.0:8000/) ...
+
+Open your browser, go to `<http://0.0.0.0:8000/>`_, and you will see
+the following:
+
+    .. figure:: images/doc-contrib.png
+       :width: 600
+       :align: center
+
+       View generated documentation locally with ``python3 -m http.server``.

docs/source/contributing/how-to-create-a-recipe.rst

@@ -0,0 +1,156 @@
+How to create a recipe
+======================
+
+.. HINT::
+
+  Please read :ref:`follow the code style` to adjust your code sytle.
+
+.. CAUTION::
+
+  ``icefall`` is designed to be as Pythonic as possible. Please use
+  Python in your recipe if possible.
+
+Data Preparation
+----------------
+
+We recommend you to prepare your training/test/validate dataset
+with `lhotse <https://github.com/lhotse-speech/lhotse>`_.
+
+Please refer to `<https://lhotse.readthedocs.io/en/latest/index.html>`_
+for how to create a recipe in ``lhotse``.
+
+.. HINT::
+
+  The ``yesno`` recipe in ``lhotse`` is a very good example.
+
+  Please refer to `<https://github.com/lhotse-speech/lhotse/pull/380>`_,
+  which shows how to add a new recipe to ``lhotse``.
+
+Suppose you would like to add a recipe for a dataset named ``foo``.
+You can do the following:
+
+.. code-block::
+
+  $ cd egs
+  $ mkdir -p foo/ASR
+  $ cd foo/ASR
+  $ touch prepare.sh
+  $ chmod +x prepare.sh
+
+If your dataset is very simple, please follow
+`egs/yesno/ASR/prepare.sh <https://github.com/k2-fsa/icefall/blob/master/egs/yesno/ASR/prepare.sh>`_
+to write your own ``prepare.sh``.
+Otherwise, please refer to
+`egs/librispeech/ASR/prepare.sh <https://github.com/k2-fsa/icefall/blob/master/egs/yesno/ASR/prepare.sh>`_
+to prepare your data.
+
+
+Training
+--------
+
+Assume you have a fancy model, called ``bar`` for the ``foo`` recipe, you can
+organize your files in the following way:
+
+.. code-block::
+
+  $ cd egs/foo/ASR
+  $ mkdir bar
+  $ cd bar
+  $ touch README.md model.py train.py decode.py asr_datamodule.py pretrained.py
+
+For instance , the ``yesno`` recipe has a ``tdnn`` model and its directory structure
+looks like the following:
+
+.. code-block:: bash
+
+  egs/yesno/ASR/tdnn/
+  |-- README.md
+  |-- asr_datamodule.py
+  |-- decode.py
+  |-- model.py
+  |-- pretrained.py
+  `-- train.py
+
+**File description**:
+
+  - ``README.md``
+
+    It contains information of this recipe, e.g., how to run it, what the WER is, etc.
+
+  - ``asr_datamodule.py``
+
+    It provides code to create PyTorch dataloaders with train/test/validation dataset.
+
+  - ``decode.py``
+
+    It takes as inputs the checkpoints saved during the training stage to decode the test
+    dataset(s).
+
+  - ``model.py``
+
+    It contains the definition of your fancy neural network model.
+
+  - ``pretrained.py``
+
+    We can use this script to do inference with a pre-trained model.
+
+  - ``train.py``
+
+    It contains training code.
+
+
+.. HINT::
+
+  Please take a look at
+
+    - `egs/yesno/tdnn <https://github.com/k2-fsa/icefall/tree/master/egs/yesno/ASR/tdnn>`_
+    - `egs/librispeech/tdnn_lstm_ctc <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/tdnn_lstm_ctc>`_
+    - `egs/librispeech/conformer_ctc <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/conformer_ctc>`_
+
+  to get a feel what the resulting files look like.
+
+.. NOTE::
+
+  Every model in a recipe is kept to be as self-contained as possible.
+  We tolerate duplicate code among different recipes.
+
+
+The training stage should be invocable by:
+
+  .. code-block::
+
+    $ cd egs/foo/ASR
+    $ ./bar/train.py
+    $ ./bar/train.py --help
+
+
+Decoding
+--------
+
+Please refer to
+
+  - `<https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/conformer_ctc/decode.py>`_
+
+    If your model is transformer/conformer based.
+
+  - `<https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py>`_
+
+    If your model is TDNN/LSTM based, i.e., there is no attention decoder.
+
+  - `<https://github.com/k2-fsa/icefall/blob/master/egs/yesno/ASR/tdnn/decode.py>`_
+
+    If there is no LM rescoring.
+
+The decoding stage should be invocable by:
+
+  .. code-block::
+
+    $ cd egs/foo/ASR
+    $ ./bar/decode.py
+    $ ./bar/decode.py --help
+
+Pre-trained model
+-----------------
+
+Please demonstrate how to use your model for inference in ``egs/foo/ASR/bar/pretrained.py``.
+If possible, please consider creating a Colab notebook to show that.

docs/source/contributing/index.rst

@@ -0,0 +1,22 @@
+Contributing
+============
+
+Contributions to ``icefall`` are very welcomed.
+There are many possible ways to make contributions and
+two of them are:
+
+  - To write documentation
+  - To write code
+
+    - (1) To follow the code style in the repository
+    - (2) To write a new recipe
+
+In this page, we describe how to contribute documentation
+and code to ``icefall``.
+
+.. toctree::
+   :maxdepth: 2
+
+   doc
+   code-style
+   how-to-create-a-recipe

docs/source/index.rst

@@ -0,0 +1,25 @@
+.. 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
+   contributing/index

docs/source/installation/images/device-CPU_CUDA-orange.svg

@@ -0,0 +1 @@
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docs/source/installation/images/k2-v-1.7.svg

@@ -0,0 +1 @@
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docs/source/installation/index.rst

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+.. _install icefall:
+
+Installation
+============
+
+- |os|
+- |device|
+- |python_versions|
+- |torch_versions|
+- |k2_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
+
+.. |k2_versions| image:: ./images/k2-v-1.7.svg
+  :alt: Supported k2 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``.
+
+.. CAUTION::
+
+  You need to install ``k2`` with a version at least **v1.7**.
+
+.. 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>`_.
+
+.. CAUTION::
+
+  If you have installed ``torchaudio``, please consider uninstalling it before
+  installing ``lhotse``. Otherwise, it may update your already installed PyTorch.
+
+(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
+
+(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/ASR
+  $ ./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``!

docs/source/recipes/index.rst

@@ -0,0 +1,17 @@
+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

docs/source/recipes/librispeech.rst

@@ -0,0 +1,10 @@
+LibriSpeech
+===========
+
+We provide the following models for the LibriSpeech dataset:
+
+.. toctree::
+   :maxdepth: 2
+
+   librispeech/tdnn_lstm_ctc
+   librispeech/conformer_ctc

