Merge branch 'master' into embedding-scale

.flake8

@@ -2,6 +2,9 @@
 show-source=true
 statistics=true
 max-line-length = 80
+per-file-ignores =
+    # line too long
+    egs/librispeech/ASR/conformer_ctc/conformer.py: E501,
 
 exclude =
   .git,

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

@@ -0,0 +1,78 @@
+# Copyright      2021  Fangjun Kuang (csukuangfj@gmail.com)
+
+# 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.
+
+name: run-yesno-recipe
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+    branches:
+      - master
+
+jobs:
+  run-yesno-recipe:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        # os: [ubuntu-18.04, macos-10.15]
+        # TODO: enable macOS for CPU testing
+        os: [ubuntu-18.04]
+        python-version: [3.8]
+      fail-fast: false
+
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+
+      - name: Setup Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v1
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install libnsdfile and libsox
+        if: startsWith(matrix.os, 'ubuntu')
+        run: |
+          sudo apt update
+          sudo apt install -q -y libsndfile1-dev libsndfile1 ffmpeg
+          sudo apt install -q -y --fix-missing sox libsox-dev libsox-fmt-all
+
+      - name: Install Python dependencies
+        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 torchaudio==0.7.2
+          python3 -m pip install git+https://github.com/lhotse-speech/lhotse
+
+          # We are in ./icefall and there is a file: requirements.txt in it
+          python3 -m pip install -r requirements.txt
+
+      - name: Run yesno recipe
+        shell: bash
+        working-directory: ${{github.workspace}}
+        run: |
+          export PYTHONPATH=$PWD:$PYTHONPATH
+          echo $PYTHONPATH
+
+
+          cd egs/yesno/ASR
+          ./prepare.sh
+          python3 ./tdnn/train.py
+          python3 ./tdnn/decode.py
+          # TODO: Check that the WER is less than some value

.github/workflows/style_check.yml

@@ -45,7 +45,7 @@ jobs:
 
       - name: Install Python dependencies
         run: |
-          python3 -m pip install --upgrade pip black flake8
+          python3 -m pip install --upgrade pip black==21.6b0 flake8==3.9.2
 
       - name: Run flake8
         shell: bash

.github/workflows/test.yml

@@ -32,7 +32,7 @@ jobs:
         os: [ubuntu-18.04, macos-10.15]
         python-version: [3.6, 3.7, 3.8, 3.9]
         torch: ["1.8.1"]
-        k2-version: ["1.2.dev20210724"]
+        k2-version: ["1.4.dev20210822"]
       fail-fast: false
 
     steps:
@@ -47,16 +47,10 @@ jobs:
 
       - name: Install Python dependencies
         run: |
-          python3 -m pip install --upgrade pip pytest kaldialign
+          python3 -m pip install --upgrade pip pytest
           pip install k2==${{ matrix.k2-version }}+cpu.torch${{ matrix.torch }} -f https://k2-fsa.org/nightly/
-
+          # icefall requirements
-          # Don't use: pip install lhotse
+          pip install -r requirements.txt
-          # since it installs a version of PyTorch that is not predictable
-          git clone --depth 1 https://github.com/lhotse-speech/lhotse
-          cd lhotse
-          sed -i.bak "/torch/d" requirements.txt
-          pip install -r ./requirements.txt
-
 
       - name: Run tests
         if: startsWith(matrix.os, 'ubuntu')

README.md

@@ -1,67 +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
+## Recipes
-here is just one alternative.
 
-### Install k2
+Please refer to <https://icefall.readthedocs.io/en/latest/recipes/index.html>
+for more information.
 
-Please refer to [k2's installation documentation][k2-install] to install k2.
+We provide two recipes at present:
-If you have any issues about installing k2, please open an issue at
-<https://github.com/k2-fsa/k2/issues>.
 
-### Install lhotse
+  - [yesno][yesno]
+  - [LibriSpeech][librispeech]
 
-Please refer to [lhotse's installation documentation][lhotse-install] to install
+### yesno
-lhotse.
 
-### Install icefall
+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:
 
-`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
 ```
-
+[test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
-To verify `icefall` was installed successfully, you can run:
-
-```bash
-python3 -c "import icefall; print(icefall.__file__)"
 ```
+We do provide a Colab notebook for this recipe.
 
-It should print the path to `icefall`.
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing)
-
-## Run recipes
-
-At present, only LibriSpeech recipe is provided. Please
-follow [egs/librispeech/ASR/README.md][LibriSpeech] to run it.
-
-## Use Pre-trained models
-
-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)
 
 
-[LibriSpeech]: egs/librispeech/ASR/README.md
+### LibriSpeech
-[k2-install]: https://k2.readthedocs.io/en/latest/installation/index.html#
+
-[k2]: https://github.com/k2-fsa/k2
+We provide two models for this recipe: [conformer CTC model][LibriSpeech_conformer_ctc]
-[lhotse]: https://github.com/lhotse-speech/lhotse
+and [TDNN LSTM CTC model][LibriSpeech_tdnn_lstm_ctc].
-[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 +1,77 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+# import os
+# import sys
+# sys.path.insert(0, os.path.abspath('.'))
+
+import sphinx_rtd_theme
+
+
+# -- Project information -----------------------------------------------------
+
+project = "icefall"
+copyright = "2021, icefall development team"
+author = "icefall development team"
+
+# The full version, including alpha/beta/rc tags
+release = "0.1"
+
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    "sphinx_rtd_theme",
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ["_templates"]
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = []
+
+source_suffix = {
+    ".rst": "restructuredtext",
+}
+master_doc = "index"
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = "sphinx_rtd_theme"
+html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+html_show_sourcelink = True
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ["_static", "installation/images"]
+
+pygments_style = "sphinx"
+
+numfig = True
+
+html_context = {
+    "display_github": True,
+    "github_user": "k2-fsa",
+    "github_repo": "icefall",
+    "github_version": "master",
+    "conf_py_path": "/icefall/docs/source/",
+}

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

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+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="122" height="20" role="img" aria-label="device: CPU | CUDA"><title>device: CPU | CUDA</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="122" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="45" height="20" fill="#555"/><rect x="45" width="77" height="20" fill="#fe7d37"/><rect width="122" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="235" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="350">device</text><text x="235" y="140" transform="scale(.1)" fill="#fff" textLength="350">device</text><text aria-hidden="true" x="825" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="670">CPU | CUDA</text><text x="825" y="140" transform="scale(.1)" fill="#fff" textLength="670">CPU | CUDA</text></g></svg>

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+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="114" height="20" role="img" aria-label="os: Linux | macOS"><title>os: Linux | macOS</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="114" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="23" height="20" fill="#555"/><rect x="23" width="91" height="20" fill="#ff69b4"/><rect width="114" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="125" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="130">os</text><text x="125" y="140" transform="scale(.1)" fill="#fff" textLength="130">os</text><text aria-hidden="true" x="675" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="810">Linux | macOS</text><text x="675" y="140" transform="scale(.1)" fill="#fff" textLength="810">Linux | macOS</text></g></svg>

docs/source/installation/images/python-3.6_3.7_3.8_3.9-blue.svg

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+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="170" height="20" role="img" aria-label="python: 3.6 | 3.7 | 3.8 | 3.9"><title>python: 3.6 | 3.7 | 3.8 | 3.9</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="170" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="49" height="20" fill="#555"/><rect x="49" width="121" height="20" fill="#007ec6"/><rect width="170" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="255" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="390">python</text><text x="255" y="140" transform="scale(.1)" fill="#fff" textLength="390">python</text><text aria-hidden="true" x="1085" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="1110">3.6 | 3.7 | 3.8 | 3.9</text><text x="1085" y="140" transform="scale(.1)" fill="#fff" textLength="1110">3.6 | 3.7 | 3.8 | 3.9</text></g></svg>

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+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="286" height="20" role="img" aria-label="torch: 1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0"><title>torch: 1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="286" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="39" height="20" fill="#555"/><rect x="39" width="247" height="20" fill="#97ca00"/><rect width="286" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="205" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="290">torch</text><text x="205" y="140" transform="scale(.1)" fill="#fff" textLength="290">torch</text><text aria-hidden="true" x="1615" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="2370">1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0</text><text x="1615" y="140" transform="scale(.1)" fill="#fff" textLength="2370">1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0</text></g></svg>

