## Installation
-`icefall` depends on [k2][k2] for FSA operations and [lhotse][lhotse] for
-data preparations. To use `icefall`, you have to install its dependencies first.
-The following subsections describe how to setup the environment.
-
-CAUTION: There are various ways to setup the environment. What we describe
-here is just one alternative.
-
-### Install k2
-
-Please refer to [k2's installation documentation][k2-install] to install k2.
-If you have any issues about installing k2, please open an issue at
-.
-
-### Install lhotse
-
-Please refer to [lhotse's installation documentation][lhotse-install] to install
-lhotse.
-
-### Install icefall
-
-`icefall` is a set of Python scripts. What you need to do is just to set
-the environment variable `PYTHONPATH`:
-
-```bash
-cd $HOME/open-source
-git clone https://github.com/k2-fsa/icefall
-cd icefall
-pip install -r requirements.txt
-export PYTHONPATH=$HOME/open-source/icefall:$PYTHONPATHON
-```
-
-To verify `icefall` was installed successfully, you can run:
-
-```bash
-python3 -c "import icefall; print(icefall.__file__)"
-```
-
-It should print the path to `icefall`.
+Please refer to
+for installation.
## Recipes
-At present, two recipes are provided:
+Please refer to
+for more information.
- - [LibriSpeech][LibriSpeech]
- - [yesno][yesno] [](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing)
+We provide two recipes at present:
-### Yesno
+ - [yesno][yesno]
+ - [LibriSpeech][librispeech]
-For the yesno recipe, training with 50 epochs takes less than 2 minutes using **CPU**.
+### yesno
-The WER is
+This is the simplest ASR recipe in `icefall` and can be run on CPU.
+Training takes less than 30 seconds and gives you the following WER:
```
[test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
```
+We do provide a Colab notebook for this recipe.
-## Use Pre-trained models
-
-See [egs/librispeech/ASR/conformer_ctc/README.md](egs/librispeech/ASR/conformer_ctc/README.md)
-for how to use pre-trained models.
-[](https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing)
+[](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing)
-[yesno]: egs/yesno/ASR/README.md
-[LibriSpeech]: egs/librispeech/ASR/README.md
-[k2-install]: https://k2.readthedocs.io/en/latest/installation/index.html#
-[k2]: https://github.com/k2-fsa/k2
-[lhotse]: https://github.com/lhotse-speech/lhotse
-[lhotse-install]: https://lhotse.readthedocs.io/en/latest/getting-started.html#installation
+### LibriSpeech
+
+We provide two models for this recipe: [conformer CTC model][LibriSpeech_conformer_ctc]
+and [TDNN LSTM CTC model][LibriSpeech_tdnn_lstm_ctc].
+
+#### 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: [](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: [](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
diff --git a/docs/.gitignore b/docs/.gitignore
new file mode 100644
index 000000000..567609b12
--- /dev/null
+++ b/docs/.gitignore
@@ -0,0 +1 @@
+build/
diff --git a/docs/Makefile b/docs/Makefile
new file mode 100644
index 000000000..d0c3cbf10
--- /dev/null
+++ b/docs/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS ?=
+SPHINXBUILD ?= sphinx-build
+SOURCEDIR = source
+BUILDDIR = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+ @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+ @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/make.bat b/docs/make.bat
new file mode 100644
index 000000000..6247f7e23
--- /dev/null
+++ b/docs/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+ set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+ echo.
+ echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+ echo.installed, then set the SPHINXBUILD environment variable to point
+ echo.to the full path of the 'sphinx-build' executable. Alternatively you
+ echo.may add the Sphinx directory to PATH.
+ echo.
+ echo.If you don't have Sphinx installed, grab it from
+ echo.http://sphinx-doc.org/
+ exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/docs/requirements.txt b/docs/requirements.txt
new file mode 100644
index 000000000..74640391e
--- /dev/null
+++ b/docs/requirements.txt
@@ -0,0 +1,2 @@
+sphinx_rtd_theme
+sphinx
diff --git a/docs/source/_static/logo.png b/docs/source/_static/logo.png
new file mode 100644
index 000000000..84d42568c
Binary files /dev/null and b/docs/source/_static/logo.png differ
diff --git a/docs/source/conf.py b/docs/source/conf.py
new file mode 100644
index 000000000..599df8b3e
--- /dev/null
+++ b/docs/source/conf.py
@@ -0,0 +1,76 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+# import os
+# import sys
+# sys.path.insert(0, os.path.abspath('.'))
+
+import sphinx_rtd_theme
+
+# -- Project information -----------------------------------------------------
+
+project = "icefall"
+copyright = "2021, icefall development team"
+author = "icefall development team"
+
+# The full version, including alpha/beta/rc tags
+release = "0.1"
+
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+ "sphinx_rtd_theme",
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ["_templates"]
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = []
+
+source_suffix = {
+ ".rst": "restructuredtext",
+}
+master_doc = "index"
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages. See the documentation for
+# a list of builtin themes.
+#
+html_theme = "sphinx_rtd_theme"
+html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+html_show_sourcelink = True
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ["_static", "installation/images"]
+
+pygments_style = "sphinx"
+
+numfig = True
+
+html_context = {
+ "display_github": True,
+ "github_user": "k2-fsa",
+ "github_repo": "icefall",
+ "github_version": "master",
+ "conf_py_path": "/icefall/docs/source/",
+}
diff --git a/docs/source/contributing/code-style.rst b/docs/source/contributing/code-style.rst
new file mode 100644
index 000000000..7d61a3ba1
--- /dev/null
+++ b/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 `_, to format the code
+ - `flake8 `_, to check the style and quality of the code
+ - `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
diff --git a/docs/source/contributing/doc.rst b/docs/source/contributing/doc.rst
new file mode 100644
index 000000000..893d8a15e
--- /dev/null
+++ b/docs/source/contributing/doc.rst
@@ -0,0 +1,45 @@
+Contributing to Documentation
+=============================
+
+We use `sphinx `_
+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 `_
+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 ``_, and you will see
+the following:
+
+ .. figure:: images/doc-contrib.png
+ :width: 600
+ :align: center
+
+ View generated documentation locally with ``python3 -m http.server``.
diff --git a/docs/source/contributing/how-to-create-a-recipe.rst b/docs/source/contributing/how-to-create-a-recipe.rst
new file mode 100644
index 000000000..a30fb9056
--- /dev/null
+++ b/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 `_.
+
+Please refer to ``_
+for how to create a recipe in ``lhotse``.
+
+.. HINT::
+
+ The ``yesno`` recipe in ``lhotse`` is a very good example.
+
+ Please refer to ``_,
+ 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 `_
+to write your own ``prepare.sh``.
+Otherwise, please refer to
+`egs/librispeech/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 `_
+ - `egs/librispeech/tdnn_lstm_ctc `_
+ - `egs/librispeech/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
+
+ - ``_
+
+ If your model is transformer/conformer based.
+
+ - ``_
+
+ If your model is TDNN/LSTM based, i.e., there is no attention decoder.
+
+ - ``_
+
+ 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.
diff --git a/docs/source/contributing/images/doc-contrib.png b/docs/source/contributing/images/doc-contrib.png
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index 000000000..00906ab83
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diff --git a/docs/source/contributing/images/pre-commit-check-success.png b/docs/source/contributing/images/pre-commit-check-success.png
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diff --git a/docs/source/contributing/index.rst b/docs/source/contributing/index.rst
new file mode 100644
index 000000000..21c747d33
--- /dev/null
+++ b/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
diff --git a/docs/source/index.rst b/docs/source/index.rst
new file mode 100644
index 000000000..b06047a89
--- /dev/null
+++ b/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 `_, containing
+speech recognition recipes using `k2 `_.
+
+.. toctree::
+ :maxdepth: 2
+ :caption: Contents:
+
+ installation/index
+ recipes/index
+ contributing/index
diff --git a/docs/source/installation/images/device-CPU_CUDA-orange.svg b/docs/source/installation/images/device-CPU_CUDA-orange.svg
new file mode 100644
index 000000000..a023a1283
--- /dev/null
+++ b/docs/source/installation/images/device-CPU_CUDA-orange.svg
@@ -0,0 +1 @@
+
diff --git a/docs/source/installation/images/k2-v1.9-blueviolet.svg b/docs/source/installation/images/k2-v1.9-blueviolet.svg
new file mode 100644
index 000000000..5a207b370
--- /dev/null
+++ b/docs/source/installation/images/k2-v1.9-blueviolet.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/docs/source/installation/images/os-Linux_macOS-ff69b4.svg b/docs/source/installation/images/os-Linux_macOS-ff69b4.svg
new file mode 100644
index 000000000..178813ed4
--- /dev/null
+++ b/docs/source/installation/images/os-Linux_macOS-ff69b4.svg
@@ -0,0 +1 @@
+
diff --git a/docs/source/installation/images/python-3.6_3.7_3.8_3.9-blue.svg b/docs/source/installation/images/python-3.6_3.7_3.8_3.9-blue.svg
new file mode 100644
index 000000000..befc1e19e
--- /dev/null
+++ b/docs/source/installation/images/python-3.6_3.7_3.8_3.9-blue.svg
@@ -0,0 +1 @@
+
diff --git a/docs/source/installation/images/torch-1.6.0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg b/docs/source/installation/images/torch-1.6.0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg
new file mode 100644
index 000000000..496e5a9ef
--- /dev/null
+++ b/docs/source/installation/images/torch-1.6.0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg
@@ -0,0 +1 @@
+
diff --git a/docs/source/installation/index.rst b/docs/source/installation/index.rst
new file mode 100644
index 000000000..f960033e8
--- /dev/null
+++ b/docs/source/installation/index.rst
@@ -0,0 +1,466 @@
+.. _install icefall:
+
+Installation
+============
+
+- |os|
+- |device|
+- |python_versions|
+- |torch_versions|
+- |k2_versions|
+
+.. |os| image:: ./images/os-Linux_macOS-ff69b4.svg
+ :alt: Supported operating systems
+
+.. |device| image:: ./images/device-CPU_CUDA-orange.svg
+ :alt: Supported devices
+
+.. |python_versions| image:: ./images/python-3.6_3.7_3.8_3.9-blue.svg
+ :alt: Supported python versions
+
+.. |torch_versions| image:: ./images/torch-1.6.0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg
+ :alt: Supported PyTorch versions
+
+.. |k2_versions| image:: ./images/k2-v1.9-blueviolet.svg
+ :alt: Supported k2 versions
+
+``icefall`` depends on `k2 `_ and
+`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 ``_
+to install ``k2``.
+
+.. CAUTION::
+
+ You need to install ``k2`` with a version at least **v1.9**.
+
+.. 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 ``_
+to install ``lhotse``.
+
+.. HINT::
+
+ Install ``lhotse`` also installs its dependency `torchaudio `_.
+
+.. CAUTION::
+
+ If you have installed ``torchaudio``, please consider uninstalling it before
+ installing ``lhotse``. Otherwise, it may update your already installed PyTorch.
+
+(3) Download icefall
+--------------------
+
+``icefall`` is a collection of Python scripts, so you don't need to install it
+and we don't provide a ``setup.py`` to install it.
+
+What you need is to download it and set the environment variable ``PYTHONPATH``
+to point to it.
+
+Assume you want to place ``icefall`` in the folder ``/tmp``. The
+following commands show you how to setup ``icefall``:
+
+
+.. code-block:: bash
+
+ cd /tmp
+ git clone https://github.com/k2-fsa/icefall
+ cd icefall
+ pip install -r requirements.txt
+ export PYTHONPATH=/tmp/icefall:$PYTHONPATH
+
+.. HINT::
+
+ You can put several versions of ``icefall`` in the same virtual environment.