docs/source/recipes/librispeech/conformer_ctc.rst

@@ -0,0 +1,627 @@
+Confromer CTC
+=============
+
+This tutorial shows you how to run a conformer ctc model
+with the `LibriSpeech <https://www.openslr.org/12>`_ dataset.
+
+
+.. HINT::
+
+  We assume you have read the page :ref:`install icefall` and have setup
+  the environment for ``icefall``.
+
+.. HINT::
+
+  We recommend you to use a GPU or several GPUs to run this recipe.
+
+In this tutorial, you will learn:
+
+  - (1) How to prepare data for training and decoding
+  - (2) How to start the training, either with a single GPU or multiple GPUs
+  - (3) How to do decoding after training, with n-gram LM rescoring and attention decoder rescoring
+  - (4) How to use a pre-trained model, provided by us
+
+Data preparation
+----------------
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/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
+
+.. HINT::
+
+  If you have pre-downloaded the `LibriSpeech <https://www.openslr.org/12>`_
+  dataset and the `musan <http://www.openslr.org/17/>`_ dataset, say,
+  they are saved in ``/tmp/LibriSpeech`` and ``/tmp/musan``, you can modify
+  the ``dl_dir`` variable in ``./prepare.sh`` to point to ``/tmp`` so that
+  ``./prepare.sh`` won't re-download them.
+
+.. NOTE::
+
+  All generated files by ``./prepare.sh``, e.g., features, lexicon, etc,
+  are saved in ``./data`` directory.
+
+
+Training
+--------
+
+Configurable options
+~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ ./conformer_ctc/train.py --help
+
+shows you the training options that can be passed from the commandline.
+The following options are used quite often:
+
+  - ``--full-libri``
+
+    If it's True, the training part uses all the training data, i.e.,
+    960 hours. Otherwise, the training part uses only the subset
+    ``train-clean-100``, which has 100 hours of training data.
+
+    .. CAUTION::
+
+      The training set is perturbed by speed with two factors: 0.9 and 1.1.
+      If ``--full-libri`` is True, each epoch actually processes
+      ``3x960 == 2880`` hours of data.
+
+  - ``--num-epochs``
+
+    It is the number of epochs to train. For instance,
+    ``./conformer_ctc/train.py --num-epochs 30`` trains for 30 epochs
+    and generates ``epoch-0.pt``, ``epoch-1.pt``, ..., ``epoch-29.pt``
+    in the folder ``./conformer_ctc/exp``.
+
+  - ``--start-epoch``
+
+    It's used to resume training.
+    ``./conformer_ctc/train.py --start-epoch 10`` loads the
+    checkpoint ``./conformer_ctc/exp/epoch-9.pt`` and starts
+    training from epoch 10, based on the state from epoch 9.
+
+  - ``--world-size``
+
+    It is used for multi-GPU single-machine DDP training.
+
+      - (a) If it is 1, then no DDP training is used.
+
+      - (b) If it is 2, then GPU 0 and GPU 1 are used for DDP training.
+
+    The following shows some use cases with it.
+
+      **Use case 1**: You have 4 GPUs, but you only want to use GPU 0 and
+      GPU 2 for training. You can do the following:
+
+        .. code-block:: bash
+
+          $ cd egs/librispeech/ASR
+          $ export CUDA_VISIBLE_DEVICES="0,2"
+          $ ./conformer_ctc/train.py --world-size 2
+
+      **Use case 2**: You have 4 GPUs and you want to use all of them
+      for training. You can do the following:
+
+        .. code-block:: bash
+
+          $ cd egs/librispeech/ASR
+          $ ./conformer_ctc/train.py --world-size 4
+
+      **Use case 3**: You have 4 GPUs but you only want to use GPU 3
+      for training. You can do the following:
+
+        .. code-block:: bash
+
+          $ cd egs/librispeech/ASR
+          $ export CUDA_VISIBLE_DEVICES="3"
+          $ ./conformer_ctc/train.py --world-size 1
+
+    .. CAUTION::
+
+      Only multi-GPU single-machine DDP training is implemented at present.
+      Multi-GPU multi-machine DDP training will be added later.
+
+  - ``--max-duration``
+
+    It specifies the number of seconds over all utterances in a
+    batch, before **padding**.
+    If you encounter CUDA OOM, please reduce it. For instance, if
+    your are using V100 NVIDIA GPU, we recommend you to set it to ``200``.
+
+    .. HINT::
+
+      Due to padding, the number of seconds of all utterances in a
+      batch will usually be larger than ``--max-duration``.
+
+      A larger value for ``--max-duration`` may cause OOM during training,
+      while a smaller value may increase the training time. You have to
+      tune it.
+
+
+Pre-configured options
+~~~~~~~~~~~~~~~~~~~~~~
+
+There are some training options, e.g., learning rate,
+number of warmup steps, results dir, etc,
+that are not passed from the commandline.
+They are pre-configured by the function ``get_params()`` in
+`conformer_ctc/train.py <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/conformer_ctc/train.py>`_
+
+You don't need to change these pre-configured parameters. If you really need to change
+them, please modify ``./conformer_ctc/train.py`` directly.
+
+
+Training logs
+~~~~~~~~~~~~~
+
+Training logs and checkpoints are saved in ``conformer_ctc/exp``.
+You will find the following files in that directory:
+
+  - ``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
+
+        $ ./conformer_ctc/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 conformer_ctc/exp/tensorboard
+        $ tensorboard dev upload --logdir . --description "Conformer CTC training for LibriSpeech 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/lzGnETjwRxC3yghNMd4kPw/
+
+        [2021-08-24T16:42:43] Started scanning logdir.
+        Uploading 4540 scalars...
+
+    Note there is a URL in the above output, click it and you will see
+    the following screenshot:
+
+      .. figure:: images/librispeech-conformer-ctc-tensorboard-log.png
+         :width: 600
+         :alt: TensorBoard screenshot
+         :align: center
+         :target: https://tensorboard.dev/experiment/lzGnETjwRxC3yghNMd4kPw/
+
+         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.
+
+Usage examples
+~~~~~~~~~~~~~~
+
+The following shows typical use cases:
+
+**Case 1**
+^^^^^^^^^^
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ ./conformer_ctc/train.py --max-duration 200 --full-libri 0
+
+It uses ``--max-duration`` of 200 to avoid OOM.  Also, it uses only
+a subset of the LibriSpeech data for training.
+
+
+**Case 2**
+^^^^^^^^^^
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ export CUDA_VISIBLE_DEVICES="0,3"
+  $ ./conformer_ctc/train.py --world-size 2
+
+It uses GPU 0 and GPU 3 for DDP training.
+
+**Case 3**
+^^^^^^^^^^
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ ./conformer_ctc/train.py --num-epochs 10 --start-epoch 3
+
+It loads checkpoint ``./conformer_ctc/exp/epoch-2.pt`` and starts
+training from epoch 3. Also, it trains for 10 epochs.
+
+Decoding
+--------
+
+The decoding part uses checkpoints saved by the training part, so you have
+to run the training part first.
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ ./conformer_ctc/decode.py --help
+
+shows the options for decoding.
+
+The commonly used options are:
+
+  - ``--method``
+
+    This specifies the decoding method.
+
+    The following command uses attention decoder for rescoring:
+
+    .. code-block::
+
+      $ cd egs/librispeech/ASR
+      $ ./conformer_ctc/decode.py --method attention-decoder --max-duration 30 --lattice-score-scale 0.5
+
+  - ``--lattice-score-scale``
+
+    It is used to scale down lattice scores so that there are more unique
+    paths for rescoring.
+
+  - ``--max-duration``
+
+    It has the same meaning as the one during training. A larger
+    value may cause OOM.
+
+Pre-trained Model
+-----------------
+
+We have uploaded a pre-trained model to
+`<https://huggingface.co/pkufool/icefall_asr_librispeech_conformer_ctc>`_.
+
+We describe how to use the pre-trained model to transcribe a sound file or
+multiple sound files in the following.
+
+Install kaldifeat
+~~~~~~~~~~~~~~~~~
+
+`kaldifeat <https://github.com/csukuangfj/kaldifeat>`_ is used to
+extract features for a single sound file or multiple sound files
+at the same time.
+
+Please refer to `<https://github.com/csukuangfj/kaldifeat>`_ for installation.
+
+Download the pre-trained model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The following commands describe how to download the pre-trained model:
+
+.. code-block::
+
+  $ cd egs/librispeech/ASR
+  $ mkdir tmp
+  $ cd tmp
+  $ git lfs install
+  $ git clone https://huggingface.co/pkufool/icefall_asr_librispeech_conformer_ctc
+
+.. 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/librispeech/ASR
+  $ tree tmp
+
+.. code-block:: bash
+
+  tmp
+  `-- icefall_asr_librispeech_conformer_ctc
+      |-- README.md
+      |-- data
+      |   |-- lang_bpe
+      |   |   |-- HLG.pt
+      |   |   |-- bpe.model
+      |   |   |-- tokens.txt
+      |   |   `-- words.txt
+      |   `-- lm
+      |       `-- G_4_gram.pt
+      |-- exp
+      |   `-- pretrained.pt
+      `-- test_wavs
+          |-- 1089-134686-0001.flac
+          |-- 1221-135766-0001.flac
+          |-- 1221-135766-0002.flac
+          `-- trans.txt
+
+  6 directories, 11 files
+
+**File descriptions**:
+
+  - ``data/lang_bpe/HLG.pt``
+
+      It is the decoding graph.
+
+  - ``data/lang_bpe/bpe.model``
+
+      It is a sentencepiece model. You can use it to reproduce our results.
+
+  - ``data/lang_bpe/tokens.txt``
+
+      It contains tokens and their IDs, generated from ``bpe.model``.
+      Provided only for convenience so that you can look up the SOS/EOS ID easily.
+
+  - ``data/lang_bpe/words.txt``
+
+      It contains words and their IDs.
+
+  - ``data/lm/G_4_gram.pt``
+
+      It is a 4-gram LM, used for n-gram LM rescoring.
+
+  - ``exp/pretrained.pt``
+
+      It contains pre-trained model parameters, obtained by averaging
+      checkpoints from ``epoch-15.pt`` to ``epoch-34.pt``.
+      Note: We have removed optimizer ``state_dict`` to reduce file size.
+
+  - ``test_waves/*.flac``
+
+      It contains some test sound files from LibriSpeech ``test-clean`` dataset.
+
+  - `test_waves/trans.txt`
+
+      It contains the reference transcripts for the sound files in `test_waves/`.
+
+The information of the test sound files is listed below:
+
+.. code-block:: bash
+
+  $ soxi tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/*.flac
+
+  Input File     : 'tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac'
+  Channels       : 1
+  Sample Rate    : 16000
+  Precision      : 16-bit
+  Duration       : 00:00:06.62 = 106000 samples ~ 496.875 CDDA sectors
+  File Size      : 116k
+  Bit Rate       : 140k
+  Sample Encoding: 16-bit FLAC
+
+  Input File     : 'tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac'
+  Channels       : 1
+  Sample Rate    : 16000
+  Precision      : 16-bit
+  Duration       : 00:00:16.71 = 267440 samples ~ 1253.62 CDDA sectors
+  File Size      : 343k
+  Bit Rate       : 164k
+  Sample Encoding: 16-bit FLAC
+
+  Input File     : 'tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac'
+  Channels       : 1
+  Sample Rate    : 16000
+  Precision      : 16-bit
+  Duration       : 00:00:04.83 = 77200 samples ~ 361.875 CDDA sectors
+  File Size      : 105k
+  Bit Rate       : 174k
+  Sample Encoding: 16-bit FLAC
+
+  Total Duration of 3 files: 00:00:28.16
+
+Usage
+~~~~~
+
+.. code-block::
+
+  $ cd egs/librispeech/ASR
+  $ ./conformer_ctc/pretrained.py --help
+
+displays the help information.
+
+It supports three decoding methods:
+
+  - HLG decoding
+  - HLG + n-gram LM rescoring
+  - HLG + n-gram LM rescoring + attention decoder rescoring
+
+HLG decoding
+^^^^^^^^^^^^
+
+HLG decoding uses the best path of the decoding lattice as the decoding result.
+
+The command to run HLG decoding is:
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ ./conformer_ctc/pretrained.py \
+    --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretrained.pt \
+    --words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \
+    --HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \
+    ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac \
+    ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac \
+    ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac
+
+The output is given below:
+
+.. code-block::
+
+  2021-08-20 11:03:05,712 INFO [pretrained.py:217] device: cuda:0
+  2021-08-20 11:03:05,712 INFO [pretrained.py:219] Creating model
+  2021-08-20 11:03:11,345 INFO [pretrained.py:238] Loading HLG from ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt
+  2021-08-20 11:03:18,442 INFO [pretrained.py:255] Constructing Fbank computer
+  2021-08-20 11:03:18,444 INFO [pretrained.py:265] Reading sound files: ['./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac']
+  2021-08-20 11:03:18,507 INFO [pretrained.py:271] Decoding started
+  2021-08-20 11:03:18,795 INFO [pretrained.py:300] Use HLG decoding
+  2021-08-20 11:03:19,149 INFO [pretrained.py:339]
+  ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac:
+  AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS
+
+  ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac:
+  GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED
+  BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN
+
+  ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac:
+  YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION
+
+  2021-08-20 11:03:19,149 INFO [pretrained.py:341] Decoding Done
+
+HLG decoding + LM rescoring
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+It uses an n-gram LM to rescore the decoding lattice and the best
+path of the rescored lattice is the decoding result.
+
+The command to run HLG decoding + LM rescoring is:
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ ./conformer_ctc/pretrained.py \
+    --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretrained.pt \
+    --words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \
+    --HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \
+    --method whole-lattice-rescoring \
+    --G ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt \
+    --ngram-lm-scale 0.8 \
+    ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac \
+    ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac \
+    ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac
+
+Its output is:
+
+.. code-block::
+
+  2021-08-20 11:12:17,565 INFO [pretrained.py:217] device: cuda:0
+  2021-08-20 11:12:17,565 INFO [pretrained.py:219] Creating model
+  2021-08-20 11:12:23,728 INFO [pretrained.py:238] Loading HLG from ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt
+  2021-08-20 11:12:30,035 INFO [pretrained.py:246] Loading G from ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt
+  2021-08-20 11:13:10,779 INFO [pretrained.py:255] Constructing Fbank computer
+  2021-08-20 11:13:10,787 INFO [pretrained.py:265] Reading sound files: ['./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac']
+  2021-08-20 11:13:10,798 INFO [pretrained.py:271] Decoding started
+  2021-08-20 11:13:11,085 INFO [pretrained.py:305] Use HLG decoding + LM rescoring
+  2021-08-20 11:13:11,736 INFO [pretrained.py:339]
+  ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac:
+  AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS
+
+  ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac:
+  GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED
+  BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN
+
+  ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac:
+  YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION
+
+  2021-08-20 11:13:11,737 INFO [pretrained.py:341] Decoding Done
+
+HLG decoding + LM rescoring + attention decoder rescoring
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+It uses an n-gram LM to rescore the decoding lattice, extracts
+n paths from the rescored lattice, recores the extracted paths with
+an attention decoder. The path with the highest score is the decoding result.
+
+The command to run HLG decoding + LM rescoring + attention decoder rescoring is:
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ ./conformer_ctc/pretrained.py \
+    --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretrained.pt \
+    --words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \
+    --HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \
+    --method attention-decoder \
+    --G ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt \
+    --ngram-lm-scale 1.3 \
+    --attention-decoder-scale 1.2 \
+    --lattice-score-scale 0.5 \
+    --num-paths 100 \
+    --sos-id 1 \
+    --eos-id 1 \
+    ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac \
+    ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac \
+    ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac
+
+The output is below:
+
+.. code-block::
+
+  2021-08-20 11:19:11,397 INFO [pretrained.py:217] device: cuda:0
+  2021-08-20 11:19:11,397 INFO [pretrained.py:219] Creating model
+  2021-08-20 11:19:17,354 INFO [pretrained.py:238] Loading HLG from ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt
+  2021-08-20 11:19:24,615 INFO [pretrained.py:246] Loading G from ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt
+  2021-08-20 11:20:04,576 INFO [pretrained.py:255] Constructing Fbank computer
+  2021-08-20 11:20:04,584 INFO [pretrained.py:265] Reading sound files: ['./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac']
+  2021-08-20 11:20:04,595 INFO [pretrained.py:271] Decoding started
+  2021-08-20 11:20:04,854 INFO [pretrained.py:313] Use HLG + LM rescoring + attention decoder rescoring
+  2021-08-20 11:20:05,805 INFO [pretrained.py:339]
+  ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac:
+  AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS
+
+  ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac:
+  GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED
+  BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN
+
+  ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac:
+  YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION
+
+  2021-08-20 11:20:05,805 INFO [pretrained.py:341] Decoding Done
+
+Colab notebook
+--------------
+
+We do provide a colab notebook for this recipe showing how to use a pre-trained model.
+
+|librispeech asr conformer ctc colab notebook|
+
+.. |librispeech asr conformer ctc colab notebook| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing
+
+.. HINT::
+
+  Due to limited memory provided by Colab, you have to upgrade to Colab Pro to
+  run ``HLG decoding + LM rescoring`` and
+  ``HLG decoding + LM rescoring + attention decoder rescoring``.
+  Otherwise, you can only run ``HLG decoding`` with Colab.
+
+**Congratulations!** You have finished the librispeech ASR recipe with
+conformer CTC models in ``icefall``.