docs/source/installation/index.rst

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+.. _install icefall:
+
+Installation
+============
+
+- |os|
+- |device|
+- |python_versions|
+- |torch_versions|
+
+.. |os| image:: ./images/os-Linux_macOS-ff69b4.svg
+  :alt: Supported operating systems
+
+.. |device| image:: ./images/device-CPU_CUDA-orange.svg
+  :alt: Supported devices
+
+.. |python_versions| image:: ./images/python-3.6_3.7_3.8_3.9-blue.svg
+  :alt: Supported python versions
+
+.. |torch_versions| image:: ./images/torch-1.6.0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg
+  :alt: Supported PyTorch versions
+
+icefall depends on `k2 <https://github.com/k2-fsa/k2>`_ and
+`lhotse <https://github.com/lhotse-speech/lhotse>`_.
+
+We recommend you to install ``k2`` first, as ``k2`` is bound to
+a specific version of PyTorch after compilation. Install ``k2`` also
+installs its dependency PyTorch, which can be reused by ``lhotse``.
+
+
+(1) Install k2
+--------------
+
+Please refer to `<https://k2.readthedocs.io/en/latest/installation/index.html>`_
+to install `k2`.
+
+.. HINT::
+
+  If you have already installed PyTorch and don't want to replace it,
+  please install a version of k2 that is compiled against the version
+  of PyTorch you are using.
+
+(2) Install lhotse
+------------------
+
+Please refer to `<https://lhotse.readthedocs.io/en/latest/getting-started.html#installation>`_
+to install ``lhotse``.
+
+.. HINT::
+
+  Install ``lhotse`` also installs its dependency `torchaudio <https://github.com/pytorch/audio>`_.
+
+(3) Download icefall
+--------------------
+
+icefall is a collection of Python scripts, so you don't need to install it
+and we don't provide a ``setup.py`` to install it.
+
+What you need is to download it and set the environment variable ``PYTHONPATH``
+to point to it.
+
+Assume you want to place ``icefall`` in the folder ``/tmp``. The
+following commands show you how to setup ``icefall``:
+
+
+.. code-block:: bash
+
+  cd /tmp
+  git clone https://github.com/k2-fsa/icefall
+  cd icefall
+  pip install -r requirements.txt
+  export PYTHONPATH=/tmp/icefall:$PYTHONPATH
+
+.. HINT::
+
+  You can put several versions of ``icefall`` in the same virtual environment.
+  To switch among different versions of ``icefall``, just set ``PYTHONPATH``
+  to point to the version you want.
+
+
+Installation example
+--------------------
+
+The following shows an example about setting up the environment.
+
+
+(1) Create a virtual environment
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+  $ virtualenv -p python3.8  test-icefall
+
+  created virtual environment CPython3.8.6.final.0-64 in 1540ms
+    creator CPython3Posix(dest=/ceph-fj/fangjun/test-icefall, clear=False, no_vcs_ignore=False, global=False)
+    seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/fangjun/.local/share/v
+  irtualenv)
+      added seed packages: pip==21.1.3, setuptools==57.4.0, wheel==0.36.2
+    activators BashActivator,CShellActivator,FishActivator,PowerShellActivator,PythonActivator,XonshActivator
+
+
+(2) Activate your virtual environment
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+  $ source test-icefall/bin/activate
+
+(3) Install k2
+~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+  $ pip install k2==1.4.dev20210822+cpu.torch1.9.0 -f https://k2-fsa.org/nightly/index.html
+
+  Looking in links: https://k2-fsa.org/nightly/index.html
+  Collecting k2==1.4.dev20210822+cpu.torch1.9.0
+    Downloading https://k2-fsa.org/nightly/whl/k2-1.4.dev20210822%2Bcpu.torch1.9.0-cp38-cp38-linux_x86_64.whl (1.6 MB)
+       |________________________________| 1.6 MB 185 kB/s
+  Collecting graphviz
+    Downloading graphviz-0.17-py3-none-any.whl (18 kB)
+  Collecting torch==1.9.0
+    Using cached torch-1.9.0-cp38-cp38-manylinux1_x86_64.whl (831.4 MB)
+  Collecting typing-extensions
+    Using cached typing_extensions-3.10.0.0-py3-none-any.whl (26 kB)
+  Installing collected packages: typing-extensions, torch, graphviz, k2
+  Successfully installed graphviz-0.17 k2-1.4.dev20210822+cpu.torch1.9.0 torch-1.9.0 typing-extensions-3.10.0.0
+
+.. WARNING::
+
+  We choose to install a CPU version of k2 for testing. You would probably want to install
+  a CUDA version of k2.
+
+
+(4) Install lhotse
+~~~~~~~~~~~~~~~~~~
+
+.. code-block::
+
+  $ pip install git+https://github.com/lhotse-speech/lhotse
+
+  Collecting git+https://github.com/lhotse-speech/lhotse
+    Cloning https://github.com/lhotse-speech/lhotse to /tmp/pip-req-build-7b1b76ge
+    Running command git clone -q https://github.com/lhotse-speech/lhotse /tmp/pip-req-build-7b1b76ge
+  Collecting audioread>=2.1.9
+    Using cached audioread-2.1.9-py3-none-any.whl
+  Collecting SoundFile>=0.10
+    Using cached SoundFile-0.10.3.post1-py2.py3-none-any.whl (21 kB)
+  Collecting click>=7.1.1
+    Using cached click-8.0.1-py3-none-any.whl (97 kB)
+  Collecting cytoolz>=0.10.1
+    Using cached cytoolz-0.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.9 MB)
+  Collecting dataclasses
+    Using cached dataclasses-0.6-py3-none-any.whl (14 kB)
+  Collecting h5py>=2.10.0
+    Downloading h5py-3.4.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (4.5 MB)
+       |________________________________| 4.5 MB 684 kB/s
+  Collecting intervaltree>=3.1.0
+    Using cached intervaltree-3.1.0-py2.py3-none-any.whl
+  Collecting lilcom>=1.1.0
+    Using cached lilcom-1.1.1-cp38-cp38-linux_x86_64.whl
+  Collecting numpy>=1.18.1
+    Using cached numpy-1.21.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.8 MB)
+  Collecting packaging
+    Using cached packaging-21.0-py3-none-any.whl (40 kB)
+  Collecting pyyaml>=5.3.1
+    Using cached PyYAML-5.4.1-cp38-cp38-manylinux1_x86_64.whl (662 kB)
+  Collecting tqdm
+    Downloading tqdm-4.62.1-py2.py3-none-any.whl (76 kB)
+       |________________________________| 76 kB 2.7 MB/s
+  Collecting torchaudio==0.9.0
+    Downloading torchaudio-0.9.0-cp38-cp38-manylinux1_x86_64.whl (1.9 MB)
+       |________________________________| 1.9 MB 73.1 MB/s
+  Requirement already satisfied: torch==1.9.0 in ./test-icefall/lib/python3.8/site-packages (from torchaudio==0.9.0->lhotse===0.8.0.dev
+  -2a1410b-clean) (1.9.0)
+  Requirement already satisfied: typing-extensions in ./test-icefall/lib/python3.8/site-packages (from torch==1.9.0->torchaudio==0.9.0-
+  >lhotse===0.8.0.dev-2a1410b-clean) (3.10.0.0)
+  Collecting toolz>=0.8.0
+    Using cached toolz-0.11.1-py3-none-any.whl (55 kB)
+  Collecting sortedcontainers<3.0,>=2.0
+    Using cached sortedcontainers-2.4.0-py2.py3-none-any.whl (29 kB)
+  Collecting cffi>=1.0
+    Using cached cffi-1.14.6-cp38-cp38-manylinux1_x86_64.whl (411 kB)
+  Collecting pycparser
+    Using cached pycparser-2.20-py2.py3-none-any.whl (112 kB)
+  Collecting pyparsing>=2.0.2
+    Using cached pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)
+  Building wheels for collected packages: lhotse
+    Building wheel for lhotse (setup.py) ... done
+    Created wheel for lhotse: filename=lhotse-0.8.0.dev_2a1410b_clean-py3-none-any.whl size=342242 sha256=f683444afa4dc0881133206b4646a
+  9d0f774224cc84000f55d0a67f6e4a37997
+    Stored in directory: /tmp/pip-ephem-wheel-cache-ftu0qysz/wheels/7f/7a/8e/a0bf241336e2e3cb573e1e21e5600952d49f5162454f2e612f
+    WARNING: Built wheel for lhotse is invalid: Metadata 1.2 mandates PEP 440 version, but '0.8.0.dev-2a1410b-clean' is not
+  Failed to build lhotse
+  Installing collected packages: pycparser, toolz, sortedcontainers, pyparsing, numpy, cffi, tqdm, torchaudio, SoundFile, pyyaml, packa
+  ging, lilcom, intervaltree, h5py, dataclasses, cytoolz, click, audioread, lhotse
+      Running setup.py install for lhotse ... done
+    DEPRECATION: lhotse was installed using the legacy 'setup.py install' method, because a wheel could not be built for it. A possible
+   replacement is to fix the wheel build issue reported above. You can find discussion regarding this at https://github.com/pypa/pip/is
+  sues/8368.
+  Successfully installed SoundFile-0.10.3.post1 audioread-2.1.9 cffi-1.14.6 click-8.0.1 cytoolz-0.11.0 dataclasses-0.6 h5py-3.4.0 inter
+  valtree-3.1.0 lhotse-0.8.0.dev-2a1410b-clean lilcom-1.1.1 numpy-1.21.2 packaging-21.0 pycparser-2.20 pyparsing-2.4.7 pyyaml-5.4.1 sor
+  tedcontainers-2.4.0 toolz-0.11.1 torchaudio-0.9.0 tqdm-4.62.1
+
+(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 +1,18 @@
+Recipes
+=======
+
+This page contains various recipes in ``icefall``.
+Currently, only speech recognition recipes are provided.
+
+We may add recipes for other tasks as well in the future.
+
+.. we put the yesno recipe as the first recipe since it is the simplest one.
+.. Other recipes are listed in a alphabetical order.
+
+.. toctree::
+   :maxdepth: 2
+
+   yesno
+
+   librispeech
+

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 scaled down lattice scores so that we can 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 the 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 soundfiles
+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, 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:
+
+.. 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 +21,26 @@ 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|
 
+
+### 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/conformer.py

@@ -1,7 +1,20 @@
 #!/usr/bin/env python3
-
 # Copyright (c)  2021  University of Chinese Academy of Sciences (author: Han Zhu)
-# Apache 2.0
+#
+# 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 math
 import warnings
@@ -396,7 +409,7 @@ class RelPositionalEncoding(torch.nn.Module):
             :,
             self.pe.size(1) // 2
             - x.size(1)
-            + 1 : self.pe.size(1) // 2
+            + 1 : self.pe.size(1) // 2  # noqa E203
             + x.size(1),
         ]
         return self.dropout(x), self.dropout(pos_emb)

egs/librispeech/ASR/conformer_ctc/decode.py

@@ -1,8 +1,20 @@
 #!/usr/bin/env python3
-
 # Copyright 2021 Xiaomi Corporation (Author: Liyong Guo, 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.
 
-# (still working in progress)
 
 import argparse
 import logging
@@ -45,28 +57,63 @@ 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.
+        """,
     )
 
     return parser
@@ -92,21 +139,6 @@ 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
@@ -117,7 +149,7 @@ 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,
@@ -151,8 +183,8 @@ 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:
@@ -205,7 +237,7 @@ 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,
         )
 
@@ -225,7 +257,7 @@ 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 [
@@ -271,7 +303,7 @@ 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
 
@@ -281,7 +313,7 @@ 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,
@@ -297,8 +329,8 @@ 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:
@@ -332,7 +364,7 @@ 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,
@@ -528,7 +560,7 @@ 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/pretrained.py

@@ -1,4 +1,20 @@
 #!/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
@@ -59,7 +75,7 @@ def get_parser():
             decoding lattice and then use 1best to decode the
             rescored lattice.
             We call it HLG decoding + n-gram LM rescoring.
-        (3) attention-decoder - Extract n paths from he rescored
+        (3) attention-decoder - Extract n paths from the rescored
             lattice and use the transformer attention decoder for
             rescoring.
             We call it HLG decoding + n-gram LM rescoring + attention
@@ -245,9 +261,9 @@ def main():
     if params.method in ["whole-lattice-rescoring", "attention-decoder"]:
         logging.info(f"Loading G from {params.G}")
         G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
-        G = G.to(device)
         # 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()
@@ -268,7 +284,7 @@ def main():
     )
     waves = [w.to(device) for w in waves]
 
-    logging.info(f"Decoding started")
+    logging.info("Decoding started")
     features = fbank(waves)
 
     features = pad_sequence(
@@ -338,7 +354,7 @@ def main():
         s += f"{filename}:\n{words}\n\n"
     logging.info(s)
 
-    logging.info(f"Decoding Done")
+    logging.info("Decoding Done")
 
 
 if __name__ == "__main__":

egs/librispeech/ASR/conformer_ctc/subsampling.py

@@ -1,3 +1,20 @@
+# 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 torch
 import torch.nn as nn
 

egs/librispeech/ASR/conformer_ctc/test_subsampling.py

@@ -1,4 +1,20 @@
 #!/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.
+
 
 from subsampling import Conv2dSubsampling
 from subsampling import VggSubsampling

egs/librispeech/ASR/conformer_ctc/test_transformer.py

@@ -1,4 +1,20 @@
 #!/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 torch
 from transformer import (

egs/librispeech/ASR/conformer_ctc/train.py

@@ -1,6 +1,20 @@
 #!/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.
 