+ To switch among different versions of ``icefall``, just set ``PYTHONPATH``
+ to point to the version you want.
+
+
+Installation example
+--------------------
+
+The following shows an example about setting up the environment.
+
+
+(1) Create a virtual environment
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+ $ virtualenv -p python3.8 test-icefall
+
+ created virtual environment CPython3.8.6.final.0-64 in 1540ms
+ creator CPython3Posix(dest=/ceph-fj/fangjun/test-icefall, clear=False, no_vcs_ignore=False, global=False)
+ seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/root/fangjun/.local/share/v
+ irtualenv)
+ added seed packages: pip==21.1.3, setuptools==57.4.0, wheel==0.36.2
+ activators BashActivator,CShellActivator,FishActivator,PowerShellActivator,PythonActivator,XonshActivator
+
+
+(2) Activate your virtual environment
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+ $ source test-icefall/bin/activate
+
+(3) Install k2
+~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+ $ pip install k2==1.4.dev20210822+cpu.torch1.9.0 -f https://k2-fsa.org/nightly/index.html
+
+ Looking in links: https://k2-fsa.org/nightly/index.html
+ Collecting k2==1.4.dev20210822+cpu.torch1.9.0
+ Downloading https://k2-fsa.org/nightly/whl/k2-1.4.dev20210822%2Bcpu.torch1.9.0-cp38-cp38-linux_x86_64.whl (1.6 MB)
+ |________________________________| 1.6 MB 185 kB/s
+ Collecting graphviz
+ Downloading graphviz-0.17-py3-none-any.whl (18 kB)
+ Collecting torch==1.9.0
+ Using cached torch-1.9.0-cp38-cp38-manylinux1_x86_64.whl (831.4 MB)
+ Collecting typing-extensions
+ Using cached typing_extensions-3.10.0.0-py3-none-any.whl (26 kB)
+ Installing collected packages: typing-extensions, torch, graphviz, k2
+ Successfully installed graphviz-0.17 k2-1.4.dev20210822+cpu.torch1.9.0 torch-1.9.0 typing-extensions-3.10.0.0
+
+.. WARNING::
+
+ We choose to install a CPU version of k2 for testing. You would probably want to install
+ a CUDA version of k2.
+
+
+(4) Install lhotse
+~~~~~~~~~~~~~~~~~~
+
+.. code-block::
+
+ $ pip install git+https://github.com/lhotse-speech/lhotse
+
+ Collecting git+https://github.com/lhotse-speech/lhotse
+ Cloning https://github.com/lhotse-speech/lhotse to /tmp/pip-req-build-7b1b76ge
+ Running command git clone -q https://github.com/lhotse-speech/lhotse /tmp/pip-req-build-7b1b76ge
+ Collecting audioread>=2.1.9
+ Using cached audioread-2.1.9-py3-none-any.whl
+ Collecting SoundFile>=0.10
+ Using cached SoundFile-0.10.3.post1-py2.py3-none-any.whl (21 kB)
+ Collecting click>=7.1.1
+ Using cached click-8.0.1-py3-none-any.whl (97 kB)
+ Collecting cytoolz>=0.10.1
+ Using cached cytoolz-0.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.9 MB)
+ Collecting dataclasses
+ Using cached dataclasses-0.6-py3-none-any.whl (14 kB)
+ Collecting h5py>=2.10.0
+ Downloading h5py-3.4.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (4.5 MB)
+ |________________________________| 4.5 MB 684 kB/s
+ Collecting intervaltree>=3.1.0
+ Using cached intervaltree-3.1.0-py2.py3-none-any.whl
+ Collecting lilcom>=1.1.0
+ Using cached lilcom-1.1.1-cp38-cp38-linux_x86_64.whl
+ Collecting numpy>=1.18.1
+ Using cached numpy-1.21.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.8 MB)
+ Collecting packaging
+ Using cached packaging-21.0-py3-none-any.whl (40 kB)
+ Collecting pyyaml>=5.3.1
+ Using cached PyYAML-5.4.1-cp38-cp38-manylinux1_x86_64.whl (662 kB)
+ Collecting tqdm
+ Downloading tqdm-4.62.1-py2.py3-none-any.whl (76 kB)
+ |________________________________| 76 kB 2.7 MB/s
+ Collecting torchaudio==0.9.0
+ Downloading torchaudio-0.9.0-cp38-cp38-manylinux1_x86_64.whl (1.9 MB)
+ |________________________________| 1.9 MB 73.1 MB/s
+ Requirement already satisfied: torch==1.9.0 in ./test-icefall/lib/python3.8/site-packages (from torchaudio==0.9.0->lhotse===0.8.0.dev
+ -2a1410b-clean) (1.9.0)
+ Requirement already satisfied: typing-extensions in ./test-icefall/lib/python3.8/site-packages (from torch==1.9.0->torchaudio==0.9.0-
+ >lhotse===0.8.0.dev-2a1410b-clean) (3.10.0.0)
+ Collecting toolz>=0.8.0
+ Using cached toolz-0.11.1-py3-none-any.whl (55 kB)
+ Collecting sortedcontainers<3.0,>=2.0
+ Using cached sortedcontainers-2.4.0-py2.py3-none-any.whl (29 kB)
+ Collecting cffi>=1.0
+ Using cached cffi-1.14.6-cp38-cp38-manylinux1_x86_64.whl (411 kB)
+ Collecting pycparser
+ Using cached pycparser-2.20-py2.py3-none-any.whl (112 kB)
+ Collecting pyparsing>=2.0.2
+ Using cached pyparsing-2.4.7-py2.py3-none-any.whl (67 kB)
+ Building wheels for collected packages: lhotse
+ Building wheel for lhotse (setup.py) ... done
+ Created wheel for lhotse: filename=lhotse-0.8.0.dev_2a1410b_clean-py3-none-any.whl size=342242 sha256=f683444afa4dc0881133206b4646a
+ 9d0f774224cc84000f55d0a67f6e4a37997
+ Stored in directory: /tmp/pip-ephem-wheel-cache-ftu0qysz/wheels/7f/7a/8e/a0bf241336e2e3cb573e1e21e5600952d49f5162454f2e612f
+ WARNING: Built wheel for lhotse is invalid: Metadata 1.2 mandates PEP 440 version, but '0.8.0.dev-2a1410b-clean' is not
+ Failed to build lhotse
+ Installing collected packages: pycparser, toolz, sortedcontainers, pyparsing, numpy, cffi, tqdm, torchaudio, SoundFile, pyyaml, packa
+ ging, lilcom, intervaltree, h5py, dataclasses, cytoolz, click, audioread, lhotse
+ Running setup.py install for lhotse ... done
+ DEPRECATION: lhotse was installed using the legacy 'setup.py install' method, because a wheel could not be built for it. A possible
+ replacement is to fix the wheel build issue reported above. You can find discussion regarding this at https://github.com/pypa/pip/is
+ sues/8368.
+ Successfully installed SoundFile-0.10.3.post1 audioread-2.1.9 cffi-1.14.6 click-8.0.1 cytoolz-0.11.0 dataclasses-0.6 h5py-3.4.0 inter
+ valtree-3.1.0 lhotse-0.8.0.dev-2a1410b-clean lilcom-1.1.1 numpy-1.21.2 packaging-21.0 pycparser-2.20 pyparsing-2.4.7 pyyaml-5.4.1 sor
+ tedcontainers-2.4.0 toolz-0.11.1 torchaudio-0.9.0 tqdm-4.62.1
+
+(5) Download icefall
+~~~~~~~~~~~~~~~~~~~~
+
+.. code-block::
+
+ $ cd /tmp
+ $ git clone https://github.com/k2-fsa/icefall
+
+ Cloning into 'icefall'...
+ remote: Enumerating objects: 500, done.
+ remote: Counting objects: 100% (500/500), done.
+ remote: Compressing objects: 100% (308/308), done.
+ remote: Total 500 (delta 263), reused 307 (delta 102), pack-reused 0
+ Receiving objects: 100% (500/500), 172.49 KiB | 385.00 KiB/s, done.
+ Resolving deltas: 100% (263/263), done.
+
+ $ cd icefall
+ $ pip install -r requirements.txt
+
+ Collecting kaldilm
+ Downloading kaldilm-1.8.tar.gz (48 kB)
+ |________________________________| 48 kB 574 kB/s
+ Collecting kaldialign
+ Using cached kaldialign-0.2-cp38-cp38-linux_x86_64.whl
+ Collecting sentencepiece>=0.1.96
+ Using cached sentencepiece-0.1.96-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)
+ Collecting tensorboard
+ Using cached tensorboard-2.6.0-py3-none-any.whl (5.6 MB)
+ Requirement already satisfied: setuptools>=41.0.0 in /ceph-fj/fangjun/test-icefall/lib/python3.8/site-packages (from tensorboard->-r
+ requirements.txt (line 4)) (57.4.0)
+ Collecting absl-py>=0.4
+ Using cached absl_py-0.13.0-py3-none-any.whl (132 kB)
+ Collecting google-auth-oauthlib<0.5,>=0.4.1
+ Using cached google_auth_oauthlib-0.4.5-py2.py3-none-any.whl (18 kB)
+ Collecting grpcio>=1.24.3
+ Using cached grpcio-1.39.0-cp38-cp38-manylinux2014_x86_64.whl (4.3 MB)
+ Requirement already satisfied: wheel>=0.26 in /ceph-fj/fangjun/test-icefall/lib/python3.8/site-packages (from tensorboard->-r require
+ ments.txt (line 4)) (0.36.2)
+ Requirement already satisfied: numpy>=1.12.0 in /ceph-fj/fangjun/test-icefall/lib/python3.8/site-packages (from tensorboard->-r requi
+ rements.txt (line 4)) (1.21.2)
+ Collecting protobuf>=3.6.0
+ Using cached protobuf-3.17.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.0 MB)
+ Collecting werkzeug>=0.11.15
+ Using cached Werkzeug-2.0.1-py3-none-any.whl (288 kB)
+ Collecting tensorboard-data-server<0.7.0,>=0.6.0
+ Using cached tensorboard_data_server-0.6.1-py3-none-manylinux2010_x86_64.whl (4.9 MB)
+ Collecting google-auth<2,>=1.6.3
+ Downloading google_auth-1.35.0-py2.py3-none-any.whl (152 kB)
+ |________________________________| 152 kB 1.4 MB/s
+ Collecting requests<3,>=2.21.0
+ Using cached requests-2.26.0-py2.py3-none-any.whl (62 kB)
+ Collecting tensorboard-plugin-wit>=1.6.0
+ Using cached tensorboard_plugin_wit-1.8.0-py3-none-any.whl (781 kB)
+ Collecting markdown>=2.6.8
+ Using cached Markdown-3.3.4-py3-none-any.whl (97 kB)
+ Collecting six
+ Using cached six-1.16.0-py2.py3-none-any.whl (11 kB)
+ Collecting cachetools<5.0,>=2.0.0
+ Using cached cachetools-4.2.2-py3-none-any.whl (11 kB)
+ Collecting rsa<5,>=3.1.4
+ Using cached rsa-4.7.2-py3-none-any.whl (34 kB)
+ Collecting pyasn1-modules>=0.2.1
+ Using cached pyasn1_modules-0.2.8-py2.py3-none-any.whl (155 kB)
+ Collecting requests-oauthlib>=0.7.0
+ Using cached requests_oauthlib-1.3.0-py2.py3-none-any.whl (23 kB)
+ Collecting pyasn1<0.5.0,>=0.4.6
+ Using cached pyasn1-0.4.8-py2.py3-none-any.whl (77 kB)
+ Collecting urllib3<1.27,>=1.21.1
+ Using cached urllib3-1.26.6-py2.py3-none-any.whl (138 kB)
+ Collecting certifi>=2017.4.17
+ Using cached certifi-2021.5.30-py2.py3-none-any.whl (145 kB)
+ Collecting charset-normalizer~=2.0.0
+ Using cached charset_normalizer-2.0.4-py3-none-any.whl (36 kB)
+ Collecting idna<4,>=2.5
+ Using cached idna-3.2-py3-none-any.whl (59 kB)
+ Collecting oauthlib>=3.0.0
+ Using cached oauthlib-3.1.1-py2.py3-none-any.whl (146 kB)
+ Building wheels for collected packages: kaldilm
+ Building wheel for kaldilm (setup.py) ... done
+ Created wheel for kaldilm: filename=kaldilm-1.8-cp38-cp38-linux_x86_64.whl size=897233 sha256=eccb906cafcd45bf9a7e1a1718e4534254bfb
+ f4c0d0cbc66eee6c88d68a63862
+ Stored in directory: /root/fangjun/.cache/pip/wheels/85/7d/63/f2dd586369b8797cb36d213bf3a84a789eeb92db93d2e723c9
+ Successfully built kaldilm
+ Installing collected packages: urllib3, pyasn1, idna, charset-normalizer, certifi, six, rsa, requests, pyasn1-modules, oauthlib, cach
+ etools, requests-oauthlib, google-auth, werkzeug, tensorboard-plugin-wit, tensorboard-data-server, protobuf, markdown, grpcio, google
+ -auth-oauthlib, absl-py, tensorboard, sentencepiece, kaldilm, kaldialign
+ Successfully installed absl-py-0.13.0 cachetools-4.2.2 certifi-2021.5.30 charset-normalizer-2.0.4 google-auth-1.35.0 google-auth-oaut
+ hlib-0.4.5 grpcio-1.39.0 idna-3.2 kaldialign-0.2 kaldilm-1.8 markdown-3.3.4 oauthlib-3.1.1 protobuf-3.17.3 pyasn1-0.4.8 pyasn1-module
+ s-0.2.8 requests-2.26.0 requests-oauthlib-1.3.0 rsa-4.7.2 sentencepiece-0.1.96 six-1.16.0 tensorboard-2.6.0 tensorboard-data-server-0
+ .6.1 tensorboard-plugin-wit-1.8.0 urllib3-1.26.6 werkzeug-2.0.1
+
+
+Test Your Installation
+----------------------
+
+To test that your installation is successful, let us run
+the `yesno recipe `_
+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]
+ 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]
+ 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``!