docs/source/recipes/librispeech/tdnn_lstm_ctc.rst

@@ -0,0 +1,322 @@
+TDNN-LSTM-CTC
+=============
+
+This tutorial shows you how to run a TDNN-LSTM-CTC model with the `LibriSpeech <https://www.openslr.org/12>`_ dataset.
+
+
+.. HINT::
+
+  We assume you have read the page :ref:`install icefall` and have setup
+  the environment for ``icefall``.
+
+
+Data preparation
+----------------
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/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/librispeech/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
+--------
+
+Now describing the training of TDNN-LSTM-CTC model, contained in
+the `tdnn_lstm_ctc <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/tdnn_lstm_ctc>`_
+folder.
+
+The command to run the training part is:
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ export CUDA_VISIBLE_DEVICES="0,1,2,3"
+  $ ./tdnn_lstm_ctc/train.py --world-size 4
+
+By default, it will run ``20`` epochs. Training logs and checkpoints are saved
+in ``tdnn_lstm_ctc/exp``.
+
+In ``tdnn_lstm_ctc/exp``, you will find the following files:
+
+  - ``epoch-0.pt``, ``epoch-1.pt``, ..., ``epoch-19.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_lstm_ctc/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_lstm_ctc/exp/tensorboard
+        $ tensorboard dev upload --logdir . --description "TDNN LSTM training for librispeech with icefall"
+
+  - ``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.
+
+
+To see available training options, you can use:
+
+.. code-block:: bash
+
+  $ ./tdnn_lstm_ctc/train.py --help
+
+Other training options, e.g., learning rate, results dir, etc., are
+pre-configured in the function ``get_params()``
+in `tdnn_lstm_ctc/train.py <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/tdnn_lstm_ctc/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="0"
+  $ ./tdnn_lstm_ctc/decode.py
+
+You will see the WER in the output log.
+
+Decoded results are saved in ``tdnn_lstm_ctc/exp``.
+
+.. code-block:: bash
+
+  $ ./tdnn_lstm_ctc/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_lstm_ctc/decode.py --epoch 10`` means to use
+    ``./tdnn_lstm_ctc/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_lstm_ctc/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_lstm_ctc/decode.py --export 1``, the code
+    will save the averaged model to ``tdnn_lstm_ctc/exp/pretrained.pt``.
+    See :ref:`tdnn_lstm_ctc use a pre-trained model` for how to use it.
+
+.. HINT::
+
+   There are several decoding methods provided in `tdnn_lstm_ctc/decode.py <https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/tdnn_lstm_ctc/train.py>`_, you can change the decoding method by modifying ``method`` parameter in function ``get_params()``.
+
+
+.. _tdnn_lstm_ctc use a pre-trained model:
+
+Pre-trained Model
+-----------------
+
+We have uploaded the pre-trained model to
+`<https://huggingface.co/pkufool/icefall_asr_librispeech_tdnn-lstm_ctc>`_.
+
+The following shows you how to use the pre-trained model.
+
+Download the pre-trained model
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+  $ cd egs/librispeech/ASR
+  $ mkdir tmp
+  $ cd tmp
+  $ git lfs install
+  $ git clone https://huggingface.co/pkufool/icefall_asr_librispeech_tdnn-lstm_ctc
+
+.. 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/librispeech/ASR
+  $ tree tmp
+
+.. code-block:: bash
+
+  tmp/
+  `-- icefall_asr_librispeech_tdnn-lstm_ctc
+      |-- README.md
+      |-- data
+      |   |-- lang_phone
+      |   |   |-- HLG.pt
+      |   |   |-- tokens.txt
+      |   |   `-- words.txt
+      |   `-- lm
+      |       `-- G_4_gram.pt
+      |-- exp
+      |   `-- pretrained.pt
+      `-- test_wavs
+          |-- 1089-134686-0001.flac
+          |-- 1221-135766-0001.flac
+          |-- 1221-135766-0002.flac
+          `-- trans.txt
+
+  6 directories, 10 files
+
+
+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/librispeech/ASR
+  $ ./tdnn_lstm_ctc/pretrained.py --help
+
+shows the usage information of ``./tdnn_lstm_ctc/pretrained.py``.
+
+To decode with ``1best`` method, we can use:
+
+.. code-block:: bash
+
+  ./tdnn_lstm_ctc/pretrained.py \
+    --checkpoint ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/exp/pretraind.pt \
+    --words-file ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/words.txt \
+    --HLG ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt \
+    ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac \
+    ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac \
+    ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac
+
+The output is:
+
+.. code-block::
+
+  2021-08-24 16:57:13,315 INFO [pretrained.py:168] device: cuda:0
+  2021-08-24 16:57:13,315 INFO [pretrained.py:170] Creating model
+  2021-08-24 16:57:18,331 INFO [pretrained.py:182] Loading HLG from ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt
+  2021-08-24 16:57:27,581 INFO [pretrained.py:199] Constructing Fbank computer
+  2021-08-24 16:57:27,584 INFO [pretrained.py:209] Reading sound files: ['./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac']
+  2021-08-24 16:57:27,599 INFO [pretrained.py:215] Decoding started
+  2021-08-24 16:57:27,791 INFO [pretrained.py:245] Use HLG decoding
+  2021-08-24 16:57:28,098 INFO [pretrained.py:266]
+  ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac:
+  AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS
+
+  ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac:
+  GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONORED BOSOM TO CONNECT HER PARENT FOREVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN
+
+  ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac:
+  YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION
+
+
+  2021-08-24 16:57:28,099 INFO [pretrained.py:268] Decoding Done
+
+
+To decode with ``whole-lattice-rescoring`` methond, you can use
+
+.. code-block:: bash
+
+  ./tdnn_lstm_ctc/pretrained.py \
+    --checkpoint ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/exp/pretraind.pt \
+    --words-file ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/words.txt \
+    --HLG ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt \
+    --method whole-lattice-rescoring \
+    --G ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lm/G_4_gram.pt \
+    --ngram-lm-scale 0.8 \
+    ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac \
+    ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac \
+    ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac
+
+The decoding output is:
+
+.. code-block::
+
+  2021-08-24 16:39:24,725 INFO [pretrained.py:168] device: cuda:0
+  2021-08-24 16:39:24,725 INFO [pretrained.py:170] Creating model
+  2021-08-24 16:39:29,403 INFO [pretrained.py:182] Loading HLG from ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt
+  2021-08-24 16:39:40,631 INFO [pretrained.py:190] Loading G from ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lm/G_4_gram.pt
+  2021-08-24 16:39:53,098 INFO [pretrained.py:199] Constructing Fbank computer
+  2021-08-24 16:39:53,107 INFO [pretrained.py:209] Reading sound files: ['./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac']
+  2021-08-24 16:39:53,121 INFO [pretrained.py:215] Decoding started
+  2021-08-24 16:39:53,443 INFO [pretrained.py:250] Use HLG decoding + LM rescoring
+  2021-08-24 16:39:54,010 INFO [pretrained.py:266]
+  ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac:
+  AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS
+
+  ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac:
+  GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONORED BOSOM TO CONNECT HER PARENT FOREVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN
+
+  ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac:
+  YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION
+
+
+  2021-08-24 16:39:54,010 INFO [pretrained.py:268] Decoding Done
+
+
+Colab notebook
+--------------
+
+We provide a colab notebook for decoding with pre-trained model.
+
+|librispeech tdnn_lstm_ctc colab notebook|
+
+.. |librispeech tdnn_lstm_ctc colab notebook| image:: https://colab.research.google.com/assets/colab-badge.svg
+   :target: https://colab.research.google.com/drive/1kNmDXNMwREi0rZGAOIAOJo93REBuOTcd
+
+
+**Congratulations!** You have finished the TDNN-LSTM-CTC recipe on librispeech in ``icefall``.

docs/source/recipes/yesno.rst

@@ -0,0 +1,445 @@
+yesno
+=====
+
+This page shows you how to run the `yesno <https://www.openslr.org/1>`_ 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``.

egs/librispeech/ASR/README.md

@@ -1,64 +1,3 @@
 
-## Data preparation
+Please refer to <https://icefall.readthedocs.io/en/latest/recipes/librispeech.html>
-
+for how to run models in this recipe.
-If you want to use `./prepare.sh` to download everything for you,
-you can just run
-
-```
-./prepare.sh
-```
-
-If you have pre-downloaded the LibriSpeech dataset, please
-read `./prepare.sh` and modify it to point to the location
-of your dataset so that it won't re-download it. After modification,
-please run
-
-```
-./prepare.sh
-```
-
-The script `./prepare.sh` prepares features, lexicon, LMs, etc.
-All generated files are saved in the folder `./data`.
-
-**HINT:** `./prepare.sh` supports options `--stage` and `--stop-stage`.
-
-## TDNN-LSTM CTC training
-
-The folder `tdnn_lstm_ctc` contains scripts for CTC training
-with TDNN-LSTM models.
-
-Pre-configured parameters for training and decoding are set in the function
-`get_params()` within `tdnn_lstm_ctc/train.py`
-and `tdnn_lstm_ctc/decode.py`.
-
-Parameters that can be passed from the command-line can be found by
-
-```
-./tdnn_lstm_ctc/train.py --help
-./tdnn_lstm_ctc/decode.py --help
-```
-
-If you have 4 GPUs on a machine and want to use GPU 0, 2, 3 for
-mutli-GPU training, you can run
-
-```
-export CUDA_VISIBLE_DEVICES="0,2,3"
-./tdnn_lstm_ctc/train.py \
-  --master-port 12345 \
-  --world-size 3
-```
-
-If you want to decode by averaging checkpoints `epoch-8.pt`,
-`epoch-9.pt` and `epoch-10.pt`, you can run
-
-```
-./tdnn_lstm_ctc/decode.py \
-  --epoch 10 \
-  --avg 3
-```
-
-## Conformer CTC training
-
-The folder `conformer-ctc` contains scripts for CTC training
-with conformer models. The steps of running the training and
-decoding are similar to `tdnn_lstm_ctc`.

egs/librispeech/ASR/RESULTS.md

@@ -6,7 +6,7 @@
 
 TensorBoard log is available at https://tensorboard.dev/experiment/GnRzq8WWQW62dK4bklXBTg/#scalars
 
-Pretrained model is available at https://huggingface.co/pkufool/conformer_ctc
+Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_librispeech_conformer_ctc
 
 The best decoding results (WER) are listed below, we got this results by averaging models from epoch 15 to 34, and using `attention-decoder` decoder with num_paths equals to 100.
 
@@ -21,3 +21,51 @@ To get more unique paths, we scaled the lattice.scores with 0.5 (see https://git
 |test-clean|1.3|1.2|
 |test-other|1.2|1.1|
 
+You can use the following commands to reproduce our results:
+
+```bash
+git clone https://github.com/k2-fsa/icefall
+cd icefall
+
+# It was using ef233486, you may not need to switch to it
+# git checkout ef233486
+
+cd egs/librispeech/ASR
+./prepare.sh
+
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+python conformer_ctc/train.py --bucketing-sampler True \
+                              --concatenate-cuts False \
+                              --max-duration 200 \
+                              --full-libri True \
+                              --world-size 4
+
+python conformer_ctc/decode.py --lattice-score-scale 0.5 \
+                               --epoch 34 \
+                               --avg 20 \
+                               --method attention-decoder \
+                               --max-duration 20 \
+                               --num-paths 100
+```
+
+### LibriSpeech training results (Tdnn-Lstm)
+#### 2021-08-24
+
+(Wei Kang): Result of phone based Tdnn-Lstm model.
+
+Icefall version: https://github.com/k2-fsa/icefall/commit/caa0b9e9425af27e0c6211048acb55a76ed5d315
+
+Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_librispeech_tdnn-lstm_ctc
+
+The best decoding results (WER) are listed below, we got this results by averaging models from epoch 19 to 14, and using `whole-lattice-rescoring` decoding method.
+
+||test-clean|test-other|
+|--|--|--|
+|WER| 6.59% | 17.69% |
+
+We searched the lm_score_scale for best results, the scales that produced the WER above are also listed below.
+
+||lm_scale|
+|--|--|
+|test-clean|0.8|
+|test-other|0.9|