-# This is just at the very beginning ...
 
 import argparse
 import logging
@@ -60,6 +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
 
 
@@ -89,11 +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.
@@ -129,8 +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/conformer_ctc/transformer.py

@@ -1,5 +1,19 @@
-# Copyright (c) 2021 University of Chinese Academy of Sciences (author: Han Zhu)
+# Copyright    2021 University of Chinese Academy of Sciences (author: Han Zhu)
-# Apache 2.0
+#
+# 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 math
 from typing import Dict, List, Optional, Tuple
@@ -779,7 +793,8 @@ class Noam(object):
 
 class LabelSmoothingLoss(nn.Module):
     """
-    Label-smoothing loss. KL-divergence between q_{smoothed ground truth prob.}(w)
+    Label-smoothing loss. KL-divergence between
+    q_{smoothed ground truth prob.}(w)
     and p_{prob. computed by model}(w) is minimized.
     Modified from
     https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/transformer/label_smoothing_loss.py  # noqa
@@ -864,7 +879,8 @@ def encoder_padding_mask(
          frames, before subsampling)
 
     Returns:
-        Tensor: Mask tensor of dimension (batch_size, input_length), True denote the masked indices.
+        Tensor: Mask tensor of dimension (batch_size, input_length),
+        True denote the masked indices.
     """
     if supervisions is None:
         return None

egs/librispeech/ASR/local/compile_hlg.py

@@ -1,4 +1,20 @@
 #!/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.
+
 
 """
 This script takes as input lang_dir and generates HLG from

egs/librispeech/ASR/local/compute_fbank_librispeech.py

@@ -1,8 +1,24 @@
 #!/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.
+
 
 """
 This file computes fbank features of the LibriSpeech dataset.
-Its looks for manifests in the directory data/manifests.
+It looks for manifests in the directory data/manifests.
 
 The generated fbank features are saved in data/fbank.
 """
@@ -17,8 +33,9 @@ from lhotse.recipes.utils import read_manifests_if_cached
 
 from icefall.utils import get_executor
 
-# Torch's multithreaded behavior needs to be disabled or it wastes a lot of CPU and
+# Torch's multithreaded behavior needs to be disabled or
-# slow things down.  Do this outside of main() in case it needs to take effect
+# it wastes a lot of CPU and slow things down.
+# Do this outside of main() in case it needs to take effect
 # even when we are not invoking the main (e.g. when spawning subprocesses).
 torch.set_num_threads(1)
 torch.set_num_interop_threads(1)
@@ -53,7 +70,8 @@ def compute_fbank_librispeech():
                 continue
             logging.info(f"Processing {partition}")
             cut_set = CutSet.from_manifests(
-                recordings=m["recordings"], supervisions=m["supervisions"],
+                recordings=m["recordings"],
+                supervisions=m["supervisions"],
             )
             if "train" in partition:
                 cut_set = (

egs/librispeech/ASR/local/compute_fbank_musan.py

@@ -1,8 +1,24 @@
 #!/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.
+
 
 """
 This file computes fbank features of the musan dataset.
-Its looks for manifests in the directory data/manifests.
+It looks for manifests in the directory data/manifests.
 
 The generated fbank features are saved in data/fbank.
 """
@@ -17,8 +33,9 @@ from lhotse.recipes.utils import read_manifests_if_cached
 
 from icefall.utils import get_executor
 
-# Torch's multithreaded behavior needs to be disabled or it wastes a lot of CPU and
+# Torch's multithreaded behavior needs to be disabled or
-# slow things down.  Do this outside of main() in case it needs to take effect
+# it wastes a lot of CPU and slow things down.
+# Do this outside of main() in case it needs to take effect
 # even when we are not invoking the main (e.g. when spawning subprocesses).
 torch.set_num_threads(1)
 torch.set_num_interop_threads(1)

egs/librispeech/ASR/local/download_lm.py

@@ -1,6 +1,21 @@
 #!/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.
+
 
-# Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
 """
 This file downloads the following LibriSpeech LM files:
 

egs/librispeech/ASR/local/prepare_lang.py

@@ -1,6 +1,20 @@
 #!/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.
 
-# Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
 
 """
 This script takes as input a lexicon file "data/lang_phone/lexicon.txt"

egs/librispeech/ASR/local/prepare_lang_bpe.py

@@ -1,4 +1,20 @@
 #!/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.
+
 
 # Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
 

egs/librispeech/ASR/local/test_prepare_lang.py

@@ -1,4 +1,20 @@
 #!/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.
+
 
 # Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
 

egs/librispeech/ASR/local/train_bpe_model.py

@@ -1,4 +1,20 @@
 #!/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.
+
 
 # You can install sentencepiece via:
 #

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

@@ -1,3 +1,20 @@
+# Copyright      2021  Piotr Żelasko
+#
+# 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
 from functools import lru_cache
@@ -23,7 +40,7 @@ from icefall.utils import str2bool
 
 class LibriSpeechAsrDataModule(DataModule):
     """
-    DataModule for K2 ASR experiments.
+    DataModule for k2 ASR experiments.
     It assumes there is always one train and valid dataloader,
     but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
     and test-other).

egs/librispeech/ASR/tdnn_lstm_ctc/decode.py

@@ -1,4 +1,19 @@
 #!/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
@@ -27,6 +42,7 @@ from icefall.utils import (
     get_texts,
     setup_logger,
     store_transcripts,
+    str2bool,
     write_error_stats,
 )
 
@@ -39,7 +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.",
     )
@@ -51,6 +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
 
 
@@ -72,7 +98,7 @@ def get_params() -> AttributeDict:
             #  - nbest
             #  - nbest-rescoring
             #  - whole-lattice-rescoring
-            "method": "1best",
+            "method": "whole-lattice-rescoring",
             # num_paths is used when method is "nbest" and "nbest-rescoring"
             "num_paths": 30,
         }
@@ -333,7 +359,7 @@ def main():
     logging.info(f"device: {device}")
 
     HLG = k2.Fsa.from_dict(
-        torch.load("data/lang_phone/HLG.pt", map_location="cpu")
+        torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu")
     )
     HLG = HLG.to(device)
     assert HLG.requires_grad is False
@@ -393,6 +419,12 @@ def main():
         logging.info(f"averaging {filenames}")
         model.load_state_dict(average_checkpoints(filenames))
 
+    if params.export:
+        logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt")
+        torch.save(
+            {"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt"
+        )
+
     model.to(device)
     model.eval()
 

egs/librispeech/ASR/tdnn_lstm_ctc/model.py

@@ -1,3 +1,20 @@
+# 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 torch
 import torch.nn as nn
 

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

@@ -1,4 +1,20 @@
 #!/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
@@ -59,6 +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
 
 
@@ -88,11 +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.
@@ -111,6 +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
@@ -127,14 +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",
@@ -382,8 +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"])
@@ -410,6 +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}, "
@@ -417,6 +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(
@@ -433,7 +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

@@ -0,0 +1,14 @@
+## Yesno recipe
+
+This is the simplest ASR recipe in `icefall`.
+
+It can be run on CPU and takes less than 30 seconds to
+get the following WER:
+
+```
+[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

@@ -0,0 +1,134 @@
+#!/usr/bin/env python3
+
+"""
+This script takes as input lang_dir and generates HLG from
+
+    - H, the ctc topology, built from tokens contained in lang_dir/lexicon.txt
+    - L, the lexicon, built from lang_dir/L_disambig.pt
+
+        Caution: We use a lexicon that contains disambiguation symbols
+
+    - G, the LM, built from data/lm/G.fst.txt
+
+The generated HLG is saved in $lang_dir/HLG.pt
+"""
+import argparse
+import logging
+from pathlib import Path
+
+import k2
+import torch
+
+from icefall.lexicon import Lexicon
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        help="""Input and output directory.
+        """,
+    )
+
+    return parser.parse_args()
+
+
+def compile_HLG(lang_dir: str) -> k2.Fsa:
+    """
+    Args:
+      lang_dir:
+        The language directory, e.g., data/lang_phone or data/lang_bpe_5000.
+
+    Return:
+      An FSA representing HLG.
+    """
+    lexicon = Lexicon(lang_dir)
+    max_token_id = max(lexicon.tokens)
+    logging.info(f"Building ctc_topo. max_token_id: {max_token_id}")
+    H = k2.ctc_topo(max_token_id)
+    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt"))
+
+    logging.info("Loading G.fst.txt")
+    with open("data/lm/G.fst.txt") as f:
+        G = k2.Fsa.from_openfst(f.read(), acceptor=False)
+
+    first_token_disambig_id = lexicon.token_table["#0"]
+    first_word_disambig_id = lexicon.word_table["#0"]
+
+    L = k2.arc_sort(L)
+    G = k2.arc_sort(G)
+
+    logging.info("Intersecting L and G")
+    LG = k2.compose(L, G)
+    logging.info(f"LG shape: {LG.shape}")
+
+    logging.info("Connecting LG")
+    LG = k2.connect(LG)
+    logging.info(f"LG shape after k2.connect: {LG.shape}")
+
+    logging.info(type(LG.aux_labels))
+    logging.info("Determinizing LG")
+
+    LG = k2.determinize(LG)
+    logging.info(type(LG.aux_labels))
+
+    logging.info("Connecting LG after k2.determinize")
+    LG = k2.connect(LG)
+
+    logging.info("Removing disambiguation symbols on LG")
+
+    LG.labels[LG.labels >= first_token_disambig_id] = 0
+
+    assert isinstance(LG.aux_labels, k2.RaggedInt)
+    LG.aux_labels.values()[LG.aux_labels.values() >= 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)
+
+    logging.info("Arc sorting LG")
+    LG = k2.arc_sort(LG)
+
+    logging.info("Composing H and LG")
+    # CAUTION: The name of the inner_labels is fixed
+    # to `tokens`. If you want to change it, please
+    # also change other places in icefall that are using
+    # it.
+    HLG = k2.compose(H, LG, inner_labels="tokens")
+
+    logging.info("Connecting LG")
+    HLG = k2.connect(HLG)
+
+    logging.info("Arc sorting LG")
+    HLG = k2.arc_sort(HLG)
+    logging.info(f"HLG.shape: {HLG.shape}")
+
+    return HLG
+
+
+def main():
+    args = get_args()
+    lang_dir = Path(args.lang_dir)
+
+    if (lang_dir / "HLG.pt").is_file():
+        logging.info(f"{lang_dir}/HLG.pt already exists - skipping")
+        return
+
+    logging.info(f"Processing {lang_dir}")
+
+    HLG = compile_HLG(lang_dir)
+    logging.info(f"Saving HLG.pt to {lang_dir}")
+    torch.save(HLG.as_dict(), f"{lang_dir}/HLG.pt")
+
+
+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/local/compute_fbank_yesno.py