diff --git a/docs/source/recipes/images/yesno-tdnn-tensorboard-log.png b/docs/source/recipes/images/yesno-tdnn-tensorboard-log.png
new file mode 100644
index 000000000..3d2612c9c
Binary files /dev/null and b/docs/source/recipes/images/yesno-tdnn-tensorboard-log.png differ
diff --git a/docs/source/recipes/index.rst b/docs/source/recipes/index.rst
new file mode 100644
index 000000000..36f8dfc39
--- /dev/null
+++ b/docs/source/recipes/index.rst
@@ -0,0 +1,17 @@
+Recipes
+=======
+
+This page contains various recipes in ``icefall``.
+Currently, only speech recognition recipes are provided.
+
+We may add recipes for other tasks as well in the future.
+
+.. we put the yesno recipe as the first recipe since it is the simplest one.
+.. Other recipes are listed in a alphabetical order.
+
+.. toctree::
+ :maxdepth: 2
+
+ yesno
+
+ librispeech
diff --git a/docs/source/recipes/librispeech.rst b/docs/source/recipes/librispeech.rst
new file mode 100644
index 000000000..946b23407
--- /dev/null
+++ b/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
diff --git a/docs/source/recipes/librispeech/conformer_ctc.rst b/docs/source/recipes/librispeech/conformer_ctc.rst
new file mode 100644
index 000000000..40100bc5a
--- /dev/null
+++ b/docs/source/recipes/librispeech/conformer_ctc.rst
@@ -0,0 +1,631 @@
+Confromer CTC
+=============
+
+This tutorial shows you how to run a conformer ctc model
+with the `LibriSpeech `_ 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/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
+
+.. HINT::
+
+ If you have pre-downloaded the `LibriSpeech `_
+ dataset and the `musan `_ 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., weight decay,
+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 `_
+
+You don't need to change these pre-configured parameters. If you really need to change
+them, please modify ``./conformer_ctc/train.py`` directly.
+
+
+Training logs
+~~~~~~~~~~~~~
+
+Training logs and checkpoints are saved in ``conformer_ctc/exp``.
+You will find the following files in that directory:
+
+ - ``epoch-0.pt``, ``epoch-1.pt``, ...
+
+ These are checkpoint files, containing model ``state_dict`` and optimizer ``state_dict``.
+ To resume training from some checkpoint, say ``epoch-10.pt``, you can use:
+
+ .. code-block:: bash
+
+ $ ./conformer_ctc/train.py --start-epoch 11
+
+ - ``tensorboard/``
+
+ This folder contains TensorBoard logs. Training loss, validation loss, learning
+ rate, etc, are recorded in these logs. You can visualize them by:
+
+ .. code-block:: bash
+
+ $ cd conformer_ctc/exp/tensorboard
+ $ tensorboard dev upload --logdir . --description "Conformer CTC training for LibriSpeech with icefall"
+
+ It will print something like below:
+
+ .. code-block::
+
+ TensorFlow installation not found - running with reduced feature set.
+ Upload started and will continue reading any new data as it's added to the logdir.
+
+ To stop uploading, press Ctrl-C.
+
+ New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/lzGnETjwRxC3yghNMd4kPw/
+
+ [2021-08-24T16:42:43] Started scanning logdir.
+ Uploading 4540 scalars...
+
+ Note there is a URL in the above output, click it and you will see
+ the following screenshot:
+
+ .. figure:: images/librispeech-conformer-ctc-tensorboard-log.png
+ :width: 600
+ :alt: TensorBoard screenshot
+ :align: center
+ :target: https://tensorboard.dev/experiment/lzGnETjwRxC3yghNMd4kPw/
+
+ TensorBoard screenshot.
+
+ - ``log/log-train-xxxx``
+
+ It is the detailed training log in text format, same as the one
+ you saw printed to the console during training.
+
+Usage examples
+~~~~~~~~~~~~~~
+
+The following shows typical use cases:
+
+**Case 1**
+^^^^^^^^^^
+
+.. code-block:: bash
+
+ $ cd egs/librispeech/ASR
+ $ ./conformer_ctc/train.py --max-duration 200 --full-libri 0
+
+It uses ``--max-duration`` of 200 to avoid OOM. Also, it uses only
+a subset of the LibriSpeech data for training.
+
+
+**Case 2**
+^^^^^^^^^^
+
+.. code-block:: bash
+
+ $ cd egs/librispeech/ASR
+ $ export CUDA_VISIBLE_DEVICES="0,3"
+ $ ./conformer_ctc/train.py --world-size 2
+
+It uses GPU 0 and GPU 3 for DDP training.
+
+**Case 3**
+^^^^^^^^^^
+
+.. code-block:: bash
+
+ $ cd egs/librispeech/ASR
+ $ ./conformer_ctc/train.py --num-epochs 10 --start-epoch 3
+
+It loads checkpoint ``./conformer_ctc/exp/epoch-2.pt`` and starts
+training from epoch 3. Also, it trains for 10 epochs.
+
+Decoding
+--------
+
+The decoding part uses checkpoints saved by the training part, so you have
+to run the training part first.
+
+.. code-block:: bash
+
+ $ cd egs/librispeech/ASR
+ $ ./conformer_ctc/decode.py --help
+
+shows the options for decoding.
+
+The commonly used options are:
+
+ - ``--method``
+
+ This specifies the decoding method.
+
+ The following command uses attention decoder for rescoring:
+
+ .. code-block::
+
+ $ cd egs/librispeech/ASR
+ $ ./conformer_ctc/decode.py --method attention-decoder --max-duration 30 --lattice-score-scale 0.5
+
+ - ``--lattice-score-scale``
+
+ It is used to scale down lattice scores so that there are more unique
+ paths for rescoring.
+
+ - ``--max-duration``
+
+ It has the same meaning as the one during training. A larger
+ value may cause OOM.
+
+Pre-trained Model
+-----------------
+
+We have uploaded a pre-trained model to
+``_.
+
+We describe how to use the pre-trained model to transcribe a sound file or
+multiple sound files in the following.
+
+Install kaldifeat
+~~~~~~~~~~~~~~~~~
+
+`kaldifeat `_ is used to
+extract features for a single sound file or multiple sound files
+at the same time.
+
+Please refer to ``_ 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.
+
+.. CAUTION::
+
+ In order to use this pre-trained model, your k2 version has to be v1.7 or later.
+
+After downloading, you will have the following files:
+
+.. code-block:: bash
+
+ $ cd egs/librispeech/ASR
+ $ tree tmp
+
+.. code-block:: bash
+
+ tmp
+ `-- icefall_asr_librispeech_conformer_ctc
+ |-- README.md
+ |-- data
+ | |-- lang_bpe
+ | | |-- HLG.pt
+ | | |-- bpe.model
+ | | |-- tokens.txt
+ | | `-- words.txt
+ | `-- lm
+ | `-- G_4_gram.pt
+ |-- exp
+ | `-- pretrained.pt
+ `-- test_wavs
+ |-- 1089-134686-0001.flac
+ |-- 1221-135766-0001.flac
+ |-- 1221-135766-0002.flac
+ `-- trans.txt
+
+ 6 directories, 11 files
+
+**File descriptions**:
+
+ - ``data/lang_bpe/HLG.pt``
+
+ It is the decoding graph.
+
+ - ``data/lang_bpe/bpe.model``
+
+ It is a sentencepiece model. You can use it to reproduce our results.
+
+ - ``data/lang_bpe/tokens.txt``
+
+ It contains tokens and their IDs, generated from ``bpe.model``.
+ Provided only for convenience so that you can look up the SOS/EOS ID easily.
+
+ - ``data/lang_bpe/words.txt``
+
+ It contains words and their IDs.
+
+ - ``data/lm/G_4_gram.pt``
+
+ It is a 4-gram LM, used for n-gram LM rescoring.
+
+ - ``exp/pretrained.pt``
+
+ It contains pre-trained model parameters, obtained by averaging
+ checkpoints from ``epoch-15.pt`` to ``epoch-34.pt``.
+ Note: We have removed optimizer ``state_dict`` to reduce file size.
+
+ - ``test_waves/*.flac``
+
+ It contains some test sound files from LibriSpeech ``test-clean`` dataset.
+
+ - ``test_waves/trans.txt``
+
+ It contains the reference transcripts for the sound files in ``test_waves/``.
+
+The information of the test sound files is listed below:
+
+.. code-block:: bash
+
+ $ soxi tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/*.flac
+
+ Input File : 'tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac'
+ Channels : 1
+ Sample Rate : 16000
+ Precision : 16-bit
+ Duration : 00:00:06.62 = 106000 samples ~ 496.875 CDDA sectors
+ File Size : 116k
+ Bit Rate : 140k
+ Sample Encoding: 16-bit FLAC
+
+ Input File : 'tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac'
+ Channels : 1
+ Sample Rate : 16000
+ Precision : 16-bit
+ Duration : 00:00:16.71 = 267440 samples ~ 1253.62 CDDA sectors
+ File Size : 343k
+ Bit Rate : 164k
+ Sample Encoding: 16-bit FLAC
+
+ Input File : 'tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac'
+ Channels : 1
+ Sample Rate : 16000
+ Precision : 16-bit
+ Duration : 00:00:04.83 = 77200 samples ~ 361.875 CDDA sectors
+ File Size : 105k
+ Bit Rate : 174k
+ Sample Encoding: 16-bit FLAC
+
+ Total Duration of 3 files: 00:00:28.16
+
+Usage
+~~~~~
+
+.. code-block::
+
+ $ cd egs/librispeech/ASR
+ $ ./conformer_ctc/pretrained.py --help
+
+displays the help information.