egs/librispeech/ASR/conformer_ctc/README.md

@@ -1,351 +1,3 @@
-
+Please visit
-# How to use a pre-trained model to transcribe a sound file or multiple sound files
+<https://icefall.readthedocs.io/en/latest/recipes/librispeech/conformer_ctc.html>
-
+for how to run this recipe.
-(See the bottom of this document for the link to a colab notebook.)
-
-You need to prepare 4 files:
-
-  - a model checkpoint file, e.g., epoch-20.pt
-  - HLG.pt, the decoding graph
-  - words.txt, the word symbol table
-  - a sound file, whose sampling rate has to be 16 kHz.
-    Supported formats are those supported by `torchaudio.load()`,
-    e.g., wav and flac.
-
-Also, you need to install `kaldifeat`. Please refer to
-<https://github.com/csukuangfj/kaldifeat> for installation.
-
-```bash
-./conformer_ctc/pretrained.py --help
-```
-
-displays the help information.
-
-## HLG decoding
-
-Once you have the above files ready and have `kaldifeat` installed,
-you can run:
-
-```bash
-./conformer_ctc/pretrained.py \
-  --checkpoint /path/to/your/checkpoint.pt \
-  --words-file /path/to/words.txt \
-  --HLG /path/to/HLG.pt \
-  /path/to/your/sound.wav
-```
-
-and you will see the transcribed result.
-
-If you want to transcribe multiple files at the same time, you can use:
-
-```bash
-./conformer_ctc/pretrained.py \
-  --checkpoint /path/to/your/checkpoint.pt \
-  --words-file /path/to/words.txt \
-  --HLG /path/to/HLG.pt \
-  /path/to/your/sound1.wav \
-  /path/to/your/sound2.wav \
-  /path/to/your/sound3.wav
-```
-
-**Note**: This is the fastest decoding method.
-
-## HLG decoding + LM rescoring
-
-`./conformer_ctc/pretrained.py` also supports `whole lattice LM rescoring`
-and `attention decoder rescoring`.
-
-To use whole lattice LM rescoring, you also need the following files:
-
-  - G.pt, e.g., `data/lm/G_4_gram.pt` if you have run `./prepare.sh`
-
-The command to run decoding with LM rescoring is:
-
-```bash
-./conformer_ctc/pretrained.py \
-  --checkpoint /path/to/your/checkpoint.pt \
-  --words-file /path/to/words.txt \
-  --HLG /path/to/HLG.pt \
-  --method whole-lattice-rescoring \
-  --G data/lm/G_4_gram.pt \
-  --ngram-lm-scale 0.8 \
-  /path/to/your/sound1.wav \
-  /path/to/your/sound2.wav \
-  /path/to/your/sound3.wav
-```
-
-## HLG Decoding + LM rescoring + attention decoder rescoring
-
-To use attention decoder for rescoring, you need the following extra information:
-
-  - sos token ID
-  - eos token ID
-
-The command to run decoding with attention decoder rescoring is:
-
-```bash
-./conformer_ctc/pretrained.py \
-  --checkpoint /path/to/your/checkpoint.pt \
-  --words-file /path/to/words.txt \
-  --HLG /path/to/HLG.pt \
-  --method attention-decoder \
-  --G data/lm/G_4_gram.pt \
-  --ngram-lm-scale 1.3 \
-  --attention-decoder-scale 1.2 \
-  --lattice-score-scale 0.5 \
-  --num-paths 100 \
-  --sos-id 1 \
-  --eos-id 1 \
-  /path/to/your/sound1.wav \
-  /path/to/your/sound2.wav \
-  /path/to/your/sound3.wav
-```
-
-# Decoding with a pre-trained model in action
-
-We have uploaded a pre-trained model to <https://huggingface.co/pkufool/conformer_ctc>
-
-The following shows the steps about the usage of the provided pre-trained model.
-
-### (1) Download the pre-trained model
-
-```bash
-sudo apt-get install git-lfs
-cd /path/to/icefall/egs/librispeech/ASR
-git lfs install
-mkdir tmp
-cd tmp
-git clone https://huggingface.co/pkufool/conformer_ctc
-```
-
-**CAUTION**: You have to install `git-lfst` to download the pre-trained model.
-
-You will find the following files:
-
-```
-tmp
-`-- conformer_ctc
-    |-- README.md
-    |-- data
-    |   |-- lang_bpe
-    |   |   |-- HLG.pt
-    |   |   |-- bpe.model
-    |   |   |-- tokens.txt
-    |   |   `-- words.txt
-    |   `-- lm
-    |       `-- G_4_gram.pt
-    |-- exp
-    |   `-- pretraind.pt
-    `-- test_wavs
-        |-- 1089-134686-0001.flac
-        |-- 1221-135766-0001.flac
-        |-- 1221-135766-0002.flac
-        `-- trans.txt
-
-6 directories, 11 files
-```
-
-**File descriptions**:
-
-  - `data/lang_bpe/HLG.pt`
-
-      It is the decoding graph.
-
-  - `data/lang_bpe/bpe.model`
-
-      It is a sentencepiece model. You can use it to reproduce our results.
-
-  - `data/lang_bpe/tokens.txt`
-
-      It contains tokens and their IDs, generated from `bpe.model`.
-      Provided only for convienice so that you can look up the SOS/EOS ID easily.
-
-  - `data/lang_bpe/words.txt`
-
-      It contains words and their IDs.
-
-  - `data/lm/G_4_gram.pt`
-
-      It is a 4-gram LM, useful for LM rescoring.
-
-  - `exp/pretrained.pt`
-
-      It contains pre-trained model parameters, obtained by averaging
-      checkpoints from `epoch-15.pt` to `epoch-34.pt`.
-      Note: We have removed optimizer `state_dict` to reduce file size.
-
-  - `test_waves/*.flac`
-
-      It contains some test sound files from LibriSpeech `test-clean` dataset.
-
-  - `test_waves/trans.txt`
-
-      It contains the reference transcripts for the sound files in `test_waves/`.
-
-The information of the test sound files is listed below:
-
-```
-$ soxi tmp/conformer_ctc/test_wavs/*.flac
-
-Input File     : 'tmp/conformer_ctc/test_wavs/1089-134686-0001.flac'
-Channels       : 1
-Sample Rate    : 16000
-Precision      : 16-bit
-Duration       : 00:00:06.62 = 106000 samples ~ 496.875 CDDA sectors
-File Size      : 116k
-Bit Rate       : 140k
-Sample Encoding: 16-bit FLAC
-
-Input File     : 'tmp/conformer_ctc/test_wavs/1221-135766-0001.flac'
-Channels       : 1
-Sample Rate    : 16000
-Precision      : 16-bit
-Duration       : 00:00:16.71 = 267440 samples ~ 1253.62 CDDA sectors
-File Size      : 343k
-Bit Rate       : 164k
-Sample Encoding: 16-bit FLAC
-
-Input File     : 'tmp/conformer_ctc/test_wavs/1221-135766-0002.flac'
-Channels       : 1
-Sample Rate    : 16000
-Precision      : 16-bit
-Duration       : 00:00:04.83 = 77200 samples ~ 361.875 CDDA sectors
-File Size      : 105k
-Bit Rate       : 174k
-Sample Encoding: 16-bit FLAC
-
-Total Duration of 3 files: 00:00:28.16
-```
-
-### (2) Use HLG decoding
-
-```bash
-cd /path/to/icefall/egs/librispeech/ASR
-
-./conformer_ctc/pretrained.py \
-  --checkpoint ./tmp/conformer_ctc/exp/pretraind.pt \
-  --words-file ./tmp/conformer_ctc/data/lang_bpe/words.txt \
-  --HLG ./tmp/conformer_ctc/data/lang_bpe/HLG.pt \
-  ./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac \
-  ./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac \
-  ./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac
-```
-
-The output is given below:
-
-```
-2021-08-20 11:03:05,712 INFO [pretrained.py:217] device: cuda:0
-2021-08-20 11:03:05,712 INFO [pretrained.py:219] Creating model
-2021-08-20 11:03:11,345 INFO [pretrained.py:238] Loading HLG from ./tmp/conformer_ctc/data/lang_bpe/HLG.pt
-2021-08-20 11:03:18,442 INFO [pretrained.py:255] Constructing Fbank computer
-2021-08-20 11:03:18,444 INFO [pretrained.py:265] Reading sound files: ['./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0002.flac']
-2021-08-20 11:03:18,507 INFO [pretrained.py:271] Decoding started
-2021-08-20 11:03:18,795 INFO [pretrained.py:300] Use HLG decoding
-2021-08-20 11:03:19,149 INFO [pretrained.py:339]
-./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac:
-AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS
-
-./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac:
-GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED
-BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN
-
-./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac:
-YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION
-
-
-2021-08-20 11:03:19,149 INFO [pretrained.py:341] Decoding Done
-```
-
-### (3) Use HLG decoding + LM rescoring
-
-```bash
-./conformer_ctc/pretrained.py \
-  --checkpoint ./tmp/conformer_ctc/exp/pretraind.pt \
-  --words-file ./tmp/conformer_ctc/data/lang_bpe/words.txt \
-  --HLG ./tmp/conformer_ctc/data/lang_bpe/HLG.pt \
-  --method whole-lattice-rescoring \
-  --G ./tmp/conformer_ctc/data/lm/G_4_gram.pt \
-  --ngram-lm-scale 0.8 \
-  ./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac \
-  ./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac \
-  ./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac
-```
-
-The output is:
-
-```
-2021-08-20 11:12:17,565 INFO [pretrained.py:217] device: cuda:0
-2021-08-20 11:12:17,565 INFO [pretrained.py:219] Creating model
-2021-08-20 11:12:23,728 INFO [pretrained.py:238] Loading HLG from ./tmp/conformer_ctc/data/lang_bpe/HLG.pt
-2021-08-20 11:12:30,035 INFO [pretrained.py:246] Loading G from ./tmp/conformer_ctc/data/lm/G_4_gram.pt
-2021-08-20 11:13:10,779 INFO [pretrained.py:255] Constructing Fbank computer
-2021-08-20 11:13:10,787 INFO [pretrained.py:265] Reading sound files: ['./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0002.flac']
-2021-08-20 11:13:10,798 INFO [pretrained.py:271] Decoding started
-2021-08-20 11:13:11,085 INFO [pretrained.py:305] Use HLG decoding + LM rescoring
-2021-08-20 11:13:11,736 INFO [pretrained.py:339]
-./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac:
-AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS
-
-./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac:
-GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED
-BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN
-
-./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac:
-YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION
-
-
-2021-08-20 11:13:11,737 INFO [pretrained.py:341] Decoding Done
-```
-
-### (4) Use HLG decoding + LM rescoring + attention decoder rescoring
-
-```bash
-./conformer_ctc/pretrained.py \
-  --checkpoint ./tmp/conformer_ctc/exp/pretraind.pt \
-  --words-file ./tmp/conformer_ctc/data/lang_bpe/words.txt \
-  --HLG ./tmp/conformer_ctc/data/lang_bpe/HLG.pt \
-  --method attention-decoder \
-  --G ./tmp/conformer_ctc/data/lm/G_4_gram.pt \
-  --ngram-lm-scale 1.3 \
-  --attention-decoder-scale 1.2 \
-  --lattice-score-scale 0.5 \
-  --num-paths 100 \
-  --sos-id 1 \
-  --eos-id 1 \
-  ./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac \
-  ./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac \
-  ./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac
-```
-
-The output is:
-
-```
-2021-08-20 11:19:11,397 INFO [pretrained.py:217] device: cuda:0
-2021-08-20 11:19:11,397 INFO [pretrained.py:219] Creating model
-2021-08-20 11:19:17,354 INFO [pretrained.py:238] Loading HLG from ./tmp/conformer_ctc/data/lang_bpe/HLG.pt
-2021-08-20 11:19:24,615 INFO [pretrained.py:246] Loading G from ./tmp/conformer_ctc/data/lm/G_4_gram.pt
-2021-08-20 11:20:04,576 INFO [pretrained.py:255] Constructing Fbank computer
-2021-08-20 11:20:04,584 INFO [pretrained.py:265] Reading sound files: ['./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/conformer_ctc/test_wavs/1221-135766-0002.flac']
-2021-08-20 11:20:04,595 INFO [pretrained.py:271] Decoding started
-2021-08-20 11:20:04,854 INFO [pretrained.py:313] Use HLG + LM rescoring + attention decoder rescoring
-2021-08-20 11:20:05,805 INFO [pretrained.py:339]
-./tmp/conformer_ctc/test_wavs/1089-134686-0001.flac:
-AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS
-
-./tmp/conformer_ctc/test_wavs/1221-135766-0001.flac:
-GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED
-BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN
-
-./tmp/conformer_ctc/test_wavs/1221-135766-0002.flac:
-YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION
-
-
-2021-08-20 11:20:05,805 INFO [pretrained.py:341] Decoding Done
-```
-
-**NOTE**: We provide a colab notebook for demonstration.
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing)
-
-Due to limited memory provided by Colab, you have to upgrade to Colab Pro to
-run `HLG decoding + LM rescoring` and `HLG decoding + LM rescoring + attention decoder rescoring`.
-Otherwise, you can only run `HLG decoding` with Colab.

egs/librispeech/ASR/conformer_ctc/decode.py

@@ -45,6 +45,7 @@ from icefall.utils import (
     get_texts,
     setup_logger,
     store_transcripts,
+    str2bool,
     write_error_stats,
 )
 
@@ -57,28 +58,74 @@ def get_parser():
     parser.add_argument(
         "--epoch",
         type=int,
-        default=9,
+        default=34,
         help="It specifies the checkpoint to use for decoding."
         "Note: Epoch counts from 0.",
     )
     parser.add_argument(
         "--avg",
         type=int,
-        default=1,
+        default=20,
         help="Number of checkpoints to average. Automatically select "
         "consecutive checkpoints before the checkpoint specified by "
         "'--epoch'. ",
     )
 
+    parser.add_argument(
+        "--method",
+        type=str,
+        default="attention-decoder",
+        help="""Decoding method.
+        Supported values are:
+            - (1) 1best. Extract the best path from the decoding lattice as the
+              decoding result.
+            - (2) nbest. Extract n paths from the decoding lattice; the path
+              with the highest score is the decoding result.
+            - (3) nbest-rescoring. Extract n paths from the decoding lattice,
+              rescore them with an n-gram LM (e.g., a 4-gram LM), the path with
+              the highest score is the decoding result.
+            - (4) whole-lattice-rescoring. Rescore the decoding lattice with an
+              n-gram LM (e.g., a 4-gram LM), the best path of rescored lattice
+              is the decoding result.
+            - (5) attention-decoder. Extract n paths from the LM rescored
+              lattice, the path with the highest score is the decoding result.
+            - (6) nbest-oracle. Its WER is the lower bound of any n-best
+              rescoring method can achieve. Useful for debugging n-best
+              rescoring method.
+        """,
+    )
+
+    parser.add_argument(
+        "--num-paths",
+        type=int,
+        default=100,
+        help="""Number of paths for n-best based decoding method.
+        Used only when "method" is one of the following values:
+        nbest, nbest-rescoring, attention-decoder, and nbest-oracle
+        """,
+    )
+
     parser.add_argument(
         "--lattice-score-scale",
         type=float,
         default=1.0,
-        help="The scale to be applied to `lattice.scores`."
+        help="""The scale to be applied to `lattice.scores`.
-        "It's needed if you use any kinds of n-best based rescoring. "
+        It's needed if you use any kinds of n-best based rescoring.
-        "Currently, it is used when the decoding method is: nbest, "
+        Used only when "method" is one of the following values:
-        "nbest-rescoring, attention-decoder, and nbest-oracle. "
+        nbest, nbest-rescoring, attention-decoder, and nbest-oracle
-        "A smaller value results in more unique paths.",
+        A smaller value results in more unique paths.
+        """,
+    )
+
+    parser.add_argument(
+        "--export",
+        type=str2bool,
+        default=False,
+        help="""When enabled, the averaged model is saved to
+        conformer_ctc/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
@@ -104,21 +151,6 @@ def get_params() -> AttributeDict:
             "min_active_states": 30,
             "max_active_states": 10000,
             "use_double_scores": True,
-            # Possible values for method:
-            #  - 1best
-            #  - nbest
-            #  - nbest-rescoring
-            #  - whole-lattice-rescoring
-            #  - attention-decoder
-            #  - nbest-oracle
-            #  "method": "nbest",
-            #  "method": "nbest-rescoring",
-            #  "method": "whole-lattice-rescoring",
-            "method": "attention-decoder",
-            #  "method": "nbest-oracle",
-            # num_paths is used when method is "nbest", "nbest-rescoring",
-            # attention-decoder, and nbest-oracle
-            "num_paths": 100,
         }
     )
     return params
@@ -129,7 +161,7 @@ def decode_one_batch(
     model: nn.Module,
     HLG: k2.Fsa,
     batch: dict,
-    lexicon: Lexicon,
+    word_table: k2.SymbolTable,
     sos_id: int,
     eos_id: int,
     G: Optional[k2.Fsa] = None,
@@ -163,8 +195,8 @@ def decode_one_batch(
         It is the return value from iterating
         `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
         for the format of the `batch`.
-      lexicon:
+      word_table:
-        It contains word symbol table.
+        The word symbol table.
       sos_id:
         The token ID of the SOS.
       eos_id:
@@ -217,7 +249,7 @@ def decode_one_batch(
             lattice=lattice,
             num_paths=params.num_paths,
             ref_texts=supervisions["text"],
-            lexicon=lexicon,
+            word_table=word_table,
             scale=params.lattice_score_scale,
         )
 