@@ -0,0 +1,81 @@
+#!/usr/bin/env python3
+
+"""
+This file computes fbank features of the yesno dataset.
+It looks for manifests in the directory data/manifests.
+
+The generated fbank features are saved in data/fbank.
+"""
+
+import logging
+import os
+from pathlib import Path
+
+import torch
+from lhotse import CutSet, Fbank, FbankConfig, LilcomHdf5Writer
+from lhotse.recipes.utils import read_manifests_if_cached
+
+from icefall.utils import get_executor
+
+# Torch's multithreaded behavior needs to be disabled or it wastes a
+# lot of CPU and slow things down.
+# Do this outside of main() in case it needs to take effect
+# even when we are not invoking the main (e.g. when spawning subprocesses).
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+
+def compute_fbank_yesno():
+    src_dir = Path("data/manifests")
+    output_dir = Path("data/fbank")
+
+    # This dataset is rather small, so we use only one job
+    num_jobs = min(1, os.cpu_count())
+    num_mel_bins = 23
+
+    dataset_parts = (
+        "train",
+        "test",
+    )
+    manifests = read_manifests_if_cached(
+        dataset_parts=dataset_parts, output_dir=src_dir
+    )
+    assert manifests is not None
+
+    extractor = Fbank(FbankConfig(num_mel_bins=num_mel_bins))
+
+    with get_executor() as ex:  # Initialize the executor only once.
+        for partition, m in manifests.items():
+            if (output_dir / f"cuts_{partition}.json.gz").is_file():
+                logging.info(f"{partition} already exists - skipping.")
+                continue
+            logging.info(f"Processing {partition}")
+            cut_set = CutSet.from_manifests(
+                recordings=m["recordings"],
+                supervisions=m["supervisions"],
+            )
+            if "train" in partition:
+                cut_set = (
+                    cut_set
+                    + cut_set.perturb_speed(0.9)
+                    + cut_set.perturb_speed(1.1)
+                )
+            cut_set = cut_set.compute_and_store_features(
+                extractor=extractor,
+                storage_path=f"{output_dir}/feats_{partition}",
+                # when an executor is specified, make more partitions
+                num_jobs=num_jobs if ex is None else 1,  # use one job
+                executor=ex,
+                storage_type=LilcomHdf5Writer,
+            )
+            cut_set.to_json(output_dir / f"cuts_{partition}.json.gz")
+
+
+if __name__ == "__main__":
+    formatter = (
+        "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    )
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    compute_fbank_yesno()

egs/yesno/ASR/local/prepare_lang.py

@@ -0,0 +1,367 @@
+#!/usr/bin/env python3
+
+# Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
+
+"""
+This script takes as input a lexicon file "data/lang_phone/lexicon.txt"
+consisting of words and tokens (i.e., phones) and does the following:
+
+1. Add disambiguation symbols to the lexicon and generate lexicon_disambig.txt
+
+2. Generate tokens.txt, the token table mapping a token to a unique integer.
+
+3. Generate words.txt, the word table mapping a word to a unique integer.
+
+4. Generate L.pt, in k2 format. It can be loaded by
+
+        d = torch.load("L.pt")
+        lexicon = k2.Fsa.from_dict(d)
+
+5. Generate L_disambig.pt, in k2 format.
+"""
+import math
+from collections import defaultdict
+from pathlib import Path
+from typing import Any, Dict, List, Tuple
+
+import k2
+import torch
+
+from icefall.lexicon import read_lexicon, write_lexicon
+
+Lexicon = List[Tuple[str, List[str]]]
+
+
+def write_mapping(filename: str, sym2id: Dict[str, int]) -> None:
+    """Write a symbol to ID mapping to a file.
+
+    Note:
+      No need to implement `read_mapping` as it can be done
+      through :func:`k2.SymbolTable.from_file`.
+
+    Args:
+      filename:
+        Filename to save the mapping.
+      sym2id:
+        A dict mapping symbols to IDs.
+    Returns:
+      Return None.
+    """
+    with open(filename, "w", encoding="utf-8") as f:
+        for sym, i in sym2id.items():
+            f.write(f"{sym} {i}\n")
+
+
+def get_tokens(lexicon: Lexicon) -> List[str]:
+    """Get tokens from a lexicon.
+
+    Args:
+      lexicon:
+        It is the return value of :func:`read_lexicon`.
+    Returns:
+      Return a list of unique tokens.
+    """
+    ans = set()
+    for _, tokens in lexicon:
+        ans.update(tokens)
+    sorted_ans = sorted(list(ans))
+    return sorted_ans
+
+
+def get_words(lexicon: Lexicon) -> List[str]:
+    """Get words from a lexicon.
+
+    Args:
+      lexicon:
+        It is the return value of :func:`read_lexicon`.
+    Returns:
+      Return a list of unique words.
+    """
+    ans = set()
+    for word, _ in lexicon:
+        ans.add(word)
+    sorted_ans = sorted(list(ans))
+    return sorted_ans
+
+
+def add_disambig_symbols(lexicon: Lexicon) -> Tuple[Lexicon, int]:
+    """It adds pseudo-token disambiguation symbols #1, #2 and so on
+    at the ends of tokens to ensure that all pronunciations are different,
+    and that none is a prefix of another.
+
+    See also add_lex_disambig.pl from kaldi.
+
+    Args:
+      lexicon:
+        It is returned by :func:`read_lexicon`.
+    Returns:
+      Return a tuple with two elements:
+
+        - The output lexicon with disambiguation symbols
+        - The ID of the max disambiguation symbol that appears
+          in the lexicon
+    """
+
+    # (1) Work out the count of each token-sequence in the
+    # lexicon.
+    count = defaultdict(int)
+    for _, tokens in lexicon:
+        count[" ".join(tokens)] += 1
+
+    # (2) For each left sub-sequence of each token-sequence, note down
+    # that it exists (for identifying prefixes of longer strings).
+    issubseq = defaultdict(int)
+    for _, tokens in lexicon:
+        tokens = tokens.copy()
+        tokens.pop()
+        while tokens:
+            issubseq[" ".join(tokens)] = 1
+            tokens.pop()
+
+    # (3) For each entry in the lexicon:
+    # if the token sequence is unique and is not a
+    # prefix of another word, no disambig symbol.
+    # Else output #1, or #2, #3, ... if the same token-seq
+    # has already been assigned a disambig symbol.
+    ans = []
+
+    # We start with #1 since #0 has its own purpose
+    first_allowed_disambig = 1
+    max_disambig = first_allowed_disambig - 1
+    last_used_disambig_symbol_of = defaultdict(int)
+
+    for word, tokens in lexicon:
+        tokenseq = " ".join(tokens)
+        assert tokenseq != ""
+        if issubseq[tokenseq] == 0 and count[tokenseq] == 1:
+            ans.append((word, tokens))
+            continue
+
+        cur_disambig = last_used_disambig_symbol_of[tokenseq]
+        if cur_disambig == 0:
+            cur_disambig = first_allowed_disambig
+        else:
+            cur_disambig += 1
+
+        if cur_disambig > max_disambig:
+            max_disambig = cur_disambig
+        last_used_disambig_symbol_of[tokenseq] = cur_disambig
+        tokenseq += f" #{cur_disambig}"
+        ans.append((word, tokenseq.split()))
+    return ans, max_disambig
+
+
+def generate_id_map(symbols: List[str]) -> Dict[str, int]:
+    """Generate ID maps, i.e., map a symbol to a unique ID.
+
+    Args:
+      symbols:
+        A list of unique symbols.
+    Returns:
+      A dict containing the mapping between symbols and IDs.
+    """
+    return {sym: i for i, sym in enumerate(symbols)}
+
+
+def add_self_loops(
+    arcs: List[List[Any]], disambig_token: int, disambig_word: int
+) -> List[List[Any]]:
+    """Adds self-loops to states of an FST to propagate disambiguation symbols
+    through it. They are added on each state with non-epsilon output symbols
+    on at least one arc out of the state.
+
+    See also fstaddselfloops.pl from Kaldi. One difference is that
+    Kaldi uses OpenFst style FSTs and it has multiple final states.
+    This function uses k2 style FSTs and it does not need to add self-loops
+    to the final state.
+
+    The input label of a self-loop is `disambig_token`, while the output
+    label is `disambig_word`.
+
+    Args:
+      arcs:
+        A list-of-list. The sublist contains
+        `[src_state, dest_state, label, aux_label, score]`
+      disambig_token:
+        It is the token ID of the symbol `#0`.
+      disambig_word:
+        It is the word ID of the symbol `#0`.
+
+    Return:
+      Return new `arcs` containing self-loops.
+    """
+    states_needs_self_loops = set()
+    for arc in arcs:
+        src, dst, ilabel, olabel, score = arc
+        if olabel != 0:
+            states_needs_self_loops.add(src)
+
+    ans = []
+    for s in states_needs_self_loops:
+        ans.append([s, s, disambig_token, disambig_word, 0])
+
+    return arcs + ans
+
+
+def lexicon_to_fst(
+    lexicon: Lexicon,
+    token2id: Dict[str, int],
+    word2id: Dict[str, int],
+    sil_token: str = "SIL",
+    sil_prob: float = 0.5,
+    need_self_loops: bool = False,
+) -> k2.Fsa:
+    """Convert a lexicon to an FST (in k2 format) with optional silence at
+    the beginning and end of each word.
+
+    Args:
+      lexicon:
+        The input lexicon. See also :func:`read_lexicon`
+      token2id:
+        A dict mapping tokens to IDs.
+      word2id:
+        A dict mapping words to IDs.
+      sil_token:
+        The silence token.
+      sil_prob:
+        The probability for adding a silence at the beginning and end
+        of the word.
+      need_self_loops:
+        If True, add self-loop to states with non-epsilon output symbols
+        on at least one arc out of the state. The input label for this
+        self loop is `token2id["#0"]` and the output label is `word2id["#0"]`.
+    Returns:
+      Return an instance of `k2.Fsa` representing the given lexicon.
+    """
+    assert sil_prob > 0.0 and sil_prob < 1.0
+    # CAUTION: we use score, i.e, negative cost.
+    sil_score = math.log(sil_prob)
+    no_sil_score = math.log(1.0 - sil_prob)
+
+    start_state = 0
+    loop_state = 1  # words enter and leave from here
+    sil_state = 2  # words terminate here when followed by silence; this state
+    # has a silence transition to loop_state.
+    next_state = 3  # the next un-allocated state, will be incremented as we go.
+    arcs = []
+
+    assert token2id["<eps>"] == 0
+    assert word2id["<eps>"] == 0
+
+    eps = 0
+
+    sil_token = token2id[sil_token]
+
+    arcs.append([start_state, loop_state, eps, eps, no_sil_score])
+    arcs.append([start_state, sil_state, eps, eps, sil_score])
+    arcs.append([sil_state, loop_state, sil_token, eps, 0])
+
+    for word, tokens in lexicon:
+        assert len(tokens) > 0, f"{word} has no pronunciations"
+        cur_state = loop_state
+
+        word = word2id[word]
+        tokens = [token2id[i] for i in tokens]
+
+        for i in range(len(tokens) - 1):
+            w = word if i == 0 else eps
+            arcs.append([cur_state, next_state, tokens[i], w, 0])
+
+            cur_state = next_state
+            next_state += 1
+
+        # now for the last token of this word
+        # It has two out-going arcs, one to the loop state,
+        # the other one to the sil_state.
+        i = len(tokens) - 1
+        w = word if i == 0 else eps
+        arcs.append([cur_state, loop_state, tokens[i], w, no_sil_score])
+        arcs.append([cur_state, sil_state, tokens[i], w, sil_score])
+
+    if need_self_loops:
+        disambig_token = token2id["#0"]
+        disambig_word = word2id["#0"]
+        arcs = add_self_loops(
+            arcs,
+            disambig_token=disambig_token,
+            disambig_word=disambig_word,
+        )
+
+    final_state = next_state
+    arcs.append([loop_state, final_state, -1, -1, 0])
+    arcs.append([final_state])
+
+    arcs = sorted(arcs, key=lambda arc: arc[0])
+    arcs = [[str(i) for i in arc] for arc in arcs]
+    arcs = [" ".join(arc) for arc in arcs]
+    arcs = "\n".join(arcs)
+
+    fsa = k2.Fsa.from_str(arcs, acceptor=False)
+    return fsa
+
+
+def main():
+    out_dir = Path("data/lang_phone")
+    lexicon_filename = out_dir / "lexicon.txt"
+    sil_token = "SIL"
+    sil_prob = 0.5
+
+    lexicon = read_lexicon(lexicon_filename)
+    tokens = get_tokens(lexicon)
+    words = get_words(lexicon)
+
+    lexicon_disambig, max_disambig = add_disambig_symbols(lexicon)
+
+    for i in range(max_disambig + 1):
+        disambig = f"#{i}"
+        assert disambig not in tokens
+        tokens.append(f"#{i}")
+
+    assert "<eps>" not in tokens
+    tokens = ["<eps>"] + tokens
+
+    assert "<eps>" not in words
+    assert "#0" not in words
+    assert "<s>" not in words
+    assert "</s>" not in words
+
+    words = ["<eps>"] + words + ["#0", "<s>", "</s>"]
+
+    token2id = generate_id_map(tokens)
+    word2id = generate_id_map(words)
+
+    write_mapping(out_dir / "tokens.txt", token2id)
+    write_mapping(out_dir / "words.txt", word2id)
+    write_lexicon(out_dir / "lexicon_disambig.txt", lexicon_disambig)
+
+    L = lexicon_to_fst(
+        lexicon,
+        token2id=token2id,
+        word2id=word2id,
+        sil_token=sil_token,
+        sil_prob=sil_prob,
+    )
+
+    L_disambig = lexicon_to_fst(
+        lexicon_disambig,
+        token2id=token2id,
+        word2id=word2id,
+        sil_token=sil_token,
+        sil_prob=sil_prob,
+        need_self_loops=True,
+    )
+    torch.save(L.as_dict(), out_dir / "L.pt")
+    torch.save(L_disambig.as_dict(), out_dir / "L_disambig.pt")
+
+    if False:
+        # Just for debugging, will remove it
+        L.labels_sym = k2.SymbolTable.from_file(out_dir / "tokens.txt")
+        L.aux_labels_sym = k2.SymbolTable.from_file(out_dir / "words.txt")
+        L_disambig.labels_sym = L.labels_sym
+        L_disambig.aux_labels_sym = L.aux_labels_sym
+        L.draw(out_dir / "L.png", title="L")
+        L_disambig.draw(out_dir / "L_disambig.png", title="L_disambig")
+
+
+if __name__ == "__main__":
+    main()