+
+It supports three decoding methods:
+
+ - HLG decoding
+ - HLG + n-gram LM rescoring
+ - HLG + n-gram LM rescoring + attention decoder rescoring
+
+HLG decoding
+^^^^^^^^^^^^
+
+HLG decoding uses the best path of the decoding lattice as the decoding result.
+
+The command to run HLG decoding is:
+
+.. code-block:: bash
+
+ $ cd egs/librispeech/ASR
+ $ ./conformer_ctc/pretrained.py \
+ --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretrained.pt \
+ --words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \
+ --HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac \
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac \
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac
+
+The output is given below:
+
+.. code-block::
+
+ 2021-08-20 11:03:05,712 INFO [pretrained.py:217] device: cuda:0
+ 2021-08-20 11:03:05,712 INFO [pretrained.py:219] Creating model
+ 2021-08-20 11:03:11,345 INFO [pretrained.py:238] Loading HLG from ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt
+ 2021-08-20 11:03:18,442 INFO [pretrained.py:255] Constructing Fbank computer
+ 2021-08-20 11:03:18,444 INFO [pretrained.py:265] Reading sound files: ['./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac']
+ 2021-08-20 11:03:18,507 INFO [pretrained.py:271] Decoding started
+ 2021-08-20 11:03:18,795 INFO [pretrained.py:300] Use HLG decoding
+ 2021-08-20 11:03:19,149 INFO [pretrained.py:339]
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac:
+ AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS
+
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac:
+ GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED
+ BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN
+
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac:
+ YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION
+
+ 2021-08-20 11:03:19,149 INFO [pretrained.py:341] Decoding Done
+
+HLG decoding + LM rescoring
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+It uses an n-gram LM to rescore the decoding lattice and the best
+path of the rescored lattice is the decoding result.
+
+The command to run HLG decoding + LM rescoring is:
+
+.. code-block:: bash
+
+ $ cd egs/librispeech/ASR
+ $ ./conformer_ctc/pretrained.py \
+ --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretrained.pt \
+ --words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \
+ --HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \
+ --method whole-lattice-rescoring \
+ --G ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt \
+ --ngram-lm-scale 0.8 \
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac \
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac \
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac
+
+Its output is:
+
+.. code-block::
+
+ 2021-08-20 11:12:17,565 INFO [pretrained.py:217] device: cuda:0
+ 2021-08-20 11:12:17,565 INFO [pretrained.py:219] Creating model
+ 2021-08-20 11:12:23,728 INFO [pretrained.py:238] Loading HLG from ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt
+ 2021-08-20 11:12:30,035 INFO [pretrained.py:246] Loading G from ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt
+ 2021-08-20 11:13:10,779 INFO [pretrained.py:255] Constructing Fbank computer
+ 2021-08-20 11:13:10,787 INFO [pretrained.py:265] Reading sound files: ['./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac']
+ 2021-08-20 11:13:10,798 INFO [pretrained.py:271] Decoding started
+ 2021-08-20 11:13:11,085 INFO [pretrained.py:305] Use HLG decoding + LM rescoring
+ 2021-08-20 11:13:11,736 INFO [pretrained.py:339]
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac:
+ AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS
+
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac:
+ GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED
+ BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN
+
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac:
+ YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION
+
+ 2021-08-20 11:13:11,737 INFO [pretrained.py:341] Decoding Done
+
+HLG decoding + LM rescoring + attention decoder rescoring
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+It uses an n-gram LM to rescore the decoding lattice, extracts
+n paths from the rescored lattice, recores the extracted paths with
+an attention decoder. The path with the highest score is the decoding result.
+
+The command to run HLG decoding + LM rescoring + attention decoder rescoring is:
+
+.. code-block:: bash
+
+ $ cd egs/librispeech/ASR
+ $ ./conformer_ctc/pretrained.py \
+ --checkpoint ./tmp/icefall_asr_librispeech_conformer_ctc/exp/pretrained.pt \
+ --words-file ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/words.txt \
+ --HLG ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt \
+ --method attention-decoder \
+ --G ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt \
+ --ngram-lm-scale 1.3 \
+ --attention-decoder-scale 1.2 \
+ --lattice-score-scale 0.5 \
+ --num-paths 100 \
+ --sos-id 1 \
+ --eos-id 1 \
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac \
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac \
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac
+
+The output is below:
+
+.. code-block::
+
+ 2021-08-20 11:19:11,397 INFO [pretrained.py:217] device: cuda:0
+ 2021-08-20 11:19:11,397 INFO [pretrained.py:219] Creating model
+ 2021-08-20 11:19:17,354 INFO [pretrained.py:238] Loading HLG from ./tmp/icefall_asr_librispeech_conformer_ctc/data/lang_bpe/HLG.pt
+ 2021-08-20 11:19:24,615 INFO [pretrained.py:246] Loading G from ./tmp/icefall_asr_librispeech_conformer_ctc/data/lm/G_4_gram.pt
+ 2021-08-20 11:20:04,576 INFO [pretrained.py:255] Constructing Fbank computer
+ 2021-08-20 11:20:04,584 INFO [pretrained.py:265] Reading sound files: ['./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac', './tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac']
+ 2021-08-20 11:20:04,595 INFO [pretrained.py:271] Decoding started
+ 2021-08-20 11:20:04,854 INFO [pretrained.py:313] Use HLG + LM rescoring + attention decoder rescoring
+ 2021-08-20 11:20:05,805 INFO [pretrained.py:339]
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1089-134686-0001.flac:
+ AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD LIGHT UP HERE AND THERE THE SQUALID QUARTER OF THE BROTHELS
+
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0001.flac:
+ GOD AS A DIRECT CONSEQUENCE OF THE SIN WHICH MAN THUS PUNISHED HAD GIVEN HER A LOVELY CHILD WHOSE PLACE WAS ON THAT SAME DISHONOURED
+ BOSOM TO CONNECT HER PARENT FOR EVER WITH THE RACE AND DESCENT OF MORTALS AND TO BE FINALLY A BLESSED SOUL IN HEAVEN
+
+ ./tmp/icefall_asr_librispeech_conformer_ctc/test_wavs/1221-135766-0002.flac:
+ YET THESE THOUGHTS AFFECTED HESTER PRYNNE LESS WITH HOPE THAN APPREHENSION
+
+ 2021-08-20 11:20:05,805 INFO [pretrained.py:341] Decoding Done
+
+Colab notebook
+--------------
+
+We do provide a colab notebook for this recipe showing how to use a pre-trained model.
+
+|librispeech asr conformer ctc colab notebook|
+
+.. |librispeech asr conformer ctc colab notebook| image:: https://colab.research.google.com/assets/colab-badge.svg
+ :target: https://colab.research.google.com/drive/1huyupXAcHsUrKaWfI83iMEJ6J0Nh0213?usp=sharing
+
+.. HINT::
+
+ Due to limited memory provided by Colab, you have to upgrade to Colab Pro to
+ run ``HLG decoding + LM rescoring`` and
+ ``HLG decoding + LM rescoring + attention decoder rescoring``.
+ Otherwise, you can only run ``HLG decoding`` with Colab.
+
+**Congratulations!** You have finished the librispeech ASR recipe with
+conformer CTC models in ``icefall``.
diff --git a/docs/source/recipes/librispeech/images/librispeech-conformer-ctc-tensorboard-log.png b/docs/source/recipes/librispeech/images/librispeech-conformer-ctc-tensorboard-log.png
new file mode 100644
index 000000000..4e8c2ea7c
Binary files /dev/null and b/docs/source/recipes/librispeech/images/librispeech-conformer-ctc-tensorboard-log.png differ
diff --git a/docs/source/recipes/librispeech/tdnn_lstm_ctc.rst b/docs/source/recipes/librispeech/tdnn_lstm_ctc.rst
new file mode 100644
index 000000000..848026802
--- /dev/null
+++ b/docs/source/recipes/librispeech/tdnn_lstm_ctc.rst
@@ -0,0 +1,394 @@
+TDNN-LSTM-CTC
+=============
+
+This tutorial shows you how to run a TDNN-LSTM-CTC model with the `LibriSpeech `_ 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 `_
+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 `_.
+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.
+
+
+.. _tdnn_lstm_ctc use a pre-trained model:
+
+Pre-trained Model
+-----------------
+
+We have uploaded the pre-trained model to
+``_.
+
+The following shows you how to use the pre-trained model.
+
+
+Install kaldifeat
+~~~~~~~~~~~~~~~~~
+
+`kaldifeat `_ is used to
+extract features for a single sound file or multiple sound files
+at the same time.
+
+Please refer to ``_ for installation.
+
+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.
+
+.. CAUTION::
+
+ In order to use this pre-trained model, your k2 version has to be v1.7 or later.
+
+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
+
+**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:
+
+.. code-block:: bash
+
+ $ 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
+
+
+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``.
diff --git a/docs/source/recipes/yesno.rst b/docs/source/recipes/yesno.rst
new file mode 100644
index 000000000..cb425ad1d
--- /dev/null
+++ b/docs/source/recipes/yesno.rst
@@ -0,0 +1,445 @@
+yesno
+=====
+
+This page shows you how to run the `yesno `_ recipe. It contains:
+
+ - (1) Prepare data for training
+ - (2) Train a TDNN model
+
+ - (a) View text format logs and visualize TensorBoard logs
+ - (b) Select device type, i.e., CPU and GPU, for training
+ - (c) Change training options
+ - (d) Resume training from a checkpoint
+
+ - (3) Decode with a trained model
+
+ - (a) Select a checkpoint for decoding
+ - (b) Model averaging
+
+ - (4) Colab notebook
+
+ - (a) It shows you step by step how to setup the environment, how to do training,
+ and how to do decoding
+ - (b) How to use a pre-trained model
+
+ - (5) Inference with a pre-trained model
+
+ - (a) Download a pre-trained model, provided by us
+ - (b) Decode a single sound file with a pre-trained model
+ - (c) Decode multiple sound files at the same time
+
+It does **NOT** show you:
+
+ - (1) How to train with multiple GPUs
+
+ The ``yesno`` dataset is so small that CPU is more than enough
+ for training as well as for decoding.
+
+ - (2) How to use LM rescoring for decoding
+
+ The dataset does not have an LM for rescoring.
+
+.. HINT::
+
+ We assume you have read the page :ref:`install icefall` and have setup
+ the environment for ``icefall``.
+
+.. HINT::
+
+ You **don't** need a **GPU** to run this recipe. It can be run on a **CPU**.
+ The training part takes less than 30 **seconds** on a CPU and you will get
+ the following WER at the end::
+
+ [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
+
+Data preparation
+----------------
+
+.. code-block:: bash
+
+ $ cd egs/yesno/ASR
+ $ ./prepare.sh
+
+The script ``./prepare.sh`` handles the data preparation for you, **automagically**.
+All you need to do is to run it.
+
+The data preparation contains several stages, you can use the following two
+options:
+
+ - ``--stage``
+ - ``--stop-stage``
+
+to control which stage(s) should be run. By default, all stages are executed.
+
+
+For example,
+
+.. code-block:: bash
+
+ $ cd egs/yesno/ASR
+ $ ./prepare.sh --stage 0 --stop-stage 0
+
+means to run only stage 0.