@@ -237,7 +269,7 @@ def decode_one_batch(
             key = f"no_rescore-scale-{params.lattice_score_scale}-{params.num_paths}"  # noqa
 
         hyps = get_texts(best_path)
-        hyps = [[lexicon.word_table[i] for i in ids] for ids in hyps]
+        hyps = [[word_table[i] for i in ids] for ids in hyps]
         return {key: hyps}
 
     assert params.method in [
@@ -283,7 +315,7 @@ def decode_one_batch(
     ans = dict()
     for lm_scale_str, best_path in best_path_dict.items():
         hyps = get_texts(best_path)
-        hyps = [[lexicon.word_table[i] for i in ids] for ids in hyps]
+        hyps = [[word_table[i] for i in ids] for ids in hyps]
         ans[lm_scale_str] = hyps
     return ans
 
@@ -293,7 +325,7 @@ def decode_dataset(
     params: AttributeDict,
     model: nn.Module,
     HLG: k2.Fsa,
-    lexicon: Lexicon,
+    word_table: k2.SymbolTable,
     sos_id: int,
     eos_id: int,
     G: Optional[k2.Fsa] = None,
@@ -309,8 +341,8 @@ def decode_dataset(
         The neural model.
       HLG:
         The decoding graph.
-      lexicon:
+      word_table:
-        It contains word symbol table.
+        It is the word symbol table.
       sos_id:
         The token ID for SOS.
       eos_id:
@@ -344,7 +376,7 @@ def decode_dataset(
             model=model,
             HLG=HLG,
             batch=batch,
-            lexicon=lexicon,
+            word_table=word_table,
             G=G,
             sos_id=sos_id,
             eos_id=eos_id,
@@ -521,6 +553,13 @@ 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"
+        )
+        return
+
     model.to(device)
     model.eval()
     num_param = sum([p.numel() for p in model.parameters()])
@@ -540,7 +579,7 @@ def main():
             params=params,
             model=model,
             HLG=HLG,
-            lexicon=lexicon,
+            word_table=lexicon.word_table,
             G=G,
             sos_id=sos_id,
             eos_id=eos_id,

egs/librispeech/ASR/conformer_ctc/test_subsampling.py

@@ -16,9 +16,8 @@
 # limitations under the License.
 
 
-from subsampling import Conv2dSubsampling
-from subsampling import VggSubsampling
 import torch
+from subsampling import Conv2dSubsampling, VggSubsampling
 
 
 def test_conv2d_subsampling():

egs/librispeech/ASR/conformer_ctc/test_transformer.py

@@ -17,17 +17,16 @@
 
 
 import torch
+from torch.nn.utils.rnn import pad_sequence
 from transformer import (
     Transformer,
+    add_eos,
+    add_sos,
+    decoder_padding_mask,
     encoder_padding_mask,
     generate_square_subsequent_mask,
-    decoder_padding_mask,
-    add_sos,
-    add_eos,
 )
 
-from torch.nn.utils.rnn import pad_sequence
-
 
 def test_encoder_padding_mask():
     supervisions = {

egs/librispeech/ASR/conformer_ctc/train.py

@@ -74,6 +74,23 @@ def get_parser():
         help="Should various information be logged in tensorboard.",
     )
 
+    parser.add_argument(
+        "--num-epochs",
+        type=int,
+        default=35,
+        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
+        conformer_ctc/exp/epoch-{start_epoch-1}.pt
+        """,
+    )
+
     return parser
 
 
@@ -103,11 +120,6 @@ def get_params() -> AttributeDict:
 
         - subsampling_factor:  The subsampling factor for the model.
 
-        - 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.
@@ -143,8 +155,6 @@ def get_params() -> AttributeDict:
             "feature_dim": 80,
             "weight_decay": 1e-6,
             "subsampling_factor": 4,
-            "start_epoch": 0,
-            "num_epochs": 20,
             "best_train_loss": float("inf"),
             "best_valid_loss": float("inf"),
             "best_train_epoch": -1,

egs/librispeech/ASR/local/compile_hlg.py

@@ -102,14 +102,14 @@ def compile_HLG(lang_dir: str) -> k2.Fsa:
 
     LG.labels[LG.labels >= first_token_disambig_id] = 0
 
-    assert isinstance(LG.aux_labels, k2.RaggedInt)
+    assert isinstance(LG.aux_labels, k2.RaggedTensor)
-    LG.aux_labels.values()[LG.aux_labels.values() >= first_word_disambig_id] = 0
+    LG.aux_labels.data[LG.aux_labels.data >= first_word_disambig_id] = 0
 
     LG = k2.remove_epsilon(LG)
     logging.info(f"LG shape after k2.remove_epsilon: {LG.shape}")
 
     LG = k2.connect(LG)
-    LG.aux_labels = k2.ragged.remove_values_eq(LG.aux_labels, 0)
+    LG.aux_labels = LG.aux_labels.remove_values_eq(0)
 
     logging.info("Arc sorting LG")
     LG = k2.arc_sort(LG)

egs/librispeech/ASR/tdnn_lstm_ctc/Pre-trained.md

@@ -0,0 +1,270 @@
+
+# How to use a pre-trained model to transcribe a sound file or multiple sound files
+
+(See the bottom of this document for the link to a colab notebook.)
+
+You need to prepare 4 files:
+
+  - a model checkpoint file, e.g., epoch-20.pt
+  - HLG.pt, the decoding graph
+  - words.txt, the word symbol table
+  - a sound file, whose sampling rate has to be 16 kHz.
+    Supported formats are those supported by `torchaudio.load()`,
+    e.g., wav and flac.
+
+Also, you need to install `kaldifeat`. Please refer to
+<https://github.com/csukuangfj/kaldifeat> for installation.
+
+```bash
+./tdnn_lstm_ctc/pretrained.py --help
+```
+
+displays the help information.
+
+## HLG decoding
+
+Once you have the above files ready and have `kaldifeat` installed,
+you can run:
+
+```bash
+./tdnn_lstm_ctc/pretrained.py \
+  --checkpoint /path/to/your/checkpoint.pt \
+  --words-file /path/to/words.txt \
+  --HLG /path/to/HLG.pt \
+  /path/to/your/sound.wav
+```
+
+and you will see the transcribed result.
+
+If you want to transcribe multiple files at the same time, you can use:
+
+```bash
+./tdnn_lstm_ctc/pretrained.py \
+  --checkpoint /path/to/your/checkpoint.pt \
+  --words-file /path/to/words.txt \
+  --HLG /path/to/HLG.pt \
+  /path/to/your/sound1.wav \
+  /path/to/your/sound2.wav \
+  /path/to/your/sound3.wav
+```
+
+**Note**: This is the fastest decoding method.
+
+## HLG decoding + LM rescoring
+
+`./tdnn_lstm_ctc/pretrained.py` also supports `whole lattice LM rescoring`.
+
+To use whole lattice LM rescoring, you also need the following files:
+
+  - G.pt, e.g., `data/lm/G_4_gram.pt` if you have run `./prepare.sh`
+
+The command to run decoding with LM rescoring is:
+
+```bash
+./tdnn_lstm_ctc/pretrained.py \
+  --checkpoint /path/to/your/checkpoint.pt \
+  --words-file /path/to/words.txt \
+  --HLG /path/to/HLG.pt \
+  --method whole-lattice-rescoring \
+  --G data/lm/G_4_gram.pt \
+  --ngram-lm-scale 0.8 \
+  /path/to/your/sound1.wav \
+  /path/to/your/sound2.wav \
+  /path/to/your/sound3.wav
+```
+
+# Decoding with a pre-trained model in action
+
+We have uploaded a pre-trained model to <https://huggingface.co/pkufool/icefall_asr_librispeech_tdnn-lstm_ctc>
+
+The following shows the steps about the usage of the provided pre-trained model.
+
+### (1) Download the pre-trained model
+
+```bash
+sudo apt-get install git-lfs
+cd /path/to/icefall/egs/librispeech/ASR
+git lfs install
+mkdir tmp
+cd tmp
+git clone https://huggingface.co/pkufool/icefall_asr_librispeech_tdnn-lstm_ctc
+```
+
+**CAUTION**: You have to install `git-lfs` to download the pre-trained model.
+
+You will find the following files:
+
+```
+tmp/
+`-- icefall_asr_librispeech_tdnn-lstm_ctc
+    |-- README.md
+    |-- data
+    |   |-- lang_phone
+    |   |   |-- HLG.pt
+    |   |   |-- tokens.txt
+    |   |   `-- words.txt
+    |   `-- lm
+    |       `-- G_4_gram.pt
+    |-- exp
+    |   `-- pretrained.pt
+    `-- test_wavs
+        |-- 1089-134686-0001.flac
+        |-- 1221-135766-0001.flac
+        |-- 1221-135766-0002.flac
+        `-- trans.txt
+
+6 directories, 10 files
+```
+
+**File descriptions**:
+
+  - `data/lang_phone/HLG.pt`
+
+      It is the decoding graph.
+
+  - `data/lang_phone/tokens.txt`
+
+      It contains tokens and their IDs.
+
+  - `data/lang_phone/words.txt`
+
+      It contains words and their IDs.
+
+  - `data/lm/G_4_gram.pt`
+
+      It is a 4-gram LM, useful for LM rescoring.
+
+  - `exp/pretrained.pt`
+
+      It contains pre-trained model parameters, obtained by averaging
+      checkpoints from `epoch-14.pt` to `epoch-19.pt`.
+      Note: We have removed optimizer `state_dict` to reduce file size.
+
+  - `test_waves/*.flac`
+
+      It contains some test sound files from LibriSpeech `test-clean` dataset.
+
+  - `test_waves/trans.txt`
+
+      It contains the reference transcripts for the sound files in `test_waves/`.
+
+The information of the test sound files is listed below:
+
+```
+$ soxi tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/*.flac
+
+Input File     : 'tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac'
+Channels       : 1
+Sample Rate    : 16000
+Precision      : 16-bit
+Duration       : 00:00:06.62 = 106000 samples ~ 496.875 CDDA sectors
+File Size      : 116k
+Bit Rate       : 140k
+Sample Encoding: 16-bit FLAC
+
+
+Input File     : 'tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac'
+Channels       : 1
+Sample Rate    : 16000
+Precision      : 16-bit
+Duration       : 00:00:16.71 = 267440 samples ~ 1253.62 CDDA sectors
+File Size      : 343k
+Bit Rate       : 164k
+Sample Encoding: 16-bit FLAC
+
+
+Input File     : 'tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac'
+Channels       : 1
+Sample Rate    : 16000
+Precision      : 16-bit
+Duration       : 00:00:04.83 = 77200 samples ~ 361.875 CDDA sectors
+File Size      : 105k
+Bit Rate       : 174k
+Sample Encoding: 16-bit FLAC
+
+Total Duration of 3 files: 00:00:28.16
+```
+
+### (2) Use HLG decoding
+
+```bash
+cd /path/to/icefall/egs/librispeech/ASR
+
+./tdnn_lstm_ctc/pretrained.py \
+  --checkpoint ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/exp/pretraind.pt \
+  --words-file ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/words.txt \
+  --HLG ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt \
+  ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac \
+  ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac \
+  ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac
+```
+
+The output is given below:
+
+```
+2021-08-24 16:57:13,315 INFO [pretrained.py:168] device: cuda:0
+2021-08-24 16:57:13,315 INFO [pretrained.py:170] Creating model
+2021-08-24 16:57:18,331 INFO [pretrained.py:182] Loading HLG from ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt
+2021-08-24 16:57:27,581 INFO [pretrained.py:199] Constructing Fbank computer
+2021-08-24 16:57:27,584 INFO [pretrained.py:209] Reading sound files: ['./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac']
+2021-08-24 16:57:27,599 INFO [pretrained.py:215] Decoding started
+2021-08-24 16:57:27,791 INFO [pretrained.py:245] Use HLG decoding
+2021-08-24 16:57:28,098 INFO [pretrained.py:266]
+./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac:
+AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS
+
+./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac:
+GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONORED BOSOM TO CONNECT HER PARENT FOREVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN
+
+./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac:
+YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION
+
+
+2021-08-24 16:57:28,099 INFO [pretrained.py:268] Decoding Done
+```
+
+### (3) Use HLG decoding + LM rescoring
+
+```bash
+./tdnn_lstm_ctc/pretrained.py \
+  --checkpoint ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/exp/pretraind.pt \
+  --words-file ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/words.txt \
+  --HLG ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt \
+  --method whole-lattice-rescoring \
+  --G ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lm/G_4_gram.pt \
+  --ngram-lm-scale 0.8 \
+  ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac \
+  ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac \
+  ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac
+```
+
+The output is:
+
+```
+2021-08-24 16:39:24,725 INFO [pretrained.py:168] device: cuda:0
+2021-08-24 16:39:24,725 INFO [pretrained.py:170] Creating model
+2021-08-24 16:39:29,403 INFO [pretrained.py:182] Loading HLG from ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lang_phone/HLG.pt
+2021-08-24 16:39:40,631 INFO [pretrained.py:190] Loading G from ./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/data/lm/G_4_gram.pt
+2021-08-24 16:39:53,098 INFO [pretrained.py:199] Constructing Fbank computer
+2021-08-24 16:39:53,107 INFO [pretrained.py:209] Reading sound files: ['./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac']
+2021-08-24 16:39:53,121 INFO [pretrained.py:215] Decoding started
+2021-08-24 16:39:53,443 INFO [pretrained.py:250] Use HLG decoding + LM rescoring
+2021-08-24 16:39:54,010 INFO [pretrained.py:266]
+./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1089-134686-0001.flac:
+AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS
+
+./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0001.flac:
+GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONORED BOSOM TO CONNECT HER PARENT FOREVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN
+
+./tmp/icefall_asr_librispeech_tdnn-lstm_ctc/test_wavs/1221-135766-0002.flac:
+YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION
+
+
+2021-08-24 16:39:54,010 INFO [pretrained.py:268] Decoding Done
+```
+
+**NOTE**: We provide a colab notebook for demonstration.
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1kNmDXNMwREi0rZGAOIAOJo93REBuOTcd?usp=sharing)
+
+Due to limited memory provided by Colab, you have to upgrade to Colab Pro to run `HLG decoding + LM rescoring`.
+Otherwise, you can only run `HLG decoding` with Colab.