egs/yesno/ASR/prepare.sh

@@ -0,0 +1,93 @@
+#!/usr/bin/env bash
+
+set -eou pipefail
+
+stage=-1
+stop_stage=100
+
+dl_dir=$PWD/download
+
+lang_dir=data/lang_phone
+lm_dir=data/lm
+
+. shared/parse_options.sh || exit 1
+
+mkdir -p $lang_dir
+mkdir -p $lm_dir
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+log "dl_dir: $dl_dir"
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+  log "stage 0: Download data"
+  mkdir -p $dl_dir
+
+  if [ ! -f $dl_dir/waves_yesno/.completed ]; then
+    lhotse download yesno $dl_dir
+  fi
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+  log "Stage 1: Prepare yesno manifest"
+  mkdir -p data/manifests
+  lhotse prepare yesno $dl_dir/waves_yesno data/manifests
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+  log "Stage 2: Compute fbank for yesno"
+  mkdir -p data/fbank
+  ./local/compute_fbank_yesno.py
+fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+  log "Stage 3: Prepare lang"
+  # NOTE: "<UNK> SIL" is added for implementation convenience
+  # as the graph compiler code requires that there is a OOV word
+  # in the lexicon.
+  (
+    echo "<SIL> SIL"
+    echo "YES Y"
+    echo "NO N"
+    echo "<UNK> SIL"
+  ) > $lang_dir/lexicon.txt
+
+  ./local/prepare_lang.py
+fi
+
+if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
+  log "Stage 4: Prepare G"
+  # We use a unigram G
+  cat <<EOF > $lm_dir/G.arpa
+
+\data\\
+ngram 1=4
+
+\1-grams:
+-1 NO
+-1 YES
+-99 <s>
+-1 </s>
+
+\end\\
+
+EOF
+
+  if [ ! -f $lm_dir/G.fst.txt ]; then
+    python3 -m kaldilm \
+      --read-symbol-table="$lang_dir/words.txt" \
+      --disambig-symbol='#0' \
+      $lm_dir/G.arpa > $lm_dir/G.fst.txt
+  fi
+fi
+
+if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
+  log "Stage 5: Compile HLG"
+  if [ ! -f $lang_dir/HLG.pt ]; then
+    ./local/compile_hlg.py --lang-dir $lang_dir
+  fi
+fi