+
+To run stage 2 to stage 5, use:
+
+.. code-block:: bash
+
+ $ ./prepare.sh --stage 2 --stop-stage 5
+
+
+Training
+--------
+
+We provide only a TDNN model, contained in
+the `tdnn `_
+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 `_.
+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
+``_.
+
+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 `_.
+ So this file contains ``NO NO YES NO YES NO NO YES``.
+
+Download kaldifeat
+~~~~~~~~~~~~~~~~~~
+
+`kaldifeat `_ is used for extracting
+features from a single or multiple sound files. Please refer to
+``_ 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``.
diff --git a/egs/librispeech/ASR/README.md b/egs/librispeech/ASR/README.md
index 30778ed05..ae0c2684d 100644
--- a/egs/librispeech/ASR/README.md
+++ b/egs/librispeech/ASR/README.md
@@ -1,64 +1,3 @@
-## Data preparation
-
-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`.
+Please refer to
+for how to run models in this recipe.
diff --git a/egs/librispeech/ASR/RESULTS.md b/egs/librispeech/ASR/RESULTS.md
index 159147a3e..d04e912bf 100644
--- a/egs/librispeech/ASR/RESULTS.md
+++ b/egs/librispeech/ASR/RESULTS.md
@@ -6,7 +6,7 @@
TensorBoard log is available at https://tensorboard.dev/experiment/GnRzq8WWQW62dK4bklXBTg/#scalars
-Pretrained model is available at https://huggingface.co/pkufool/conformer_ctc
+Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_librispeech_conformer_ctc
The best decoding results (WER) are listed below, we got this results by averaging models from epoch 15 to 34, and using `attention-decoder` decoder with num_paths equals to 100.
@@ -21,3 +21,51 @@ To get more unique paths, we scaled the lattice.scores with 0.5 (see https://git
|test-clean|1.3|1.2|
|test-other|1.2|1.1|
+You can use the following commands to reproduce our results:
+
+```bash
+git clone https://github.com/k2-fsa/icefall
+cd icefall
+
+# It was using ef233486, you may not need to switch to it
+# git checkout ef233486
+
+cd egs/librispeech/ASR
+./prepare.sh
+
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+python conformer_ctc/train.py --bucketing-sampler True \
+ --concatenate-cuts False \
+ --max-duration 200 \
+ --full-libri True \
+ --world-size 4
+
+python conformer_ctc/decode.py --lattice-score-scale 0.5 \
+ --epoch 34 \
+ --avg 20 \
+ --method attention-decoder \
+ --max-duration 20 \
+ --num-paths 100
+```
+
+### LibriSpeech training results (Tdnn-Lstm)
+#### 2021-08-24
+
+(Wei Kang): Result of phone based Tdnn-Lstm model.
+
+Icefall version: https://github.com/k2-fsa/icefall/commit/caa0b9e9425af27e0c6211048acb55a76ed5d315
+
+Pretrained model is available at https://huggingface.co/pkufool/icefall_asr_librispeech_tdnn-lstm_ctc
+
+The best decoding results (WER) are listed below, we got this results by averaging models from epoch 19 to 14, and using `whole-lattice-rescoring` decoding method.
+
+||test-clean|test-other|
+|--|--|--|
+|WER| 6.59% | 17.69% |
+
+We searched the lm_score_scale for best results, the scales that produced the WER above are also listed below.
+
+||lm_scale|
+|--|--|
+|test-clean|0.8|
+|test-other|0.9|
diff --git a/egs/librispeech/ASR/conformer_ctc/README.md b/egs/librispeech/ASR/conformer_ctc/README.md
index 130d21351..23b51167b 100644
--- a/egs/librispeech/ASR/conformer_ctc/README.md
+++ b/egs/librispeech/ASR/conformer_ctc/README.md
@@ -1,351 +1,3 @@
-
-# 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
- 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
-
-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.
-[](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.
+Please visit
+
+for how to run this recipe.
diff --git a/egs/librispeech/ASR/conformer_ctc/conformer.py b/egs/librispeech/ASR/conformer_ctc/conformer.py
index 08287d686..b19b94db1 100644
--- a/egs/librispeech/ASR/conformer_ctc/conformer.py
+++ b/egs/librispeech/ASR/conformer_ctc/conformer.py
@@ -56,8 +56,6 @@ class Conformer(Transformer):
cnn_module_kernel: int = 31,
normalize_before: bool = True,
vgg_frontend: bool = False,
- is_espnet_structure: bool = False,
- mmi_loss: bool = True,
use_feat_batchnorm: bool = False,
) -> None:
super(Conformer, self).__init__(
@@ -72,7 +70,6 @@ class Conformer(Transformer):
dropout=dropout,
normalize_before=normalize_before,
vgg_frontend=vgg_frontend,
- mmi_loss=mmi_loss,
use_feat_batchnorm=use_feat_batchnorm,
)
@@ -85,12 +82,10 @@ class Conformer(Transformer):
dropout,
cnn_module_kernel,
normalize_before,
- is_espnet_structure,
)
self.encoder = ConformerEncoder(encoder_layer, num_encoder_layers)
self.normalize_before = normalize_before
- self.is_espnet_structure = is_espnet_structure
- if self.normalize_before and self.is_espnet_structure:
+ if self.normalize_before:
self.after_norm = nn.LayerNorm(d_model)
else:
# Note: TorchScript detects that self.after_norm could be used inside forward()
@@ -103,7 +98,7 @@ class Conformer(Transformer):
"""
Args:
x:
- The model input. Its shape is [N, T, C].
+ The model input. Its shape is (N, T, C).
supervisions:
Supervision in lhotse format.
See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py#L32 # noqa
@@ -125,7 +120,7 @@ class Conformer(Transformer):
mask = mask.to(x.device)
x = self.encoder(x, pos_emb, src_key_padding_mask=mask) # (T, B, F)
- if self.normalize_before and self.is_espnet_structure:
+ if self.normalize_before:
x = self.after_norm(x)
return x, mask
@@ -159,11 +154,10 @@ class ConformerEncoderLayer(nn.Module):
dropout: float = 0.1,
cnn_module_kernel: int = 31,
normalize_before: bool = True,
- is_espnet_structure: bool = False,
) -> None:
super(ConformerEncoderLayer, self).__init__()
self.self_attn = RelPositionMultiheadAttention(
- d_model, nhead, dropout=0.0, is_espnet_structure=is_espnet_structure
+ d_model, nhead, dropout=0.0
)
self.feed_forward = nn.Sequential(
@@ -436,7 +430,6 @@ class RelPositionMultiheadAttention(nn.Module):
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
- is_espnet_structure: bool = False,
) -> None:
super(RelPositionMultiheadAttention, self).__init__()
self.embed_dim = embed_dim
@@ -459,8 +452,6 @@ class RelPositionMultiheadAttention(nn.Module):
self._reset_parameters()
- self.is_espnet_structure = is_espnet_structure
-
def _reset_parameters(self) -> None:
nn.init.xavier_uniform_(self.in_proj.weight)
nn.init.constant_(self.in_proj.bias, 0.0)
@@ -690,9 +681,6 @@ class RelPositionMultiheadAttention(nn.Module):
_b = _b[_start:]
v = nn.functional.linear(value, _w, _b)
- if not self.is_espnet_structure:
- q = q * scaling
-
if attn_mask is not None:
assert (
attn_mask.dtype == torch.float32
@@ -785,14 +773,9 @@ class RelPositionMultiheadAttention(nn.Module):
) # (batch, head, time1, 2*time1-1)
matrix_bd = self.rel_shift(matrix_bd)
- if not self.is_espnet_structure:
- attn_output_weights = (
- matrix_ac + matrix_bd
- ) # (batch, head, time1, time2)
- else:
- attn_output_weights = (
- matrix_ac + matrix_bd
- ) * scaling # (batch, head, time1, time2)
+ attn_output_weights = (
+ matrix_ac + matrix_bd
+ ) * scaling # (batch, head, time1, time2)
attn_output_weights = attn_output_weights.view(
bsz * num_heads, tgt_len, -1
diff --git a/egs/librispeech/ASR/conformer_ctc/decode.py b/egs/librispeech/ASR/conformer_ctc/decode.py
index 6abcf3385..b5b41c82e 100755
--- a/egs/librispeech/ASR/conformer_ctc/decode.py
+++ b/egs/librispeech/ASR/conformer_ctc/decode.py
@@ -45,6 +45,7 @@ from icefall.utils import (
get_texts,
setup_logger,
store_transcripts,
+ str2bool,
write_error_stats,
)
@@ -57,28 +58,74 @@ def get_parser():
parser.add_argument(
"--epoch",
type=int,
- default=9,
+ default=34,
help="It specifies the checkpoint to use for decoding."
"Note: Epoch counts from 0.",
)
parser.add_argument(
"--avg",
type=int,
- default=1,
+ default=20,
help="Number of checkpoints to average. Automatically select "
"consecutive checkpoints before the checkpoint specified by "
"'--epoch'. ",
)
+ parser.add_argument(
+ "--method",
+ type=str,
+ default="attention-decoder",
+ help="""Decoding method.
+ Supported values are:
+ - (1) 1best. Extract the best path from the decoding lattice as the
+ decoding result.
+ - (2) nbest. Extract n paths from the decoding lattice; the path
+ with the highest score is the decoding result.
+ - (3) nbest-rescoring. Extract n paths from the decoding lattice,
+ rescore them with an n-gram LM (e.g., a 4-gram LM), the path with
+ the highest score is the decoding result.
+ - (4) whole-lattice-rescoring. Rescore the decoding lattice with an
+ n-gram LM (e.g., a 4-gram LM), the best path of rescored lattice
+ is the decoding result.
+ - (5) attention-decoder. Extract n paths from the LM rescored
+ lattice, the path with the highest score is the decoding result.
+ - (6) nbest-oracle. Its WER is the lower bound of any n-best
+ rescoring method can achieve. Useful for debugging n-best
+ rescoring method.
+ """,
+ )
+
+ parser.add_argument(
+ "--num-paths",
+ type=int,
+ default=100,
+ help="""Number of paths for n-best based decoding method.
+ Used only when "method" is one of the following values:
+ nbest, nbest-rescoring, attention-decoder, and nbest-oracle
+ """,
+ )
+
parser.add_argument(
"--lattice-score-scale",
type=float,
- default=1.0,
- help="The scale to be applied to `lattice.scores`."
- "It's needed if you use any kinds of n-best based rescoring. "
- "Currently, it is used when the decoding method is: nbest, "
- "nbest-rescoring, attention-decoder, and nbest-oracle. "
- "A smaller value results in more unique paths.",
+ default=0.5,
+ help="""The scale to be applied to `lattice.scores`.
+ It's needed if you use any kinds of n-best based rescoring.
+ Used only when "method" is one of the following values:
+ nbest, nbest-rescoring, attention-decoder, and nbest-oracle
+ A smaller value results in more unique paths.
+ """,
+ )
+
+ parser.add_argument(
+ "--export",
+ type=str2bool,
+ default=False,
+ help="""When enabled, the averaged model is saved to
+ conformer_ctc/exp/pretrained.pt. Note: only model.state_dict() is saved.
+ pretrained.pt contains a dict {"model": model.state_dict()},
+ which can be loaded by `icefall.checkpoint.load_checkpoint()`.