egs/librispeech/ASR/tdnn_lstm_ctc/README.md

@@ -1,2 +1,4 @@
 
-Will add results later.
+Please visit
+<https://icefall.readthedocs.io/en/latest/recipes/librispeech/tdnn_lstm_ctc.html>
+for how to run this recipe.

egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py

@@ -82,14 +82,14 @@ class LibriSpeechAsrDataModule(DataModule):
         group.add_argument(
             "--max-duration",
             type=int,
-            default=500.0,
+            default=200.0,
             help="Maximum pooled recordings duration (seconds) in a "
             "single batch. You can reduce it if it causes CUDA OOM.",
         )
         group.add_argument(
             "--bucketing-sampler",
             type=str2bool,
-            default=False,
+            default=True,
             help="When enabled, the batches will come from buckets of "
             "similar duration (saves padding frames).",
         )

egs/librispeech/ASR/tdnn_lstm_ctc/decode.py

@@ -42,6 +42,7 @@ from icefall.utils import (
     get_texts,
     setup_logger,
     store_transcripts,
+    str2bool,
     write_error_stats,
 )
 
@@ -54,7 +55,7 @@ def get_parser():
     parser.add_argument(
         "--epoch",
         type=int,
-        default=9,
+        default=19,
         help="It specifies the checkpoint to use for decoding."
         "Note: Epoch counts from 0.",
     )
@@ -66,6 +67,16 @@ 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
 
 
@@ -87,9 +98,11 @@ def get_params() -> AttributeDict:
             #  - nbest
             #  - nbest-rescoring
             #  - whole-lattice-rescoring
-            "method": "1best",
+            "method": "whole-lattice-rescoring",
+            #  "method": "1best",
+            #  "method": "nbest",
             # num_paths is used when method is "nbest" and "nbest-rescoring"
-            "num_paths": 30,
+            "num_paths": 100,
         }
     )
     return params
@@ -408,6 +421,13 @@ 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"
+        )
+        return
+
     model.to(device)
     model.eval()
 

egs/librispeech/ASR/tdnn_lstm_ctc/pretrained.py

@@ -0,0 +1,277 @@
+#!/usr/bin/env python3
+# Copyright      2021  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                    Wei Kang)
+#
+# 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 TdnnLstm
+from torch.nn.utils.rnn import pad_sequence
+
+from icefall.decode import (
+    get_lattice,
+    one_best_decoding,
+    rescore_with_whole_lattice,
+)
+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(
+        "--method",
+        type=str,
+        default="1best",
+        help="""Decoding method.
+        Possible values are:
+        (1) 1best - Use the best path as decoding output. Only
+            the transformer encoder output is used for decoding.
+            We call it HLG decoding.
+        (2) whole-lattice-rescoring - Use an LM to rescore the
+            decoding lattice and then use 1best to decode the
+            rescored lattice.
+            We call it HLG decoding + n-gram LM rescoring.
+        """,
+    )
+
+    parser.add_argument(
+        "--G",
+        type=str,
+        help="""An LM for rescoring.
+        Used only when method is
+        whole-lattice-rescoring.
+        It's usually a 4-gram LM.
+        """,
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=0.8,
+        help="""
+        Used only when method is whole-lattice-rescoring.
+        It specifies the scale for n-gram LM scores.
+        (Note: You need to tune it on a dataset.)
+        """,
+    )
+
+    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": 80,
+            "subsampling_factor": 3,
+            "num_classes": 72,
+            "sample_rate": 16000,
+            "search_beam": 20,
+            "output_beam": 5,
+            "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 = TdnnLstm(
+        num_features=params.feature_dim,
+        num_classes=params.num_classes,
+        subsampling_factor=params.subsampling_factor,
+    )
+
+    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)
+    if not hasattr(HLG, "lm_scores"):
+        # For whole-lattice-rescoring and attention-decoder
+        HLG.lm_scores = HLG.scores.clone()
+
+    if params.method == "whole-lattice-rescoring":
+        logging.info(f"Loading G from {params.G}")
+        G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+        # Add epsilon self-loops to G as we will compose
+        # it with the whole lattice later
+        G = G.to(device)
+        G = k2.add_epsilon_self_loops(G)
+        G = k2.arc_sort(G)
+        G.lm_scores = G.scores.clone()
+
+    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)
+    )
+    features = features.permute(0, 2, 1)  # now features is [N, C, T]
+
+    with torch.no_grad():
+        nnet_output = model(features)
+        # nnet_output is [N, T, C]
+
+    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,
+        subsampling_factor=params.subsampling_factor,
+    )
+
+    if params.method == "1best":
+        logging.info("Use HLG decoding")
+        best_path = one_best_decoding(
+            lattice=lattice, use_double_scores=params.use_double_scores
+        )
+    elif params.method == "whole-lattice-rescoring":
+        logging.info("Use HLG decoding + LM rescoring")
+        best_path_dict = rescore_with_whole_lattice(
+            lattice=lattice,
+            G_with_epsilon_loops=G,
+            lm_scale_list=[params.ngram_lm_scale],
+        )
+        best_path = next(iter(best_path_dict.values()))
+
+    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()

egs/librispeech/ASR/tdnn_lstm_ctc/train.py

@@ -75,6 +75,23 @@ def get_parser():
         help="Should various information be logged in tensorboard.",
     )
 
+    parser.add_argument(
+        "--num-epochs",
+        type=int,
+        default=20,
+        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_lstm_ctc/exp/epoch-{start_epoch-1}.pt
+        """,
+    )
+
     return parser
 
 
@@ -104,11 +121,6 @@ def get_params() -> AttributeDict:
 
         - subsampling_factor:  The subsampling factor for the model.
 
-        - 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.
@@ -127,6 +139,8 @@ def get_params() -> AttributeDict:
 
         - log_interval:  Print training loss if batch_idx % log_interval` is 0
 
+        - reset_interval: Reset statistics if batch_idx % reset_interval is 0
+
         - valid_interval:  Run validation if batch_idx % valid_interval` is 0
 
         - beam_size: It is used in k2.ctc_loss
@@ -143,14 +157,13 @@ def get_params() -> AttributeDict:
             "feature_dim": 80,
             "weight_decay": 5e-4,
             "subsampling_factor": 3,
-            "start_epoch": 0,
-            "num_epochs": 10,
             "best_train_loss": float("inf"),
             "best_valid_loss": float("inf"),
             "best_train_epoch": -1,
             "best_valid_epoch": -1,
             "batch_idx_train": 0,
             "log_interval": 10,
+            "reset_interval": 200,
             "valid_interval": 1000,
             "beam_size": 10,
             "reduction": "sum",
@@ -398,8 +411,12 @@ def train_one_epoch(
     """
     model.train()
 
-    tot_loss = 0.0  # sum of losses over all batches
+    tot_loss = 0.0  # reset after params.reset_interval of batches
-    tot_frames = 0.0  # sum of frames over all batches
+    tot_frames = 0.0  # reset after params.reset_interval of batches
+
+    params.tot_loss = 0.0
+    params.tot_frames = 0.0
+
     for batch_idx, batch in enumerate(train_dl):
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
@@ -426,6 +443,9 @@ def train_one_epoch(
         tot_loss += loss_cpu
         tot_avg_loss = tot_loss / tot_frames
 
+        params.tot_frames += params.train_frames
+        params.tot_loss += loss_cpu
+
         if batch_idx % params.log_interval == 0:
             logging.info(
                 f"Epoch {params.cur_epoch}, batch {batch_idx}, "
@@ -433,6 +453,22 @@ def train_one_epoch(
                 f"total avg loss: {tot_avg_loss:.4f}, "
                 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.reset_interval == 0:
+            tot_loss = 0
+            tot_frames = 0
 
         if batch_idx > 0 and batch_idx % params.valid_interval == 0:
             compute_validation_loss(
@@ -449,7 +485,7 @@ def train_one_epoch(
                 f"best valid epoch: {params.best_valid_epoch}"
             )
 
-    params.train_loss = tot_loss / tot_frames
+    params.train_loss = params.tot_loss / params.tot_frames
 
     if params.train_loss < params.best_train_loss:
         params.best_train_epoch = params.cur_epoch

egs/yesno/ASR/README.md

@@ -1,15 +1,14 @@
 ## Yesno recipe
 
-You can run the recipe with **CPU**.
+This is the simplest ASR recipe in `icefall`.
 
-
+It can be run on CPU and takes less than 30 seconds to
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing)
+get the following WER:
-
-The above Colab notebook finishes the training using **CPU**
-within two minutes (50 epochs in total).
-
-The WER is
 
 ```
 [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
 ```
+
+Please refer to
+<https://icefall.readthedocs.io/en/latest/recipes/yesno.html>
+for detailed instructions.

egs/yesno/ASR/local/compile_hlg.py

@@ -80,14 +80,14 @@ def compile_HLG(lang_dir: str) -> k2.Fsa:
 
     LG.labels[LG.labels >= first_token_disambig_id] = 0
 
-    assert isinstance(LG.aux_labels, k2.RaggedInt)
+    assert isinstance(LG.aux_labels, k2.RaggedTensor)
-    LG.aux_labels.values()[LG.aux_labels.values() >= first_word_disambig_id] = 0
+    LG.aux_labels.data[LG.aux_labels.data >= first_word_disambig_id] = 0
 
     LG = k2.remove_epsilon(LG)
     logging.info(f"LG shape after k2.remove_epsilon: {LG.shape}")
 
     LG = k2.connect(LG)
-    LG.aux_labels = k2.ragged.remove_values_eq(LG.aux_labels, 0)
+    LG.aux_labels = LG.aux_labels.remove_values_eq(0)
 
     logging.info("Arc sorting LG")
     LG = k2.arc_sort(LG)

egs/yesno/ASR/tdnn/README.md

@@ -0,0 +1,8 @@
+
+## How to run this recipe
+
+You can find detailed instructions by visiting
+<https://icefall.readthedocs.io/en/latest/recipes/yesno.html>
+
+It describes how to run this recipe and how to use
+a pre-trained model with `./pretrained.py`.

egs/yesno/ASR/tdnn/asr_datamodule.py

@@ -27,7 +27,6 @@ from lhotse.dataset import (
     K2SpeechRecognitionDataset,
     PrecomputedFeatures,
     SingleCutSampler,
-    SpecAugment,
 )
 from lhotse.dataset.input_strategies import OnTheFlyFeatures
 from torch.utils.data import DataLoader
@@ -163,18 +162,8 @@ class YesNoAsrDataModule(DataModule):
                 )
             ] + transforms
 
-        input_transforms = [
-            SpecAugment(
-                num_frame_masks=2,
-                features_mask_size=27,
-                num_feature_masks=2,
-                frames_mask_size=100,
-            )
-        ]
-
         train = K2SpeechRecognitionDataset(
             cut_transforms=transforms,
-            input_transforms=input_transforms,
             return_cuts=self.args.return_cuts,
         )
 
@@ -194,7 +183,6 @@ class YesNoAsrDataModule(DataModule):
                 input_strategy=OnTheFlyFeatures(
                     Fbank(FbankConfig(num_mel_bins=23))
                 ),
-                input_transforms=input_transforms,
                 return_cuts=self.args.return_cuts,
             )
 

egs/yesno/ASR/tdnn/decode.py

@@ -20,6 +20,7 @@ from icefall.utils import (
     get_texts,
     setup_logger,
     store_transcripts,
+    str2bool,
     write_error_stats,
 )
 
@@ -32,18 +33,29 @@ def get_parser():
     parser.add_argument(
         "--epoch",
         type=int,
-        default=9,
+        default=14,
         help="It specifies the checkpoint to use for decoding."
         "Note: Epoch counts from 0.",
     )
     parser.add_argument(
         "--avg",
         type=int,
-        default=15,
+        default=2,
         help="Number of checkpoints to average. Automatically select "
         "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
 
 
@@ -104,16 +116,11 @@ def decode_one_batch(
     nnet_output = model(feature)
     # nnet_output is [N, T, C]
 
-    supervisions = batch["supervisions"]
+    batch_size = nnet_output.shape[0]
-
+    supervision_segments = torch.tensor(
-    supervision_segments = torch.stack(
+        [[i, 0, nnet_output.shape[1]] for i in range(batch_size)],
-        (
+        dtype=torch.int32,
-            supervisions["sequence_idx"],
+    )
-            supervisions["start_frame"],
-            supervisions["num_frames"],
-        ),
-        1,
-    ).to(torch.int32)
 
     lattice = get_lattice(
         nnet_output=nnet_output,
@@ -284,6 +291,13 @@ 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"
+        )
+        return
+
     model.to(device)
     model.eval()
 

egs/yesno/ASR/tdnn/model.py

@@ -23,7 +23,6 @@ class Tdnn(nn.Module):
                 in_channels=num_features,
                 out_channels=32,
                 kernel_size=3,
-                padding=1,
             ),
             nn.ReLU(inplace=True),
             nn.BatchNorm1d(num_features=32, affine=False),
@@ -31,7 +30,6 @@ class Tdnn(nn.Module):
                 in_channels=32,
                 out_channels=32,
                 kernel_size=5,
-                padding=4,
                 dilation=2,
             ),
             nn.ReLU(inplace=True),
@@ -40,7 +38,6 @@ class Tdnn(nn.Module):
                 in_channels=32,
                 out_channels=32,
                 kernel_size=5,
-                padding=8,
                 dilation=4,
             ),
             nn.ReLU(inplace=True),