egs/yesno/ASR/shared

@@ -0,0 +1 @@
+../../../icefall/shared/

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

@@ -0,0 +1,248 @@
+# Copyright      2021  Piotr Żelasko
+#                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
+from functools import lru_cache
+from pathlib import Path
+from typing import List
+
+from lhotse import CutSet, Fbank, FbankConfig, load_manifest
+from lhotse.dataset import (
+    BucketingSampler,
+    CutConcatenate,
+    K2SpeechRecognitionDataset,
+    PrecomputedFeatures,
+    SingleCutSampler,
+)
+from lhotse.dataset.input_strategies import OnTheFlyFeatures
+from torch.utils.data import DataLoader
+
+from icefall.dataset.datamodule import DataModule
+from icefall.utils import str2bool
+
+
+class YesNoAsrDataModule(DataModule):
+    """
+    DataModule for k2 ASR experiments.
+    It assumes there is always one train dataloader,
+    but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
+    and test-other).
+
+    It contains all the common data pipeline modules used in ASR
+    experiments, e.g.:
+    - dynamic batch size,
+    - bucketing samplers,
+    - cut concatenation,
+    - augmentation,
+    - on-the-fly feature extraction
+    """
+
+    @classmethod
+    def add_arguments(cls, parser: argparse.ArgumentParser):
+        super().add_arguments(parser)
+        group = parser.add_argument_group(
+            title="ASR data related options",
+            description="These options are used for the preparation of "
+            "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
+            "effective batch sizes, sampling strategies, applied data "
+            "augmentations, etc.",
+        )
+        group.add_argument(
+            "--feature-dir",
+            type=Path,
+            default=Path("data/fbank"),
+            help="Path to directory with train/test cuts.",
+        )
+        group.add_argument(
+            "--max-duration",
+            type=int,
+            default=30.0,
+            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,
+            help="When enabled, the batches will come from buckets of "
+            "similar duration (saves padding frames).",
+        )
+        group.add_argument(
+            "--num-buckets",
+            type=int,
+            default=10,
+            help="The number of buckets for the BucketingSampler"
+            "(you might want to increase it for larger datasets).",
+        )
+        group.add_argument(
+            "--concatenate-cuts",
+            type=str2bool,
+            default=False,
+            help="When enabled, utterances (cuts) will be concatenated "
+            "to minimize the amount of padding.",
+        )
+        group.add_argument(
+            "--duration-factor",
+            type=float,
+            default=1.0,
+            help="Determines the maximum duration of a concatenated cut "
+            "relative to the duration of the longest cut in a batch.",
+        )
+        group.add_argument(
+            "--gap",
+            type=float,
+            default=1.0,
+            help="The amount of padding (in seconds) inserted between "
+            "concatenated cuts. This padding is filled with noise when "
+            "noise augmentation is used.",
+        )
+        group.add_argument(
+            "--on-the-fly-feats",
+            type=str2bool,
+            default=False,
+            help="When enabled, use on-the-fly cut mixing and feature "
+            "extraction. Will drop existing precomputed feature manifests "
+            "if available.",
+        )
+        group.add_argument(
+            "--shuffle",
+            type=str2bool,
+            default=True,
+            help="When enabled (=default), the examples will be "
+            "shuffled for each epoch.",
+        )
+        group.add_argument(
+            "--return-cuts",
+            type=str2bool,
+            default=True,
+            help="When enabled, each batch will have the "
+            "field: batch['supervisions']['cut'] with the cuts that "
+            "were used to construct it.",
+        )
+
+        group.add_argument(
+            "--num-workers",
+            type=int,
+            default=2,
+            help="The number of training dataloader workers that "
+            "collect the batches.",
+        )
+
+    def train_dataloaders(self) -> DataLoader:
+        logging.info("About to get train cuts")
+        cuts_train = self.train_cuts()
+
+        logging.info("About to create train dataset")
+        transforms = []
+        if self.args.concatenate_cuts:
+            logging.info(
+                f"Using cut concatenation with duration factor "
+                f"{self.args.duration_factor} and gap {self.args.gap}."
+            )
+            # Cut concatenation should be the first transform in the list,
+            # so that if we e.g. mix noise in, it will fill the gaps between
+            # different utterances.
+            transforms = [
+                CutConcatenate(
+                    duration_factor=self.args.duration_factor, gap=self.args.gap
+                )
+            ] + transforms
+
+        train = K2SpeechRecognitionDataset(
+            cut_transforms=transforms,
+            return_cuts=self.args.return_cuts,
+        )
+
+        if self.args.on_the_fly_feats:
+            # NOTE: the PerturbSpeed transform should be added only if we
+            # remove it from data prep stage.
+            # Add on-the-fly speed perturbation; since originally it would
+            # have increased epoch size by 3, we will apply prob 2/3 and use
+            # 3x more epochs.
+            # Speed perturbation probably should come first before
+            # concatenation, but in principle the transforms order doesn't have
+            # to be strict (e.g. could be randomized)
+            # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms   # noqa
+            # Drop feats to be on the safe side.
+            train = K2SpeechRecognitionDataset(
+                cut_transforms=transforms,
+                input_strategy=OnTheFlyFeatures(
+                    Fbank(FbankConfig(num_mel_bins=23))
+                ),
+                return_cuts=self.args.return_cuts,
+            )
+
+        if self.args.bucketing_sampler:
+            logging.info("Using BucketingSampler.")
+            train_sampler = BucketingSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+                num_buckets=self.args.num_buckets,
+                bucket_method="equal_duration",
+                drop_last=True,
+            )
+        else:
+            logging.info("Using SingleCutSampler.")
+            train_sampler = SingleCutSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+            )
+        logging.info("About to create train dataloader")
+
+        train_dl = DataLoader(
+            train,
+            sampler=train_sampler,
+            batch_size=None,
+            num_workers=self.args.num_workers,
+            persistent_workers=False,
+        )
+
+        return train_dl
+
+    def test_dataloaders(self) -> DataLoader:
+        logging.info("About to get test cuts")
+        cuts_test = self.test_cuts()
+
+        logging.debug("About to create test dataset")
+        test = K2SpeechRecognitionDataset(
+            input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=23)))
+            if self.args.on_the_fly_feats
+            else PrecomputedFeatures(),
+            return_cuts=self.args.return_cuts,
+        )
+        sampler = SingleCutSampler(
+            cuts_test, max_duration=self.args.max_duration
+        )
+        logging.debug("About to create test dataloader")
+        test_dl = DataLoader(
+            test, batch_size=None, sampler=sampler, num_workers=1
+        )
+        return test_dl
+
+    @lru_cache()
+    def train_cuts(self) -> CutSet:
+        logging.info("About to get train cuts")
+        cuts_train = load_manifest(self.args.feature_dir / "cuts_train.json.gz")
+        return cuts_train
+
+    @lru_cache()
+    def test_cuts(self) -> List[CutSet]:
+        logging.info("About to get test cuts")
+        cuts_test = load_manifest(self.args.feature_dir / "cuts_test.json.gz")
+        return cuts_test

egs/yesno/ASR/tdnn/decode.py

@@ -0,0 +1,321 @@
+#!/usr/bin/env python3
+
+
+import argparse
+import logging
+from pathlib import Path
+from typing import List, Tuple
+
+import k2
+import torch
+import torch.nn as nn
+from asr_datamodule import YesNoAsrDataModule
+from model import Tdnn
+
+from icefall.checkpoint import average_checkpoints, load_checkpoint
+from icefall.decode import get_lattice, one_best_decoding
+from icefall.lexicon import Lexicon
+from icefall.utils import (
+    AttributeDict,
+    get_texts,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    write_error_stats,
+)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=14,
+        help="It specifies the checkpoint to use for decoding."
+        "Note: Epoch counts from 0.",
+    )
+    parser.add_argument(
+        "--avg",
+        type=int,
+        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
+
+
+def get_params() -> AttributeDict:
+    params = AttributeDict(
+        {
+            "exp_dir": Path("tdnn/exp/"),
+            "lang_dir": Path("data/lang_phone"),
+            "lm_dir": Path("data/lm"),
+            "feature_dim": 23,
+            "search_beam": 20,
+            "output_beam": 8,
+            "min_active_states": 30,
+            "max_active_states": 10000,
+            "use_double_scores": True,
+        }
+    )
+    return params
+
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    HLG: k2.Fsa,
+    batch: dict,
+    word_table: k2.SymbolTable,
+) -> List[List[int]]:
+    """Decode one batch and return the result in a list-of-list.
+    Each sub list contains the word IDs for an utterance in the batch.
+
+    Args:
+      params:
+        It's the return value of :func:`get_params`.
+
+        - params.method is "1best", it uses 1best decoding.
+        - params.method is "nbest", it uses nbest decoding.
+
+      model:
+        The neural model.
+      HLG:
+        The decoding graph.
+      batch:
+        It is the return value from iterating
+        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
+        for the format of the `batch`.
+        (https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py)
+      word_table:
+        It is the word symbol table.
+    Returns:
+      Return the decoding result. `len(ans)` == batch size.
+    """
+    device = HLG.device
+    feature = batch["inputs"]
+    assert feature.ndim == 3
+    feature = feature.to(device)
+    # at entry, feature is [N, T, C]
+
+    nnet_output = model(feature)
+    # 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,
+    )
+
+    best_path = one_best_decoding(
+        lattice=lattice, use_double_scores=params.use_double_scores
+    )
+    hyps = get_texts(best_path)
+    hyps = [[word_table[i] for i in ids] for ids in hyps]
+    return hyps
+
+
+def decode_dataset(
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    model: nn.Module,
+    HLG: k2.Fsa,
+    word_table: k2.SymbolTable,
+) -> List[Tuple[List[int], List[int]]]:
+    """Decode dataset.
+
+    Args:
+      dl:
+        PyTorch's dataloader containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural model.
+      HLG:
+        The decoding graph.
+      word_table:
+        It is word symbol table.
+    Returns:
+      Return a tuple contains two elements (ref_text, hyp_text):
+      The first is the reference transcript, and the second is the
+      predicted result.
+    """
+    results = []
+
+    num_cuts = 0
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
+
+    results = []
+    for batch_idx, batch in enumerate(dl):
+        texts = batch["supervisions"]["text"]
+
+        hyps = decode_one_batch(
+            params=params,
+            model=model,
+            HLG=HLG,
+            batch=batch,
+            word_table=word_table,
+        )
+
+        this_batch = []
+        assert len(hyps) == len(texts)
+        for hyp_words, ref_text in zip(hyps, texts):
+            ref_words = ref_text.split()
+            this_batch.append((ref_words, hyp_words))
+
+        results.extend(this_batch)
+
+        num_cuts += len(batch["supervisions"]["text"])
+
+        if batch_idx % 100 == 0:
+            batch_str = f"{batch_idx}/{num_batches}"
+
+            logging.info(
+                f"batch {batch_str}, cuts processed until now is {num_cuts}"
+            )
+    return results
+
+
+def save_results(
+    exp_dir: Path,
+    test_set_name: str,
+    results: List[Tuple[List[int], List[int]]],
+) -> None:
+    """Save results to `exp_dir`.
+    Args:
+      exp_dir:
+        The output directory. This function create the following files inside
+        this directory:
+
+            - recogs-{test_set_name}.text
+
+                It contains the reference and hypothesis results, like below::
+
+                    ref=['NO', 'NO', 'NO', 'YES', 'NO', 'NO', 'NO', 'YES']
+                    hyp=['NO', 'NO', 'NO', 'YES', 'NO', 'NO', 'NO', 'YES']
+                    ref=['NO', 'NO', 'YES', 'NO', 'YES', 'NO', 'NO', 'YES']
+                    hyp=['NO', 'NO', 'YES', 'NO', 'YES', 'NO', 'NO', 'YES']
+
+            - errs-{test_set_name}.txt
+
+                It contains the detailed WER.
+      test_set_name:
+        The name of the test set, which will be part of the result filename.
+      results:
+        A list of tuples, each of which contains (ref_words, hyp_words).
+    Returns:
+      Return None.
+    """
+    recog_path = exp_dir / f"recogs-{test_set_name}.txt"
+    store_transcripts(filename=recog_path, texts=results)
+    logging.info(f"The transcripts are stored in {recog_path}")
+
+    # The following prints out WERs, per-word error statistics and aligned
+    # ref/hyp pairs.
+    errs_filename = exp_dir / f"errs-{test_set_name}.txt"
+    with open(errs_filename, "w") as f:
+        write_error_stats(f, f"{test_set_name}", results)
+
+    logging.info("Wrote detailed error stats to {}".format(errs_filename))
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    YesNoAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+
+    params = get_params()
+    params.update(vars(args))
+
+    setup_logger(f"{params.exp_dir}/log/log-decode")
+    logging.info("Decoding started")
+    logging.info(params)
+
+    lexicon = Lexicon(params.lang_dir)
+    max_token_id = max(lexicon.tokens)
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"device: {device}")
+
+    HLG = k2.Fsa.from_dict(
+        torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu")
+    )
+    HLG = HLG.to(device)
+    assert HLG.requires_grad is False
+
+    model = Tdnn(
+        num_features=params.feature_dim,
+        num_classes=max_token_id + 1,  # +1 for the blank symbol
+    )
+    if params.avg == 1:
+        load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+    else:
+        start = params.epoch - params.avg + 1
+        filenames = []
+        for i in range(start, params.epoch + 1):
+            if start >= 0:
+                filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+        logging.info(f"averaging {filenames}")
+        model.load_state_dict(average_checkpoints(filenames))
+
+    if params.export:
+        logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt")
+        torch.save(
+            {"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt"
+        )
+
+    model.to(device)
+    model.eval()
+
+    yes_no = YesNoAsrDataModule(args)
+    test_dl = yes_no.test_dataloaders()
+    results = decode_dataset(
+        dl=test_dl,
+        params=params,
+        model=model,
+        HLG=HLG,
+        word_table=lexicon.word_table,
+    )
+
+    save_results(
+        exp_dir=params.exp_dir, test_set_name="test_set", results=results
+    )
+
+    logging.info("Done!")
+
+
+if __name__ == "__main__":
+    main()