+ """,
)
return parser
@@ -90,35 +137,20 @@ def get_params() -> AttributeDict:
"exp_dir": Path("conformer_ctc/exp"),
"lang_dir": Path("data/lang_bpe"),
"lm_dir": Path("data/lm"),
+ # parameters for conformer
+ "subsampling_factor": 4,
+ "vgg_frontend": False,
+ "use_feat_batchnorm": True,
"feature_dim": 80,
"nhead": 8,
"attention_dim": 512,
- "subsampling_factor": 4,
"num_decoder_layers": 6,
- "vgg_frontend": False,
- "is_espnet_structure": True,
- "mmi_loss": False,
- "use_feat_batchnorm": True,
+ # parameters for decoding
"search_beam": 20,
"output_beam": 8,
"min_active_states": 30,
"max_active_states": 10000,
"use_double_scores": True,
- # Possible values for method:
- # - 1best
- # - nbest
- # - nbest-rescoring
- # - whole-lattice-rescoring
- # - attention-decoder
- # - nbest-oracle
- # "method": "nbest",
- # "method": "nbest-rescoring",
- # "method": "whole-lattice-rescoring",
- "method": "attention-decoder",
- # "method": "nbest-oracle",
- # num_paths is used when method is "nbest", "nbest-rescoring",
- # attention-decoder, and nbest-oracle
- "num_paths": 100,
}
)
return params
@@ -129,7 +161,7 @@ def decode_one_batch(
model: nn.Module,
HLG: k2.Fsa,
batch: dict,
- lexicon: Lexicon,
+ word_table: k2.SymbolTable,
sos_id: int,
eos_id: int,
G: Optional[k2.Fsa] = None,
@@ -163,8 +195,8 @@ def decode_one_batch(
It is the return value from iterating
`lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
for the format of the `batch`.
- lexicon:
- It contains word symbol table.
+ word_table:
+ The word symbol table.
sos_id:
The token ID of the SOS.
eos_id:
@@ -181,12 +213,12 @@ def decode_one_batch(
feature = batch["inputs"]
assert feature.ndim == 3
feature = feature.to(device)
- # at entry, feature is [N, T, C]
+ # at entry, feature is (N, T, C)
supervisions = batch["supervisions"]
nnet_output, memory, memory_key_padding_mask = model(feature, supervisions)
- # nnet_output is [N, T, C]
+ # nnet_output is (N, T, C)
supervision_segments = torch.stack(
(
@@ -212,14 +244,19 @@ def decode_one_batch(
# Note: You can also pass rescored lattices to it.
# We choose the HLG decoded lattice for speed reasons
# as HLG decoding is faster and the oracle WER
- # is slightly worse than that of rescored lattices.
- return nbest_oracle(
+ # is only slightly worse than that of rescored lattices.
+ best_path = nbest_oracle(
lattice=lattice,
num_paths=params.num_paths,
ref_texts=supervisions["text"],
- lexicon=lexicon,
- scale=params.lattice_score_scale,
+ word_table=word_table,
+ lattice_score_scale=params.lattice_score_scale,
+ oov="",
)
+ hyps = get_texts(best_path)
+ hyps = [[word_table[i] for i in ids] for ids in hyps]
+ key = f"oracle_{params.num_paths}_lattice_score_scale_{params.lattice_score_scale}" # noqa
+ return {key: hyps}
if params.method in ["1best", "nbest"]:
if params.method == "1best":
@@ -232,12 +269,12 @@ def decode_one_batch(
lattice=lattice,
num_paths=params.num_paths,
use_double_scores=params.use_double_scores,
- scale=params.lattice_score_scale,
+ lattice_score_scale=params.lattice_score_scale,
)
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 [
@@ -246,7 +283,8 @@ def decode_one_batch(
"attention-decoder",
]
- lm_scale_list = [0.8, 0.9, 1.0, 1.1, 1.2, 1.3]
+ lm_scale_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
+ lm_scale_list += [0.8, 0.9, 1.0, 1.1, 1.2, 1.3]
lm_scale_list += [1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0]
if params.method == "nbest-rescoring":
@@ -255,17 +293,23 @@ def decode_one_batch(
G=G,
num_paths=params.num_paths,
lm_scale_list=lm_scale_list,
- scale=params.lattice_score_scale,
+ lattice_score_scale=params.lattice_score_scale,
)
elif params.method == "whole-lattice-rescoring":
best_path_dict = rescore_with_whole_lattice(
- lattice=lattice, G_with_epsilon_loops=G, lm_scale_list=lm_scale_list
+ lattice=lattice,
+ G_with_epsilon_loops=G,
+ lm_scale_list=lm_scale_list,
)
elif params.method == "attention-decoder":
# lattice uses a 3-gram Lm. We rescore it with a 4-gram LM.
rescored_lattice = rescore_with_whole_lattice(
- lattice=lattice, G_with_epsilon_loops=G, lm_scale_list=None
+ lattice=lattice,
+ G_with_epsilon_loops=G,
+ lm_scale_list=None,
)
+ # TODO: pass `lattice` instead of `rescored_lattice` to
+ # `rescore_with_attention_decoder`
best_path_dict = rescore_with_attention_decoder(
lattice=rescored_lattice,
@@ -275,16 +319,20 @@ def decode_one_batch(
memory_key_padding_mask=memory_key_padding_mask,
sos_id=sos_id,
eos_id=eos_id,
- scale=params.lattice_score_scale,
+ lattice_score_scale=params.lattice_score_scale,
)
else:
assert False, f"Unsupported decoding method: {params.method}"
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]
- ans[lm_scale_str] = hyps
+ if best_path_dict is not None:
+ for lm_scale_str, best_path in best_path_dict.items():
+ hyps = get_texts(best_path)
+ hyps = [[word_table[i] for i in ids] for ids in hyps]
+ ans[lm_scale_str] = hyps
+ else:
+ for lm_scale in lm_scale_list:
+ ans[lm_scale_str] = [[] * lattice.shape[0]]
return ans
@@ -293,7 +341,7 @@ def decode_dataset(
params: AttributeDict,
model: nn.Module,
HLG: k2.Fsa,
- lexicon: Lexicon,
+ word_table: k2.SymbolTable,
sos_id: int,
eos_id: int,
G: Optional[k2.Fsa] = None,
@@ -309,8 +357,8 @@ def decode_dataset(
The neural model.
HLG:
The decoding graph.
- lexicon:
- It contains word symbol table.
+ word_table:
+ It is the word symbol table.
sos_id:
The token ID for SOS.
eos_id:
@@ -344,7 +392,7 @@ 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,
@@ -505,8 +553,6 @@ def main():
subsampling_factor=params.subsampling_factor,
num_decoder_layers=params.num_decoder_layers,
vgg_frontend=params.vgg_frontend,
- is_espnet_structure=params.is_espnet_structure,
- mmi_loss=params.mmi_loss,
use_feat_batchnorm=params.use_feat_batchnorm,
)
@@ -521,6 +567,13 @@ def main():
logging.info(f"averaging {filenames}")
model.load_state_dict(average_checkpoints(filenames))
+ if params.export:
+ logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt")
+ torch.save(
+ {"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt"
+ )
+ return
+
model.to(device)
model.eval()
num_param = sum([p.numel() for p in model.parameters()])
@@ -540,7 +593,7 @@ 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,
diff --git a/egs/librispeech/ASR/conformer_ctc/pretrained.py b/egs/librispeech/ASR/conformer_ctc/pretrained.py
index 95029fadb..c924b87bb 100755
--- a/egs/librispeech/ASR/conformer_ctc/pretrained.py
+++ b/egs/librispeech/ASR/conformer_ctc/pretrained.py
@@ -173,17 +173,17 @@ def get_parser():
def get_params() -> AttributeDict:
params = AttributeDict(
{
+ "sample_rate": 16000,
+ # parameters for conformer
+ "subsampling_factor": 4,
+ "vgg_frontend": False,
+ "use_feat_batchnorm": True,
"feature_dim": 80,
"nhead": 8,
"num_classes": 5000,
- "sample_rate": 16000,
"attention_dim": 512,
- "subsampling_factor": 4,
"num_decoder_layers": 6,
- "vgg_frontend": False,
- "is_espnet_structure": True,
- "mmi_loss": False,
- "use_feat_batchnorm": True,
+ # parameters for decoding
"search_beam": 20,
"output_beam": 8,
"min_active_states": 30,
@@ -241,8 +241,6 @@ def main():
subsampling_factor=params.subsampling_factor,
num_decoder_layers=params.num_decoder_layers,
vgg_frontend=params.vgg_frontend,
- is_espnet_structure=params.is_espnet_structure,
- mmi_loss=params.mmi_loss,
use_feat_batchnorm=params.use_feat_batchnorm,
)
@@ -338,7 +336,7 @@ def main():
memory_key_padding_mask=memory_key_padding_mask,
sos_id=params.sos_id,
eos_id=params.eos_id,
- scale=params.lattice_score_scale,
+ lattice_score_scale=params.lattice_score_scale,
ngram_lm_scale=params.ngram_lm_scale,
attention_scale=params.attention_decoder_scale,
)
diff --git a/egs/librispeech/ASR/conformer_ctc/subsampling.py b/egs/librispeech/ASR/conformer_ctc/subsampling.py
index 720ed6c22..542fb0364 100644
--- a/egs/librispeech/ASR/conformer_ctc/subsampling.py
+++ b/egs/librispeech/ASR/conformer_ctc/subsampling.py
@@ -22,8 +22,8 @@ import torch.nn as nn
class Conv2dSubsampling(nn.Module):
"""Convolutional 2D subsampling (to 1/4 length).
- Convert an input of shape [N, T, idim] to an output
- with shape [N, T', odim], where
+ Convert an input of shape (N, T, idim) to an output
+ with shape (N, T', odim), where
T' = ((T-1)//2 - 1)//2, which approximates T' == T//4
It is based on
@@ -34,10 +34,10 @@ class Conv2dSubsampling(nn.Module):
"""
Args:
idim:
- Input dim. The input shape is [N, T, idim].
+ Input dim. The input shape is (N, T, idim).
Caution: It requires: T >=7, idim >=7
odim:
- Output dim. The output shape is [N, ((T-1)//2 - 1)//2, odim]
+ Output dim. The output shape is (N, ((T-1)//2 - 1)//2, odim)
"""
assert idim >= 7
super().__init__()
@@ -58,18 +58,18 @@ class Conv2dSubsampling(nn.Module):
Args:
x:
- Its shape is [N, T, idim].
+ Its shape is (N, T, idim).
Returns:
- Return a tensor of shape [N, ((T-1)//2 - 1)//2, odim]
+ Return a tensor of shape (N, ((T-1)//2 - 1)//2, odim)
"""
- # On entry, x is [N, T, idim]
- x = x.unsqueeze(1) # [N, T, idim] -> [N, 1, T, idim] i.e., [N, C, H, W]
+ # On entry, x is (N, T, idim)
+ x = x.unsqueeze(1) # (N, T, idim) -> (N, 1, T, idim) i.e., (N, C, H, W)
x = self.conv(x)
- # Now x is of shape [N, odim, ((T-1)//2 - 1)//2, ((idim-1)//2 - 1)//2]
+ # Now x is of shape (N, odim, ((T-1)//2 - 1)//2, ((idim-1)//2 - 1)//2)
b, c, t, f = x.size()
x = self.out(x.transpose(1, 2).contiguous().view(b, t, c * f))
- # Now x is of shape [N, ((T-1)//2 - 1))//2, odim]
+ # Now x is of shape (N, ((T-1)//2 - 1))//2, odim)
return x
@@ -80,8 +80,8 @@ class VggSubsampling(nn.Module):
This paper is not 100% explicit so I am guessing to some extent,
and trying to compare with other VGG implementations.
- Convert an input of shape [N, T, idim] to an output
- with shape [N, T', odim], where
+ Convert an input of shape (N, T, idim) to an output
+ with shape (N, T', odim), where
T' = ((T-1)//2 - 1)//2, which approximates T' = T//4
"""
@@ -93,10 +93,10 @@ class VggSubsampling(nn.Module):
Args:
idim:
- Input dim. The input shape is [N, T, idim].
+ Input dim. The input shape is (N, T, idim).