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()

egs/yesno/ASR/tdnn/train.py

@@ -24,12 +24,7 @@ from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.graph_compiler import CtcTrainingGraphCompiler
 from icefall.lexicon import Lexicon
-from icefall.utils import (
+from icefall.utils import AttributeDict, setup_logger, str2bool
-    AttributeDict,
-    encode_supervisions,
-    setup_logger,
-    str2bool,
-)
 
 
 def get_parser():
@@ -61,10 +56,20 @@ def get_parser():
     parser.add_argument(
         "--num-epochs",
         type=int,
-        default=50,
+        default=15,
         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
 
 
@@ -97,8 +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.
@@ -129,11 +132,10 @@ def get_params() -> AttributeDict:
         {
             "exp_dir": Path("tdnn/exp"),
             "lang_dir": Path("data/lang_phone"),
-            "lr": 1e-3,
+            "lr": 1e-2,
             "feature_dim": 23,
             "weight_decay": 1e-6,
             "start_epoch": 0,
-            "num_epochs": 50,
             "best_train_loss": float("inf"),
             "best_valid_loss": float("inf"),
             "best_train_epoch": -1,
@@ -278,9 +280,14 @@ def compute_loss(
     # different duration in decreasing order, required by
     # `k2.intersect_dense` called in `k2.ctc_loss`
     supervisions = batch["supervisions"]
-    supervision_segments, texts = encode_supervisions(
+    texts = supervisions["text"]
-        supervisions, subsampling_factor=1
+
+    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,
     )
+
     decoding_graph = graph_compiler.compile(texts)
 
     dense_fsa_vec = k2.DenseFsaVec(
@@ -421,6 +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,
@@ -435,6 +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
 
@@ -491,7 +517,7 @@ def run(rank, world_size, args):
     if world_size > 1:
         model = DDP(model, device_ids=[rank])
 
-    optimizer = optim.AdamW(
+    optimizer = optim.SGD(
         model.parameters(),
         lr=params.lr,
         weight_decay=params.weight_decay,

icefall/decode.py

@@ -22,8 +22,6 @@ import kaldialign
 import torch
 import torch.nn as nn
 
-from icefall.lexicon import Lexicon
-
 
 def _get_random_paths(
     lattice: k2.Fsa,
@@ -86,8 +84,8 @@ def _intersect_device(
     for start, end in splits:
         indexes = torch.arange(start, end).to(b_to_a_map)
 
-        fsas = k2.index(b_fsas, indexes)
+        fsas = k2.index_fsa(b_fsas, indexes)
-        b_to_a = k2.index(b_to_a_map, indexes)
+        b_to_a = k2.index_select(b_to_a_map, indexes)
         path_lattice = k2.intersect_device(
             a_fsas, fsas, b_to_a_map=b_to_a, sorted_match_a=sorted_match_a
         )
@@ -217,18 +215,16 @@ def nbest_decoding(
         scale=scale,
     )
 
-    # word_seq is a k2.RaggedInt sharing the same shape as `path`
+    # word_seq is a k2.RaggedTensor sharing the same shape as `path`
     # but it contains word IDs. Note that it also contains 0s and -1s.
     # The last entry in each sublist is -1.
-    word_seq = k2.index(lattice.aux_labels, path)
+    if isinstance(lattice.aux_labels, torch.Tensor):
-    # Note: the above operation supports also the case when
+        word_seq = k2.ragged.index(lattice.aux_labels, path)
-    # lattice.aux_labels is a ragged tensor. In that case,
+    else:
-    # `remove_axis=True` is used inside the pybind11 binding code,
+        word_seq = lattice.aux_labels.index(path, remove_axis=True)
-    # so the resulting `word_seq` still has 3 axes, like `path`.
-    # The 3 axes are [seq][path][word_id]
 
     # Remove 0 (epsilon) and -1 from word_seq
-    word_seq = k2.ragged.remove_values_leq(word_seq, 0)
+    word_seq = word_seq.remove_values_leq(0)
 
     # Remove sequences with identical word sequences.
     #
@@ -236,12 +232,12 @@ def nbest_decoding(
     # `new2old` is a 1-D torch.Tensor mapping from the output path index
     # to the input path index.
     # new2old.numel() == unique_word_seqs.tot_size(1)
-    unique_word_seq, _, new2old = k2.ragged.unique_sequences(
+    unique_word_seq, _, new2old = word_seq.unique(
-        word_seq, need_num_repeats=False, need_new2old_indexes=True
+        need_num_repeats=False, need_new2old_indexes=True
     )
     # Note: unique_word_seq still has the same axes as word_seq
 
-    seq_to_path_shape = k2.ragged.get_layer(unique_word_seq.shape(), 0)
+    seq_to_path_shape = unique_word_seq.shape.get_layer(0)
 
     # path_to_seq_map is a 1-D torch.Tensor.
     # path_to_seq_map[i] is the seq to which the i-th path belongs
@@ -249,7 +245,7 @@ def nbest_decoding(
 
     # Remove the seq axis.
     # Now unique_word_seq has only two axes [path][word]
-    unique_word_seq = k2.ragged.remove_axis(unique_word_seq, 0)
+    unique_word_seq = unique_word_seq.remove_axis(0)
 
     # word_fsa is an FsaVec with axes [path][state][arc]
     word_fsa = k2.linear_fsa(unique_word_seq)
@@ -277,35 +273,35 @@ def nbest_decoding(
         use_double_scores=use_double_scores, log_semiring=False
     )
 
-    # RaggedFloat currently supports float32 only.
+    ragged_tot_scores = k2.RaggedTensor(seq_to_path_shape, tot_scores)
-    # If Ragged<double> is wrapped, we can use k2.RaggedDouble here
-    ragged_tot_scores = k2.RaggedFloat(
-        seq_to_path_shape, tot_scores.to(torch.float32)
-    )
 
-    argmax_indexes = k2.ragged.argmax_per_sublist(ragged_tot_scores)
+    argmax_indexes = ragged_tot_scores.argmax()
 
     # Since we invoked `k2.ragged.unique_sequences`, which reorders
     # the index from `path`, we use `new2old` here to convert argmax_indexes
     # to the indexes into `path`.
     #
     # Use k2.index here since argmax_indexes' dtype is torch.int32
-    best_path_indexes = k2.index(new2old, argmax_indexes)
+    best_path_indexes = k2.index_select(new2old, argmax_indexes)
 
-    path_2axes = k2.ragged.remove_axis(path, 0)
+    path_2axes = path.remove_axis(0)
 
-    # best_path is a k2.RaggedInt with 2 axes [path][arc_pos]
+    # best_path is a k2.RaggedTensor with 2 axes [path][arc_pos]
-    best_path = k2.index(path_2axes, best_path_indexes)
+    best_path, _ = path_2axes.index(
+        indexes=best_path_indexes, axis=0, need_value_indexes=False
+    )
 
-    # labels is a k2.RaggedInt with 2 axes [path][token_id]
+    # labels is a k2.RaggedTensor with 2 axes [path][token_id]
     # Note that it contains -1s.
-    labels = k2.index(lattice.labels.contiguous(), best_path)
+    labels = k2.ragged.index(lattice.labels.contiguous(), best_path)
 
-    labels = k2.ragged.remove_values_eq(labels, -1)
+    labels = labels.remove_values_eq(-1)
 
-    # lattice.aux_labels is a k2.RaggedInt tensor with 2 axes, so
+    # lattice.aux_labels is a k2.RaggedTensor with 2 axes, so
-    # aux_labels is also a k2.RaggedInt with 2 axes
+    # aux_labels is also a k2.RaggedTensor with 2 axes
-    aux_labels = k2.index(lattice.aux_labels, best_path.values())
+    aux_labels, _ = lattice.aux_labels.index(
+        indexes=best_path.data, axis=0, need_value_indexes=False
+    )
 
     best_path_fsa = k2.linear_fsa(labels)
     best_path_fsa.aux_labels = aux_labels
@@ -428,33 +424,36 @@ def rescore_with_n_best_list(
         scale=scale,
     )
 
-    # word_seq is a k2.RaggedInt sharing the same shape as `path`
+    # word_seq is a k2.RaggedTensor sharing the same shape as `path`
     # but it contains word IDs. Note that it also contains 0s and -1s.
     # The last entry in each sublist is -1.
-    word_seq = k2.index(lattice.aux_labels, path)
+    if isinstance(lattice.aux_labels, torch.Tensor):
+        word_seq = k2.ragged.index(lattice.aux_labels, path)
+    else:
+        word_seq = lattice.aux_labels.index(path, remove_axis=True)
 
     # Remove epsilons and -1 from word_seq
-    word_seq = k2.ragged.remove_values_leq(word_seq, 0)
+    word_seq = word_seq.remove_values_leq(0)
 
     # Remove paths that has identical word sequences.
     #
-    # unique_word_seq is still a k2.RaggedInt with 3 axes [seq][path][word]
+    # unique_word_seq is still a k2.RaggedTensor with 3 axes [seq][path][word]
     # except that there are no repeated paths with the same word_seq
     # within a sequence.
     #
-    # num_repeats is also a k2.RaggedInt with 2 axes containing the
+    # num_repeats is also a k2.RaggedTensor with 2 axes containing the
     # multiplicities of each path.
-    # num_repeats.num_elements() == unique_word_seqs.tot_size(1)
+    # num_repeats.numel() == unique_word_seqs.tot_size(1)
     #
     # Since k2.ragged.unique_sequences will reorder paths within a seq,
     # `new2old` is a 1-D torch.Tensor mapping from the output path index
     # to the input path index.
     # new2old.numel() == unique_word_seqs.tot_size(1)
-    unique_word_seq, num_repeats, new2old = k2.ragged.unique_sequences(
+    unique_word_seq, num_repeats, new2old = word_seq.unique(
-        word_seq, need_num_repeats=True, need_new2old_indexes=True
+        need_num_repeats=True, need_new2old_indexes=True
     )
 
-    seq_to_path_shape = k2.ragged.get_layer(unique_word_seq.shape(), 0)
+    seq_to_path_shape = unique_word_seq.shape.get_layer(0)
 
     # path_to_seq_map is a 1-D torch.Tensor.
     # path_to_seq_map[i] is the seq to which the i-th path
@@ -463,7 +462,7 @@ def rescore_with_n_best_list(
 
     # Remove the seq axis.
     # Now unique_word_seq has only two axes [path][word]
-    unique_word_seq = k2.ragged.remove_axis(unique_word_seq, 0)
+    unique_word_seq = unique_word_seq.remove_axis(0)
 
     # word_fsa is an FsaVec with axes [path][state][arc]
     word_fsa = k2.linear_fsa(unique_word_seq)
@@ -487,39 +486,42 @@ def rescore_with_n_best_list(
         use_double_scores=True, log_semiring=False
     )
 
-    path_2axes = k2.ragged.remove_axis(path, 0)
+    path_2axes = path.remove_axis(0)
 
     ans = dict()
     for lm_scale in lm_scale_list:
         tot_scores = am_scores / lm_scale + lm_scores
 
-        # Remember that we used `k2.ragged.unique_sequences` to remove repeated
+        # Remember that we used `k2.RaggedTensor.unique` to remove repeated
         # paths to avoid redundant computation in `k2.intersect_device`.
         # Now we use `num_repeats` to correct the scores for each path.
         #
         # NOTE(fangjun): It is commented out as it leads to a worse WER
         # tot_scores = tot_scores * num_repeats.values()
 
-        ragged_tot_scores = k2.RaggedFloat(
+        ragged_tot_scores = k2.RaggedTensor(seq_to_path_shape, tot_scores)
-            seq_to_path_shape, tot_scores.to(torch.float32)
+        argmax_indexes = ragged_tot_scores.argmax()
-        )
-        argmax_indexes = k2.ragged.argmax_per_sublist(ragged_tot_scores)
 
         # Use k2.index here since argmax_indexes' dtype is torch.int32
-        best_path_indexes = k2.index(new2old, argmax_indexes)
+        best_path_indexes = k2.index_select(new2old, argmax_indexes)
 
         # best_path is a k2.RaggedInt with 2 axes [path][arc_pos]
-        best_path = k2.index(path_2axes, best_path_indexes)
+        best_path, _ = path_2axes.index(
+            indexes=best_path_indexes, axis=0, need_value_indexes=False
+        )
 
-        # labels is a k2.RaggedInt with 2 axes [path][phone_id]
+        # labels is a k2.RaggedTensor with 2 axes [path][phone_id]
         # Note that it contains -1s.
-        labels = k2.index(lattice.labels.contiguous(), best_path)
+        labels = k2.ragged.index(lattice.labels.contiguous(), best_path)
 
-        labels = k2.ragged.remove_values_eq(labels, -1)
+        labels = labels.remove_values_eq(-1)
 