egs/yesno/ASR/tdnn/model.py

@@ -0,0 +1,81 @@
+#!/usr/bin/env python3
+
+# Copyright (c)  2021  Xiaomi Corp.       (author: Fangjun Kuang)
+
+
+import torch
+import torch.nn as nn
+
+
+class Tdnn(nn.Module):
+    def __init__(self, num_features: int, num_classes: int):
+        """
+        Args:
+          num_features:
+            Model input dimension.
+          num_classes:
+            Model output dimension
+        """
+        super().__init__()
+
+        self.tdnn = nn.Sequential(
+            nn.Conv1d(
+                in_channels=num_features,
+                out_channels=32,
+                kernel_size=3,
+            ),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm1d(num_features=32, affine=False),
+            nn.Conv1d(
+                in_channels=32,
+                out_channels=32,
+                kernel_size=5,
+                dilation=2,
+            ),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm1d(num_features=32, affine=False),
+            nn.Conv1d(
+                in_channels=32,
+                out_channels=32,
+                kernel_size=5,
+                dilation=4,
+            ),
+            nn.ReLU(inplace=True),
+            nn.BatchNorm1d(num_features=32, affine=False),
+        )
+        self.output_linear = nn.Linear(in_features=32, out_features=num_classes)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+          x:
+            The input tensor with shape [N, T, C]
+
+        Returns:
+          The output tensor has shape [N, T, C]
+        """
+        x = x.permute(0, 2, 1)  # [N, T, C] -> [N, C, T]
+        x = self.tdnn(x)
+        x = x.permute(0, 2, 1)  # [N, C, T] -> [N, T, C]
+        x = self.output_linear(x)
+        x = nn.functional.log_softmax(x, dim=-1)
+        return x
+
+
+def test_tdnn():
+    num_features = 23
+    num_classes = 4
+    model = Tdnn(num_features=num_features, num_classes=num_classes)
+    num_param = sum([p.numel() for p in model.parameters()])
+    print(f"Number of model parameters: {num_param}")
+    N = 2
+    T = 100
+    C = num_features
+    x = torch.randn(N, T, C)
+    y = model(x)
+    print(x.shape)
+    print(y.shape)
+
+
+if __name__ == "__main__":
+    test_tdnn()

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

@@ -0,0 +1,584 @@
+#!/usr/bin/env python3
+
+import argparse
+import logging
+from pathlib import Path
+from shutil import copyfile
+from typing import Optional
+
+import k2
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+import torch.nn as nn
+import torch.optim as optim
+from asr_datamodule import YesNoAsrDataModule
+from lhotse.utils import fix_random_seed
+from model import Tdnn
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.nn.utils import clip_grad_norm_
+from torch.utils.tensorboard import SummaryWriter
+
+from icefall.checkpoint import load_checkpoint
+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 AttributeDict, setup_logger, str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--world-size",
+        type=int,
+        default=1,
+        help="Number of GPUs for DDP training.",
+    )
+
+    parser.add_argument(
+        "--master-port",
+        type=int,
+        default=12354,
+        help="Master port to use for DDP training.",
+    )
+
+    parser.add_argument(
+        "--tensorboard",
+        type=str2bool,
+        default=True,
+        help="Should various information be logged in tensorboard.",
+    )
+
+    parser.add_argument(
+        "--num-epochs",
+        type=int,
+        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
+
+
+def get_params() -> AttributeDict:
+    """Return a dict containing training parameters.
+
+    All training related parameters that are not passed from the commandline
+    is saved in the variable `params`.
+
+    Commandline options are merged into `params` after they are parsed, so
+    you can also access them via `params`.
+
+    Explanation of options saved in `params`:
+
+        - exp_dir: It specifies the directory where all training related
+                   files, e.g., checkpoints, log, etc, are saved
+
+        - lang_dir: It contains language related input files such as
+                    "lexicon.txt"
+
+        - lr: It specifies the initial learning rate
+
+        - feature_dim: The model input dim. It has to match the one used
+                       in computing features.
+
+        - weight_decay:  The weight_decay for the optimizer.
+
+        - 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.
+
+        - 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.
+
+        - best_valid_loss: Best validation loss so far. It is used to select
+                           the model that has the lowest validation loss. It is
+                           updated during the training.
+
+        - best_train_epoch: It is the epoch that has the best training loss.
+
+        - best_valid_epoch: It is the epoch that has the best validation loss.
+
+        - batch_idx_train: Used to writing statistics to tensorboard. It
+                           contains number of batches trained so far across
+                           epochs.
+
+        - log_interval:  Print training loss if batch_idx % log_interval` is 0
+
+        - valid_interval:  Run validation if batch_idx % valid_interval` is 0
+
+        - beam_size: It is used in k2.ctc_loss
+
+        - reduction: It is used in k2.ctc_loss
+
+        - use_double_scores: It is used in k2.ctc_loss
+    """
+    params = AttributeDict(
+        {
+            "exp_dir": Path("tdnn/exp"),
+            "lang_dir": Path("data/lang_phone"),
+            "lr": 1e-2,
+            "feature_dim": 23,
+            "weight_decay": 1e-6,
+            "start_epoch": 0,
+            "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,
+            "valid_interval": 10,
+            "beam_size": 10,
+            "reduction": "sum",
+            "use_double_scores": True,
+        }
+    )
+
+    return params
+
+
+def load_checkpoint_if_available(
+    params: AttributeDict,
+    model: nn.Module,
+    optimizer: Optional[torch.optim.Optimizer] = None,
+    scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
+) -> None:
+    """Load checkpoint from file.
+
+    If params.start_epoch is positive, it will load the checkpoint from
+    `params.start_epoch - 1`. Otherwise, this function does nothing.
+
+    Apart from loading state dict for `model`, `optimizer` and `scheduler`,
+    it also updates `best_train_epoch`, `best_train_loss`, `best_valid_epoch`,
+    and `best_valid_loss` in `params`.
+
+    Args:
+      params:
+        The return value of :func:`get_params`.
+      model:
+        The training model.
+      optimizer:
+        The optimizer that we are using.
+      scheduler:
+        The learning rate scheduler we are using.
+    Returns:
+      Return None.
+    """
+    if params.start_epoch <= 0:
+        return
+
+    filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
+    saved_params = load_checkpoint(
+        filename,
+        model=model,
+        optimizer=optimizer,
+        scheduler=scheduler,
+    )
+
+    keys = [
+        "best_train_epoch",
+        "best_valid_epoch",
+        "batch_idx_train",
+        "best_train_loss",
+        "best_valid_loss",
+    ]
+    for k in keys:
+        params[k] = saved_params[k]
+
+    return saved_params
+
+
+def save_checkpoint(
+    params: AttributeDict,
+    model: nn.Module,
+    optimizer: torch.optim.Optimizer,
+    scheduler: torch.optim.lr_scheduler._LRScheduler,
+    rank: int = 0,
+) -> None:
+    """Save model, optimizer, scheduler and training stats to file.
+
+    Args:
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The training model.
+    """
+    if rank != 0:
+        return
+    filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
+    save_checkpoint_impl(
+        filename=filename,
+        model=model,
+        params=params,
+        optimizer=optimizer,
+        scheduler=scheduler,
+        rank=rank,
+    )
+
+    if params.best_train_epoch == params.cur_epoch:
+        best_train_filename = params.exp_dir / "best-train-loss.pt"
+        copyfile(src=filename, dst=best_train_filename)
+
+    if params.best_valid_epoch == params.cur_epoch:
+        best_valid_filename = params.exp_dir / "best-valid-loss.pt"
+        copyfile(src=filename, dst=best_valid_filename)
+
+
+def compute_loss(
+    params: AttributeDict,
+    model: nn.Module,
+    batch: dict,
+    graph_compiler: CtcTrainingGraphCompiler,
+    is_training: bool,
+):
+    """
+    Compute CTC loss given the model and its inputs.
+
+    Args:
+      params:
+        Parameters for training. See :func:`get_params`.
+      model:
+        The model for training. It is an instance of Tdnn in our case.
+      batch:
+        A batch of data. See `lhotse.dataset.K2SpeechRecognitionDataset()`
+        for the content in it.
+      graph_compiler:
+        It is used to build a decoding graph from a ctc topo and training
+        transcript. The training transcript is contained in the given `batch`,
+        while the ctc topo is built when this compiler is instantiated.
+      is_training:
+        True for training. False for validation. When it is True, this
+        function enables autograd during computation; when it is False, it
+        disables autograd.
+    """
+    device = graph_compiler.device
+    feature = batch["inputs"]
+    # at entry, feature is [N, T, C]
+    assert feature.ndim == 3
+    feature = feature.to(device)
+
+    with torch.set_grad_enabled(is_training):
+        nnet_output = model(feature)
+        # nnet_output is [N, T, C]
+
+    # NOTE: We need `encode_supervisions` to sort sequences with
+    # different duration in decreasing order, required by
+    # `k2.intersect_dense` called in `k2.ctc_loss`
+    supervisions = batch["supervisions"]
+    texts = supervisions["text"]
+
+    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(
+        nnet_output,
+        supervision_segments,
+    )
+
+    loss = k2.ctc_loss(
+        decoding_graph=decoding_graph,
+        dense_fsa_vec=dense_fsa_vec,
+        output_beam=params.beam_size,
+        reduction=params.reduction,
+        use_double_scores=params.use_double_scores,
+    )
+
+    assert loss.requires_grad == is_training
+
+    # train_frames and valid_frames are used for printing.
+    if is_training:
+        params.train_frames = supervision_segments[:, 2].sum().item()
+    else:
+        params.valid_frames = supervision_segments[:, 2].sum().item()
+
+    return loss
+
+
+def compute_validation_loss(
+    params: AttributeDict,
+    model: nn.Module,
+    graph_compiler: CtcTrainingGraphCompiler,
+    valid_dl: torch.utils.data.DataLoader,
+    world_size: int = 1,
+) -> None:
+    """Run the validation process. The validation loss
+    is saved in `params.valid_loss`.
+    """
+    model.eval()
+
+    tot_loss = 0.0
+    tot_frames = 0.0
+    for batch_idx, batch in enumerate(valid_dl):
+        loss = compute_loss(
+            params=params,
+            model=model,
+            batch=batch,
+            graph_compiler=graph_compiler,
+            is_training=False,
+        )
+        assert loss.requires_grad is False
+
+        loss_cpu = loss.detach().cpu().item()
+        tot_loss += loss_cpu
+        tot_frames += params.valid_frames
+
+    if world_size > 1:
+        s = torch.tensor([tot_loss, tot_frames], device=loss.device)
+        dist.all_reduce(s, op=dist.ReduceOp.SUM)
+        s = s.cpu().tolist()
+        tot_loss = s[0]
+        tot_frames = s[1]
+
+    params.valid_loss = tot_loss / tot_frames
+
+    if params.valid_loss < params.best_valid_loss:
+        params.best_valid_epoch = params.cur_epoch
+        params.best_valid_loss = params.valid_loss
+
+
+def train_one_epoch(
+    params: AttributeDict,
+    model: nn.Module,
+    optimizer: torch.optim.Optimizer,
+    graph_compiler: CtcTrainingGraphCompiler,
+    train_dl: torch.utils.data.DataLoader,
+    valid_dl: torch.utils.data.DataLoader,
+    tb_writer: Optional[SummaryWriter] = None,
+    world_size: int = 1,
+) -> None:
+    """Train the model for one epoch.
+
+    The training loss from the mean of all frames is saved in
+    `params.train_loss`. It runs the validation process every
+    `params.valid_interval` batches.
+
+    Args:
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The model for training.
+      optimizer:
+        The optimizer we are using.
+      graph_compiler:
+        It is used to convert transcripts to FSAs.
+      train_dl:
+        Dataloader for the training dataset.
+      valid_dl:
+        Dataloader for the validation dataset.
+      tb_writer:
+        Writer to write log messages to tensorboard.
+      world_size:
+        Number of nodes in DDP training. If it is 1, DDP is disabled.
+    """
+    model.train()
+
+    tot_loss = 0.0  # sum of losses over all batches
+    tot_frames = 0.0  # sum of frames over all batches
+    for batch_idx, batch in enumerate(train_dl):
+        params.batch_idx_train += 1
+        batch_size = len(batch["supervisions"]["text"])
+
+        loss = compute_loss(
+            params=params,
+            model=model,
+            batch=batch,
+            graph_compiler=graph_compiler,
+            is_training=True,
+        )
+
+        # NOTE: We use reduction==sum and loss is computed over utterances
+        # in the batch and there is no normalization to it so far.
+
+        optimizer.zero_grad()
+        loss.backward()
+        clip_grad_norm_(model.parameters(), 5.0, 2.0)
+        optimizer.step()
+
+        loss_cpu = loss.detach().cpu().item()
+
+        tot_frames += params.train_frames
+        tot_loss += loss_cpu
+        tot_avg_loss = tot_loss / tot_frames
+
+        if batch_idx % params.log_interval == 0:
+            logging.info(
+                f"Epoch {params.cur_epoch}, batch {batch_idx}, "
+                f"batch avg loss {loss_cpu/params.train_frames:.4f}, "
+                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.valid_interval == 0:
+            compute_validation_loss(
+                params=params,
+                model=model,
+                graph_compiler=graph_compiler,
+                valid_dl=valid_dl,
+                world_size=world_size,
+            )
+            model.train()
+            logging.info(
+                f"Epoch {params.cur_epoch}, valid loss {params.valid_loss:.4f},"
+                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
+
+    if params.train_loss < params.best_train_loss:
+        params.best_train_epoch = params.cur_epoch
+        params.best_train_loss = params.train_loss
+
+
+def run(rank, world_size, args):
+    """
+    Args:
+      rank:
+        It is a value between 0 and `world_size-1`, which is
+        passed automatically by `mp.spawn()` in :func:`main`.
+        The node with rank 0 is responsible for saving checkpoint.
+      world_size:
+        Number of GPUs for DDP training.
+      args:
+        The return value of get_parser().parse_args()
+    """
+    params = get_params()
+    params.update(vars(args))
+
+    fix_random_seed(42)
+    if world_size > 1:
+        setup_dist(rank, world_size, params.master_port)
+
+    setup_logger(f"{params.exp_dir}/log/log-train")
+    logging.info("Training started")
+    logging.info(params)
+
+    if args.tensorboard and rank == 0:
+        tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
+    else:
+        tb_writer = None
+
+    lexicon = Lexicon(params.lang_dir)
+    max_phone_id = max(lexicon.tokens)
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", rank)
+
+    graph_compiler = CtcTrainingGraphCompiler(lexicon=lexicon, device=device)
+
+    model = Tdnn(
+        num_features=params.feature_dim,
+        num_classes=max_phone_id + 1,  # +1 for the blank symbol
+    )
+
+    checkpoints = load_checkpoint_if_available(params=params, model=model)
+
+    model.to(device)
+    if world_size > 1:
+        model = DDP(model, device_ids=[rank])
+
+    optimizer = optim.SGD(
+        model.parameters(),
+        lr=params.lr,
+        weight_decay=params.weight_decay,
+    )
+
+    if checkpoints:
+        optimizer.load_state_dict(checkpoints["optimizer"])
+
+    yes_no = YesNoAsrDataModule(args)
+    train_dl = yes_no.train_dataloaders()
+
+    # There are only 60 waves: 30 files are used for training
+    # and the remaining 30 files are used for testing.
+    # We use test data as validation.
+    valid_dl = yes_no.test_dataloaders()
+
+    for epoch in range(params.start_epoch, params.num_epochs):
+        train_dl.sampler.set_epoch(epoch)
+
+        if tb_writer is not None:
+            tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
+
+        params.cur_epoch = epoch
+
+        train_one_epoch(
+            params=params,
+            model=model,
+            optimizer=optimizer,
+            graph_compiler=graph_compiler,
+            train_dl=train_dl,
+            valid_dl=valid_dl,
+            tb_writer=tb_writer,
+            world_size=world_size,
+        )
+
+        save_checkpoint(
+            params=params,
+            model=model,
+            optimizer=optimizer,
+            scheduler=None,
+            rank=rank,
+        )
+
+    logging.info("Done!")
+    if world_size > 1:
+        torch.distributed.barrier()
+        cleanup_dist()
+
+
+def main():
+    parser = get_parser()
+    YesNoAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+
+    world_size = args.world_size
+    assert world_size >= 1
+    if world_size > 1:
+        mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True)
+    else:
+        run(rank=0, world_size=1, args=args)
+
+
+if __name__ == "__main__":
+    main()