Caution: It requires: T >=7, idim >=7
odim:
- Output dim. The output shape is [N, ((T-1)//2 - 1)//2, odim]
+ Output dim. The output shape is (N, ((T-1)//2 - 1)//2, odim)
"""
super().__init__()
@@ -149,10 +149,10 @@ class VggSubsampling(nn.Module):
Args:
x:
- Its shape is [N, T, idim].
+ Its shape is (N, T, idim).
Returns:
- Return a tensor of shape [N, ((T-1)//2 - 1)//2, odim]
+ Return a tensor of shape (N, ((T-1)//2 - 1)//2, odim)
"""
x = x.unsqueeze(1)
x = self.layers(x)
diff --git a/egs/librispeech/ASR/conformer_ctc/test_subsampling.py b/egs/librispeech/ASR/conformer_ctc/test_subsampling.py
index e3361d0c9..81fa234dd 100755
--- a/egs/librispeech/ASR/conformer_ctc/test_subsampling.py
+++ b/egs/librispeech/ASR/conformer_ctc/test_subsampling.py
@@ -16,9 +16,8 @@
# limitations under the License.
-from subsampling import Conv2dSubsampling
-from subsampling import VggSubsampling
import torch
+from subsampling import Conv2dSubsampling, VggSubsampling
def test_conv2d_subsampling():
diff --git a/egs/librispeech/ASR/conformer_ctc/test_transformer.py b/egs/librispeech/ASR/conformer_ctc/test_transformer.py
index b90215274..667057c51 100644
--- a/egs/librispeech/ASR/conformer_ctc/test_transformer.py
+++ b/egs/librispeech/ASR/conformer_ctc/test_transformer.py
@@ -17,17 +17,16 @@
import torch
+from torch.nn.utils.rnn import pad_sequence
from transformer import (
Transformer,
+ add_eos,
+ add_sos,
+ decoder_padding_mask,
encoder_padding_mask,
generate_square_subsequent_mask,
- decoder_padding_mask,
- add_sos,
- add_eos,
)
-from torch.nn.utils.rnn import pad_sequence
-
def test_encoder_padding_mask():
supervisions = {
diff --git a/egs/librispeech/ASR/conformer_ctc/train.py b/egs/librispeech/ASR/conformer_ctc/train.py
index df9637c34..80b2d924a 100755
--- a/egs/librispeech/ASR/conformer_ctc/train.py
+++ b/egs/librispeech/ASR/conformer_ctc/train.py
@@ -1,5 +1,6 @@
#!/usr/bin/env python3
-# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
+# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang,
+# Wei Kang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
@@ -74,6 +75,23 @@ 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
@@ -94,20 +112,6 @@ def get_params() -> AttributeDict:
- 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.
-
- - 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.
@@ -126,25 +130,40 @@ 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
- - reset_interval: Reset statistics if batch_idx % reset_interval is 0
+ - feature_dim: The model input dim. It has to match the one used
+ in computing features.
+
+ - subsampling_factor: The subsampling factor for the model.
+
+ - use_feat_batchnorm: Whether to do batch normalization for the
+ input features.
+
+ - attention_dim: Hidden dim for multi-head attention model.
+
+ - head: Number of heads of multi-head attention model.
+
+ - num_decoder_layers: Number of decoder layer of transformer decoder.
- 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
+
+ - weight_decay: The weight_decay for the optimizer.
+
+ - lr_factor: The lr_factor for Noam optimizer.
+
+ - warm_step: The warm_step for Noam optimizer.
"""
params = AttributeDict(
{
"exp_dir": Path("conformer_ctc/exp"),
"lang_dir": Path("data/lang_bpe"),
- "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,
@@ -153,17 +172,20 @@ def get_params() -> AttributeDict:
"log_interval": 10,
"reset_interval": 200,
"valid_interval": 3000,
- "beam_size": 10,
- "reduction": "sum",
- "use_double_scores": True,
- "accum_grad": 1,
- "att_rate": 0.7,
+ # parameters for conformer
+ "feature_dim": 80,
+ "subsampling_factor": 4,
+ "use_feat_batchnorm": True,
"attention_dim": 512,
"nhead": 8,
"num_decoder_layers": 6,
- "is_espnet_structure": True,
- "mmi_loss": False,
- "use_feat_batchnorm": True,
+ # parameters for loss
+ "beam_size": 10,
+ "reduction": "sum",
+ "use_double_scores": True,
+ "att_rate": 0.7,
+ # parameters for Noam
+ "weight_decay": 1e-6,
"lr_factor": 5.0,
"warm_step": 80000,
}
@@ -288,14 +310,14 @@ def compute_loss(
"""
device = graph_compiler.device
feature = batch["inputs"]
- # at entry, feature is [N, T, C]
+ # at entry, feature is (N, T, C)
assert feature.ndim == 3
feature = feature.to(device)
supervisions = batch["supervisions"]
with torch.set_grad_enabled(is_training):
nnet_output, encoder_memory, memory_mask = model(feature, supervisions)
- # nnet_output is [N, T, C]
+ # nnet_output is (N, T, C)
# NOTE: We need `encode_supervisions` to sort sequences with
# different duration in decreasing order, required by
@@ -636,8 +658,6 @@ def run(rank, world_size, args):
subsampling_factor=params.subsampling_factor,
num_decoder_layers=params.num_decoder_layers,
vgg_frontend=False,
- is_espnet_structure=params.is_espnet_structure,
- mmi_loss=params.mmi_loss,
use_feat_batchnorm=params.use_feat_batchnorm,
)
diff --git a/egs/librispeech/ASR/conformer_ctc/transformer.py b/egs/librispeech/ASR/conformer_ctc/transformer.py
index 191d2d612..68a4ff65c 100644
--- a/egs/librispeech/ASR/conformer_ctc/transformer.py
+++ b/egs/librispeech/ASR/conformer_ctc/transformer.py
@@ -41,7 +41,6 @@ class Transformer(nn.Module):
dropout: float = 0.1,
normalize_before: bool = True,
vgg_frontend: bool = False,
- mmi_loss: bool = True,
use_feat_batchnorm: bool = False,
) -> None:
"""
@@ -70,7 +69,6 @@ class Transformer(nn.Module):
If True, use pre-layer norm; False to use post-layer norm.
vgg_frontend:
True to use vgg style frontend for subsampling.
- mmi_loss:
use_feat_batchnorm:
True to use batchnorm for the input layer.
"""
@@ -85,8 +83,8 @@ class Transformer(nn.Module):
if subsampling_factor != 4:
raise NotImplementedError("Support only 'subsampling_factor=4'.")
- # self.encoder_embed converts the input of shape [N, T, num_classes]
- # to the shape [N, T//subsampling_factor, d_model].
+ # self.encoder_embed converts the input of shape (N, T, num_classes)
+ # to the shape (N, T//subsampling_factor, d_model).
# That is, it does two things simultaneously:
# (1) subsampling: T -> T//subsampling_factor
# (2) embedding: num_classes -> d_model
@@ -122,14 +120,9 @@ class Transformer(nn.Module):
)
if num_decoder_layers > 0:
- if mmi_loss:
- self.decoder_num_class = (
- self.num_classes + 1
- ) # +1 for the sos/eos symbol
- else:
- self.decoder_num_class = (
- self.num_classes
- ) # bpe model already has sos/eos symbol
+ self.decoder_num_class = (
+ self.num_classes
+ ) # bpe model already has sos/eos symbol
self.decoder_embed = nn.Embedding(
num_embeddings=self.decoder_num_class, embedding_dim=d_model
@@ -169,7 +162,7 @@ class Transformer(nn.Module):
"""
Args:
x:
- The input tensor. Its shape is [N, T, C].
+ The input tensor. Its shape is (N, T, C).
supervision:
Supervision in lhotse format.
See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py#L32 # noqa
@@ -178,17 +171,17 @@ class Transformer(nn.Module):
Returns:
Return a tuple containing 3 tensors:
- - CTC output for ctc decoding. Its shape is [N, T, C]
- - Encoder output with shape [T, N, C]. It can be used as key and
+ - CTC output for ctc decoding. Its shape is (N, T, C)
+ - Encoder output with shape (T, N, C). It can be used as key and
value for the decoder.
- Encoder output padding mask. It can be used as
- memory_key_padding_mask for the decoder. Its shape is [N, T].
+ memory_key_padding_mask for the decoder. Its shape is (N, T).
It is None if `supervision` is None.
"""
if self.use_feat_batchnorm:
- x = x.permute(0, 2, 1) # [N, T, C] -> [N, C, T]
+ x = x.permute(0, 2, 1) # (N, T, C) -> (N, C, T)
x = self.feat_batchnorm(x)
- x = x.permute(0, 2, 1) # [N, C, T] -> [N, T, C]
+ x = x.permute(0, 2, 1) # (N, C, T) -> (N, T, C)
encoder_memory, memory_key_padding_mask = self.run_encoder(
x, supervision
)
@@ -202,7 +195,7 @@ class Transformer(nn.Module):
Args:
x:
- The model input. Its shape is [N, T, C].
+ The model input. Its shape is (N, T, C).
supervisions:
Supervision in lhotse format.
See https://github.com/lhotse-speech/lhotse/blob/master/lhotse/dataset/speech_recognition.py#L32 # noqa
@@ -213,8 +206,8 @@ class Transformer(nn.Module):
padding mask for the decoder.
Returns:
Return a tuple with two tensors:
- - The encoder output, with shape [T, N, C]
- - encoder padding mask, with shape [N, T].
+ - The encoder output, with shape (T, N, C)
+ - encoder padding mask, with shape (N, T).
The mask is None if `supervisions` is None.
It is used as memory key padding mask in the decoder.
"""
@@ -232,11 +225,11 @@ class Transformer(nn.Module):
Args:
x:
The output tensor from the transformer encoder.
- Its shape is [T, N, C]
+ Its shape is (T, N, C)
Returns:
Return a tensor that can be used for CTC decoding.
- Its shape is [N, T, C]
+ Its shape is (N, T, C)
"""
x = self.encoder_output_layer(x)
x = x.permute(1, 0, 2) # (T, N, C) ->(N, T, C)
@@ -254,7 +247,7 @@ class Transformer(nn.Module):
"""
Args:
memory:
- It's the output of the encoder with shape [T, N, C]
+ It's the output of the encoder with shape (T, N, C)
memory_key_padding_mask:
The padding mask from the encoder.
token_ids:
@@ -319,7 +312,7 @@ class Transformer(nn.Module):
"""
Args:
memory:
- It's the output of the encoder with shape [T, N, C]
+ It's the output of the encoder with shape (T, N, C)
memory_key_padding_mask:
The padding mask from the encoder.
token_ids:
@@ -661,13 +654,13 @@ class PositionalEncoding(nn.Module):
def extend_pe(self, x: torch.Tensor) -> None:
"""Extend the time t in the positional encoding if required.
- The shape of `self.pe` is [1, T1, d_model]. The shape of the input x
- is [N, T, d_model]. If T > T1, then we change the shape of self.pe
- to [N, T, d_model]. Otherwise, nothing is done.
+ The shape of `self.pe` is (1, T1, d_model). The shape of the input x
+ is (N, T, d_model). If T > T1, then we change the shape of self.pe
+ to (N, T, d_model). Otherwise, nothing is done.
Args:
x:
- It is a tensor of shape [N, T, C].
+ It is a tensor of shape (N, T, C).
Returns:
Return None.