-        # lattice.aux_labels is a k2.RaggedInt tensor with 2 axes, so
+        # lattice.aux_labels is a k2.RaggedTensor tensor with 2 axes, so
-        # aux_labels is also a k2.RaggedInt with 2 axes
+        # aux_labels is also a k2.RaggedTensor with 2 axes
-        aux_labels = k2.index(lattice.aux_labels, best_path.values())
+
+        aux_labels, _ = lattice.aux_labels.index(
+            indexes=best_path.data, axis=0, need_value_indexes=False
+        )
 
         best_path_fsa = k2.linear_fsa(labels)
         best_path_fsa.aux_labels = aux_labels
@@ -623,7 +625,7 @@ def nbest_oracle(
     lattice: k2.Fsa,
     num_paths: int,
     ref_texts: List[str],
-    lexicon: Lexicon,
+    word_table: k2.SymbolTable,
     scale: float = 1.0,
 ) -> Dict[str, List[List[int]]]:
     """Select the best hypothesis given a lattice and a reference transcript.
@@ -644,8 +646,8 @@ def nbest_oracle(
       ref_texts:
         A list of reference transcript. Each entry contains space(s)
         separated words
-      lexicon:
+      word_table:
-        It is used to convert word IDs to word symbols.
+        It is the word symbol table.
       scale:
         It's the scale applied to the lattice.scores. A smaller value
         yields more unique paths.
@@ -661,12 +663,16 @@ def nbest_oracle(
         scale=scale,
     )
 
-    word_seq = k2.index(lattice.aux_labels, path)
+    if isinstance(lattice.aux_labels, torch.Tensor):
-    word_seq = k2.ragged.remove_values_leq(word_seq, 0)
+        word_seq = k2.ragged.index(lattice.aux_labels, path)
-    unique_word_seq, _, _ = k2.ragged.unique_sequences(
+    else:
-        word_seq, need_num_repeats=False, need_new2old_indexes=False
+        word_seq = lattice.aux_labels.index(path, remove_axis=True)
+
+    word_seq = word_seq.remove_values_leq(0)
+    unique_word_seq, _, _ = word_seq.unique(
+        need_num_repeats=False, need_new2old_indexes=False
     )
-    unique_word_ids = k2.ragged.to_list(unique_word_seq)
+    unique_word_ids = unique_word_seq.tolist()
     assert len(unique_word_ids) == len(ref_texts)
     # unique_word_ids[i] contains all hypotheses of the i-th utterance
 
@@ -680,7 +686,7 @@ def nbest_oracle(
         best_hyp_words = None
         min_error = float("inf")
         for hyp_words in hyps:
-            hyp_words = [lexicon.word_table[i] for i in hyp_words]
+            hyp_words = [word_table[i] for i in hyp_words]
             this_error = kaldialign.edit_distance(ref_words, hyp_words)["total"]
             if this_error < min_error:
                 min_error = this_error
@@ -745,33 +751,36 @@ def rescore_with_attention_decoder(
         scale=scale,
     )
 
-    # word_seq is a k2.RaggedInt sharing the same shape as `path`
+    # word_seq is a k2.RaggedTensor sharing the same shape as `path`
     # but it contains word IDs. Note that it also contains 0s and -1s.
     # The last entry in each sublist is -1.
-    word_seq = k2.index(lattice.aux_labels, path)
+    if isinstance(lattice.aux_labels, torch.Tensor):
+        word_seq = k2.ragged.index(lattice.aux_labels, path)
+    else:
+        word_seq = lattice.aux_labels.index(path, remove_axis=True)
 
     # Remove epsilons and -1 from word_seq
-    word_seq = k2.ragged.remove_values_leq(word_seq, 0)
+    word_seq = word_seq.remove_values_leq(0)
 
     # Remove paths that has identical word sequences.
     #
-    # unique_word_seq is still a k2.RaggedInt with 3 axes [seq][path][word]
+    # unique_word_seq is still a k2.RaggedTensor with 3 axes [seq][path][word]
     # except that there are no repeated paths with the same word_seq
     # within a sequence.
     #
-    # num_repeats is also a k2.RaggedInt with 2 axes containing the
+    # num_repeats is also a k2.RaggedTensor with 2 axes containing the
     # multiplicities of each path.
-    # num_repeats.num_elements() == unique_word_seqs.tot_size(1)
+    # num_repeats.numel() == unique_word_seqs.tot_size(1)
     #
     # Since k2.ragged.unique_sequences will reorder paths within a seq,
     # `new2old` is a 1-D torch.Tensor mapping from the output path index
     # to the input path index.
     # new2old.numel() == unique_word_seq.tot_size(1)
-    unique_word_seq, num_repeats, new2old = k2.ragged.unique_sequences(
+    unique_word_seq, num_repeats, new2old = word_seq.unique(
-        word_seq, need_num_repeats=True, need_new2old_indexes=True
+        need_num_repeats=True, need_new2old_indexes=True
     )
 
-    seq_to_path_shape = k2.ragged.get_layer(unique_word_seq.shape(), 0)
+    seq_to_path_shape = unique_word_seq.shape.get_layer(0)
 
     # path_to_seq_map is a 1-D torch.Tensor.
     # path_to_seq_map[i] is the seq to which the i-th path
@@ -780,7 +789,7 @@ def rescore_with_attention_decoder(
 
     # Remove the seq axis.
     # Now unique_word_seq has only two axes [path][word]
-    unique_word_seq = k2.ragged.remove_axis(unique_word_seq, 0)
+    unique_word_seq = unique_word_seq.remove_axis(0)
 
     # word_fsa is an FsaVec with axes [path][state][arc]
     word_fsa = k2.linear_fsa(unique_word_seq)
@@ -798,20 +807,23 @@ def rescore_with_attention_decoder(
 
     # CAUTION: The "tokens" attribute is set in the file
     # local/compile_hlg.py
-    token_seq = k2.index(lattice.tokens, path)
+    if isinstance(lattice.tokens, torch.Tensor):
+        token_seq = k2.ragged.index(lattice.tokens, path)
+    else:
+        token_seq = lattice.tokens.index(path, remove_axis=True)
 
     # Remove epsilons and -1 from token_seq
-    token_seq = k2.ragged.remove_values_leq(token_seq, 0)
+    token_seq = token_seq.remove_values_leq(0)
 
     # Remove the seq axis.
-    token_seq = k2.ragged.remove_axis(token_seq, 0)
+    token_seq = token_seq.remove_axis(0)
 
-    token_seq, _ = k2.ragged.index(
+    token_seq, _ = token_seq.index(
-        token_seq, indexes=new2old, axis=0, need_value_indexes=False
+        indexes=new2old, axis=0, need_value_indexes=False
     )
 
     # Now word in unique_word_seq has its corresponding token IDs.
-    token_ids = k2.ragged.to_list(token_seq)
+    token_ids = token_seq.tolist()
 
     num_word_seqs = new2old.numel()
 
@@ -851,7 +863,7 @@ def rescore_with_attention_decoder(
     else:
         attention_scale_list = [attention_scale]
 
-    path_2axes = k2.ragged.remove_axis(path, 0)
+    path_2axes = path.remove_axis(0)
 
     ans = dict()
     for n_scale in ngram_lm_scale_list:
@@ -861,23 +873,28 @@ def rescore_with_attention_decoder(
                 + n_scale * ngram_lm_scores
                 + a_scale * attention_scores
             )
-            ragged_tot_scores = k2.RaggedFloat(seq_to_path_shape, tot_scores)
+            ragged_tot_scores = k2.RaggedTensor(seq_to_path_shape, tot_scores)
-            argmax_indexes = k2.ragged.argmax_per_sublist(ragged_tot_scores)
+            argmax_indexes = ragged_tot_scores.argmax()
 
-            best_path_indexes = k2.index(new2old, argmax_indexes)
+            best_path_indexes = k2.index_select(new2old, argmax_indexes)
 
             # best_path is a k2.RaggedInt with 2 axes [path][arc_pos]
-            best_path = k2.index(path_2axes, best_path_indexes)
+            best_path, _ = path_2axes.index(
+                indexes=best_path_indexes, axis=0, need_value_indexes=False
+            )
 
-            # labels is a k2.RaggedInt with 2 axes [path][token_id]
+            # labels is a k2.RaggedTensor with 2 axes [path][token_id]
             # Note that it contains -1s.
-            labels = k2.index(lattice.labels.contiguous(), best_path)
+            labels = k2.ragged.index(lattice.labels.contiguous(), best_path)
 
-            labels = k2.ragged.remove_values_eq(labels, -1)
+            labels = labels.remove_values_eq(-1)
 
-            # lattice.aux_labels is a k2.RaggedInt tensor with 2 axes, so
+            if isinstance(lattice.aux_labels, torch.Tensor):
-            # aux_labels is also a k2.RaggedInt with 2 axes
+                aux_labels = k2.index_select(lattice.aux_labels, best_path.data)
-            aux_labels = k2.index(lattice.aux_labels, best_path.values())
+            else:
+                aux_labels, _ = lattice.aux_labels.index(
+                    indexes=best_path.data, axis=0, need_value_indexes=False
+                )
 
             best_path_fsa = k2.linear_fsa(labels)
             best_path_fsa.aux_labels = aux_labels

icefall/lexicon.py

@@ -157,7 +157,7 @@ class BpeLexicon(Lexicon):
             lang_dir / "lexicon.txt"
         )
 
-    def convert_lexicon_to_ragged(self, filename: str) -> k2.RaggedInt:
+    def convert_lexicon_to_ragged(self, filename: str) -> k2.RaggedTensor:
         """Read a BPE lexicon from file and convert it to a
         k2 ragged tensor.
 
@@ -200,19 +200,18 @@ class BpeLexicon(Lexicon):
         )
         values = torch.tensor(token_ids, dtype=torch.int32)
 
-        return k2.RaggedInt(shape, values)
+        return k2.RaggedTensor(shape, values)
 
-    def words_to_piece_ids(self, words: List[str]) -> k2.RaggedInt:
+    def words_to_piece_ids(self, words: List[str]) -> k2.RaggedTensor:
         """Convert a list of words to a ragged tensor contained
         word piece IDs.
         """
         word_ids = [self.word_table[w] for w in words]
         word_ids = torch.tensor(word_ids, dtype=torch.int32)
 
-        ragged, _ = k2.ragged.index(
+        ragged, _ = self.ragged_lexicon.index(
-            self.ragged_lexicon,
             indexes=word_ids,
-            need_value_indexes=False,
             axis=0,
+            need_value_indexes=False,
         )
         return ragged

icefall/utils.py

@@ -26,7 +26,6 @@ from pathlib import Path
 from typing import Dict, Iterable, List, TextIO, Tuple, Union
 
 import k2
-import k2.ragged as k2r
 import kaldialign
 import torch
 import torch.distributed as dist
@@ -199,26 +198,25 @@ def get_texts(best_paths: k2.Fsa) -> List[List[int]]:
       Returns a list of lists of int, containing the label sequences we
       decoded.
     """
-    if isinstance(best_paths.aux_labels, k2.RaggedInt):
+    if isinstance(best_paths.aux_labels, k2.RaggedTensor):
         # remove 0's and -1's.
-        aux_labels = k2r.remove_values_leq(best_paths.aux_labels, 0)
+        aux_labels = best_paths.aux_labels.remove_values_leq(0)
-        aux_shape = k2r.compose_ragged_shapes(
+        # TODO: change arcs.shape() to arcs.shape
-            best_paths.arcs.shape(), aux_labels.shape()
+        aux_shape = best_paths.arcs.shape().compose(aux_labels.shape)
-        )
 
         # remove the states and arcs axes.
-        aux_shape = k2r.remove_axis(aux_shape, 1)
+        aux_shape = aux_shape.remove_axis(1)
-        aux_shape = k2r.remove_axis(aux_shape, 1)
+        aux_shape = aux_shape.remove_axis(1)
-        aux_labels = k2.RaggedInt(aux_shape, aux_labels.values())
+        aux_labels = k2.RaggedTensor(aux_shape, aux_labels.data)
     else:
         # remove axis corresponding to states.
-        aux_shape = k2r.remove_axis(best_paths.arcs.shape(), 1)
+        aux_shape = best_paths.arcs.shape().remove_axis(1)
-        aux_labels = k2.RaggedInt(aux_shape, best_paths.aux_labels)
+        aux_labels = k2.RaggedTensor(aux_shape, best_paths.aux_labels)
         # remove 0's and -1's.
-        aux_labels = k2r.remove_values_leq(aux_labels, 0)
+        aux_labels = aux_labels.remove_values_leq(0)
 
-    assert aux_labels.num_axes() == 2
+    assert aux_labels.num_axes == 2
-    return k2r.to_list(aux_labels)
+    return aux_labels.tolist()
 
 
 def store_transcripts(

test/test_bpe_graph_compiler.py

@@ -16,9 +16,10 @@
 # limitations under the License.
 
 
+from pathlib import Path
+
 from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
 from icefall.lexicon import BpeLexicon
-from pathlib import Path
 
 
 def test():

test/test_utils.py

@@ -60,7 +60,7 @@ def test_get_texts_ragged():
         4
     """
     )
-    fsa1.aux_labels = k2.RaggedInt("[ [1 3 0 2] [] [4 0 1] [-1]]")
+    fsa1.aux_labels = k2.RaggedTensor("[ [1 3 0 2] [] [4 0 1] [-1]]")
 
     fsa2 = k2.Fsa.from_str(
         """
@@ -70,7 +70,7 @@ def test_get_texts_ragged():
         3
     """
     )
-    fsa2.aux_labels = k2.RaggedInt("[[3 0 5 0 8] [0 9 7 0] [-1]]")
+    fsa2.aux_labels = k2.RaggedTensor("[[3 0 5 0 8] [0 9 7 0] [-1]]")
     fsas = k2.Fsa.from_fsas([fsa1, fsa2])
     texts = get_texts(fsas)
     assert texts == [[1, 3, 2, 4, 1], [3, 5, 8, 9, 7]]