icefall/bpe_graph_compiler.py

@@ -1,3 +1,20 @@
+# 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.
+
+
 from pathlib import Path
 from typing import List, Union
 
@@ -57,7 +74,9 @@ class BpeCtcTrainingGraphCompiler(object):
         return self.sp.encode(texts, out_type=int)
 
     def compile(
-        self, piece_ids: List[List[int]], modified: bool = False,
+        self,
+        piece_ids: List[List[int]],
+        modified: bool = False,
     ) -> k2.Fsa:
         """Build a ctc graph from a list-of-list piece IDs.
 

icefall/checkpoint.py

@@ -1,3 +1,20 @@
+# 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 logging
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Union

icefall/dataset/datamodule.py

@@ -1,3 +1,20 @@
+# Copyright      2021  Piotr Żelasko
+#
+# 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
 from typing import List, Union
 

icefall/decode.py

@@ -1,3 +1,19 @@
+# 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 logging
 from typing import Dict, List, Optional, Tuple, Union
 
@@ -6,8 +22,6 @@ import kaldialign
 import torch
 import torch.nn as nn
 
-from icefall.lexicon import Lexicon
-
 
 def _get_random_paths(
     lattice: k2.Fsa,
@@ -607,7 +621,7 @@ 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.
@@ -628,8 +642,8 @@ 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.
@@ -664,7 +678,7 @@ 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

icefall/dist.py

@@ -1,3 +1,20 @@
+# 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 os
 
 import torch

icefall/graph_compiler.py

@@ -1,3 +1,20 @@
+# 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.
+
+
 from typing import List
 
 import k2
@@ -8,7 +25,10 @@ from icefall.lexicon import Lexicon
 
 class CtcTrainingGraphCompiler(object):
     def __init__(
-        self, lexicon: Lexicon, device: torch.device, oov: str = "<UNK>",
+        self,
+        lexicon: Lexicon,
+        device: torch.device,
+        oov: str = "<UNK>",
     ):
         """
         Args:

icefall/lexicon.py

@@ -1,3 +1,20 @@
+# 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 logging
 import re
 import sys

icefall/utils.py

@@ -1,3 +1,20 @@
+# 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 os

test/test_bpe_graph_compiler.py

@@ -1,6 +1,20 @@
 #!/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.
 
-# Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
 
 from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
 from icefall.lexicon import BpeLexicon
@@ -15,7 +29,7 @@ def test():
 
     compiler = BpeCtcTrainingGraphCompiler(lang_dir)
     ids = compiler.texts_to_ids(["HELLO", "WORLD ZZZ"])
-    fsa = compiler.compile(ids)
+    compiler.compile(ids)
 
     lexicon = BpeLexicon(lang_dir)
     ids0 = lexicon.words_to_piece_ids(["HELLO"])

test/test_checkpoint.py

@@ -1,4 +1,20 @@
 #!/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 pytest
 import torch

test/test_graph_compiler.py

@@ -1,6 +1,20 @@
 #!/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.
 
-# Copyright (c)  2021  Xiaomi Corporation (authors: Fangjun Kuang)
 
 import re
 

test/test_lexicon.py

@@ -1,4 +1,20 @@
 #!/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.
+
 
 from pathlib import Path
 

test/test_utils.py

@@ -1,4 +1,21 @@
 #!/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 k2
 import pytest
 import torch