"""
@@ -685,7 +678,7 @@ class PositionalEncoding(nn.Module):
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0)
- # Now pe is of shape [1, T, d_model], where T is x.size(1)
+ # Now pe is of shape (1, T, d_model), where T is x.size(1)
self.pe = pe.to(device=x.device, dtype=x.dtype)
def forward(self, x: torch.Tensor) -> torch.Tensor:
@@ -694,10 +687,10 @@ class PositionalEncoding(nn.Module):
Args:
x:
- Its shape is [N, T, C]
+ Its shape is (N, T, C)
Returns:
- Return a tensor of shape [N, T, C]
+ Return a tensor of shape (N, T, C)
"""
self.extend_pe(x)
x = x * self.xscale + self.pe[:, : x.size(1), :]
diff --git a/egs/librispeech/ASR/local/compile_hlg.py b/egs/librispeech/ASR/local/compile_hlg.py
index 19a1ddd23..098d5d6a3 100755
--- a/egs/librispeech/ASR/local/compile_hlg.py
+++ b/egs/librispeech/ASR/local/compile_hlg.py
@@ -102,14 +102,14 @@ def compile_HLG(lang_dir: str) -> k2.Fsa:
LG.labels[LG.labels >= first_token_disambig_id] = 0
- assert isinstance(LG.aux_labels, k2.RaggedInt)
- LG.aux_labels.values()[LG.aux_labels.values() >= first_word_disambig_id] = 0
+ assert isinstance(LG.aux_labels, k2.RaggedTensor)
+ 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)
+ LG.aux_labels = LG.aux_labels.remove_values_eq(0)
logging.info("Arc sorting LG")
LG = k2.arc_sort(LG)
diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/README.md b/egs/librispeech/ASR/tdnn_lstm_ctc/README.md
index df98a0e11..94d4ed6a3 100644
--- a/egs/librispeech/ASR/tdnn_lstm_ctc/README.md
+++ b/egs/librispeech/ASR/tdnn_lstm_ctc/README.md
@@ -1,2 +1,4 @@
-Will add results later.
+Please visit
+
+for how to run this recipe.
diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py b/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py
index 91c1d6a96..8290e71d1 100644
--- a/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py
+++ b/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py
@@ -82,14 +82,14 @@ class LibriSpeechAsrDataModule(DataModule):
group.add_argument(
"--max-duration",
type=int,
- default=500.0,
+ default=200.0,
help="Maximum pooled recordings duration (seconds) in a "
"single batch. You can reduce it if it causes CUDA OOM.",
)
group.add_argument(
"--bucketing-sampler",
type=str2bool,
- default=False,
+ default=True,
help="When enabled, the batches will come from buckets of "
"similar duration (saves padding frames).",
)
diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py b/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py
index 27e0b9643..1e91b1008 100755
--- a/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py
+++ b/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py
@@ -42,6 +42,7 @@ from icefall.utils import (
get_texts,
setup_logger,
store_transcripts,
+ str2bool,
write_error_stats,
)
@@ -54,7 +55,7 @@ def get_parser():
parser.add_argument(
"--epoch",
type=int,
- default=9,
+ default=19,
help="It specifies the checkpoint to use for decoding."
"Note: Epoch counts from 0.",
)
@@ -66,6 +67,57 @@ def get_parser():
"consecutive checkpoints before the checkpoint specified by "
"'--epoch'. ",
)
+ parser.add_argument(
+ "--method",
+ type=str,
+ default="whole-lattice-rescoring",
+ 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.
+ """,
+ )
+
+ 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
+ """,
+ )
+
+ parser.add_argument(
+ "--lattice-score-scale",
+ type=float,
+ default=0.5,
+ help="""The scale to be applied to `lattice.scores`.
+ It's needed if you use any kinds of n-best based rescoring.
+ Used only when "method" is one of the following values:
+ nbest, nbest-rescoring
+ A smaller value results in more unique paths.
+ """,
+ )
+
+ parser.add_argument(
+ "--export",
+ type=str2bool,
+ default=False,
+ help="""When enabled, the averaged model is saved to
+ 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
@@ -82,14 +134,6 @@ 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
- "method": "1best",
- # num_paths is used when method is "nbest" and "nbest-rescoring"
- "num_paths": 30,
}
)
return params
@@ -146,12 +190,12 @@ def decode_one_batch(
feature = batch["inputs"]
assert feature.ndim == 3
feature = feature.to(device)
- # at entry, feature is [N, T, C]
+ # at entry, feature is (N, T, C)
- feature = feature.permute(0, 2, 1) # now feature is [N, C, T]
+ feature = feature.permute(0, 2, 1) # now feature is (N, C, T)
nnet_output = model(feature)
- # nnet_output is [N, T, C]
+ # nnet_output is (N, T, C)
supervisions = batch["supervisions"]
@@ -185,6 +229,7 @@ def decode_one_batch(
lattice=lattice,
num_paths=params.num_paths,
use_double_scores=params.use_double_scores,
+ lattice_score_scale=params.lattice_score_scale,
)
key = f"no_rescore-{params.num_paths}"
hyps = get_texts(best_path)
@@ -193,7 +238,8 @@ def decode_one_batch(
assert params.method in ["nbest-rescoring", "whole-lattice-rescoring"]
- lm_scale_list = [0.8, 0.9, 1.0, 1.1, 1.2, 1.3]
+ lm_scale_list = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]
+ lm_scale_list += [0.8, 0.9, 1.0, 1.1, 1.2, 1.3]
lm_scale_list += [1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0]
if params.method == "nbest-rescoring":
@@ -202,10 +248,13 @@ def decode_one_batch(
G=G,
num_paths=params.num_paths,
lm_scale_list=lm_scale_list,
+ lattice_score_scale=params.lattice_score_scale,
)
else:
best_path_dict = rescore_with_whole_lattice(
- lattice=lattice, G_with_epsilon_loops=G, lm_scale_list=lm_scale_list
+ lattice=lattice,
+ G_with_epsilon_loops=G,
+ lm_scale_list=lm_scale_list,
)
ans = dict()
@@ -408,6 +457,13 @@ def main():
logging.info(f"averaging {filenames}")
model.load_state_dict(average_checkpoints(filenames))
+ if params.export:
+ logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt")
+ torch.save(
+ {"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt"
+ )
+ return
+
model.to(device)
model.eval()
diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/pretrained.py b/egs/librispeech/ASR/tdnn_lstm_ctc/pretrained.py
new file mode 100755
index 000000000..0a543d859
--- /dev/null
+++ b/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()
diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/train.py b/egs/librispeech/ASR/tdnn_lstm_ctc/train.py
index 23e224f76..695ee5130 100755
--- a/egs/librispeech/ASR/tdnn_lstm_ctc/train.py
+++ b/egs/librispeech/ASR/tdnn_lstm_ctc/train.py
@@ -75,6 +75,23 @@ def get_parser():
help="Should various information be logged in tensorboard.",
)
+ parser.add_argument(
+ "--num-epochs",
+ type=int,
+ default=20,
+ help="Number of epochs to train.",
+ )
+
+ parser.add_argument(
+ "--start-epoch",
+ type=int,
+ default=0,
+ help="""Resume training from from this epoch.
+ If it is positive, it will load checkpoint from
+ tdnn_lstm_ctc/exp/epoch-{start_epoch-1}.pt
+ """,
+ )
+
return parser
@@ -104,11 +121,6 @@ def get_params() -> AttributeDict:
- subsampling_factor: The subsampling factor for the model.
- - start_epoch: If it is not zero, load checkpoint `start_epoch-1`
- and continue training from that checkpoint.
-
- - num_epochs: Number of epochs to train.
-
- best_train_loss: Best training loss so far. It is used to select
the model that has the lowest training loss. It is
updated during the training.
@@ -127,6 +139,8 @@ def get_params() -> AttributeDict:
- log_interval: Print training loss if batch_idx % log_interval` is 0
+ - reset_interval: Reset statistics if batch_idx % reset_interval is 0
+
- valid_interval: Run validation if batch_idx % valid_interval` is 0
- beam_size: It is used in k2.ctc_loss
@@ -143,14 +157,13 @@ def get_params() -> AttributeDict:
"feature_dim": 80,
"weight_decay": 5e-4,
"subsampling_factor": 3,
- "start_epoch": 0,
- "num_epochs": 10,
"best_train_loss": float("inf"),
"best_valid_loss": float("inf"),
"best_train_epoch": -1,
"best_valid_epoch": -1,
"batch_idx_train": 0,
"log_interval": 10,
+ "reset_interval": 200,
"valid_interval": 1000,
"beam_size": 10,
"reduction": "sum",
@@ -277,14 +290,14 @@ def compute_loss(
"""
device = graph_compiler.device
feature = batch["inputs"]
- # at entry, feature is [N, T, C]
- feature = feature.permute(0, 2, 1) # now feature is [N, C, T]
+ # at entry, feature is (N, T, C)
+ feature = feature.permute(0, 2, 1) # now feature is (N, C, T)
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]
+ # nnet_output is (N, T, C)
# NOTE: We need `encode_supervisions` to sort sequences with
# different duration in decreasing order, required by
@@ -398,8 +411,12 @@ def train_one_epoch(
"""
model.train()
- tot_loss = 0.0 # sum of losses over all batches
- tot_frames = 0.0 # sum of frames over all batches
+ tot_loss = 0.0 # reset after params.reset_interval of batches
+ tot_frames = 0.0 # reset after params.reset_interval of batches
+
+ params.tot_loss = 0.0
+ params.tot_frames = 0.0
+
for batch_idx, batch in enumerate(train_dl):
params.batch_idx_train += 1
batch_size = len(batch["supervisions"]["text"])
@@ -426,6 +443,9 @@ def train_one_epoch(
tot_loss += loss_cpu
tot_avg_loss = tot_loss / tot_frames
+ params.tot_frames += params.train_frames
+ params.tot_loss += loss_cpu
+
if batch_idx % params.log_interval == 0:
logging.info(
f"Epoch {params.cur_epoch}, batch {batch_idx}, "
@@ -433,6 +453,22 @@ def train_one_epoch(
f"total avg loss: {tot_avg_loss:.4f}, "
f"batch size: {batch_size}"
)
+ if tb_writer is not None:
+ tb_writer.add_scalar(
+ "train/current_loss",
+ loss_cpu / params.train_frames,
+ params.batch_idx_train,
+ )
+
+ tb_writer.add_scalar(
+ "train/tot_avg_loss",
+ tot_avg_loss,
+ params.batch_idx_train,
+ )
+
+ if batch_idx > 0 and batch_idx % params.reset_interval == 0:
+ tot_loss = 0
+ tot_frames = 0
if batch_idx > 0 and batch_idx % params.valid_interval == 0:
compute_validation_loss(
@@ -449,7 +485,7 @@ def train_one_epoch(
f"best valid epoch: {params.best_valid_epoch}"
)
- params.train_loss = tot_loss / tot_frames
+ params.train_loss = params.tot_loss / params.tot_frames
if params.train_loss < params.best_train_loss:
params.best_train_epoch = params.cur_epoch
diff --git a/egs/yesno/ASR/README.md b/egs/yesno/ASR/README.md
index 653c576fa..6f57412c0 100644
--- a/egs/yesno/ASR/README.md
+++ b/egs/yesno/ASR/README.md
@@ -1,15 +1,14 @@
## Yesno recipe
-You can run the recipe with **CPU**.
+This is the simplest ASR recipe in `icefall`.
-
-[](https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing)
-
-The above Colab notebook finishes the training using **CPU**
-within two minutes (50 epochs in total).
-
-The WER is
+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
+
+for detailed instructions.
diff --git a/egs/yesno/ASR/local/compile_hlg.py b/egs/yesno/ASR/local/compile_hlg.py
index f2fafd013..9b6a4c5ba 100755
--- a/egs/yesno/ASR/local/compile_hlg.py
+++ b/egs/yesno/ASR/local/compile_hlg.py
@@ -80,14 +80,14 @@ def compile_HLG(lang_dir: str) -> k2.Fsa:
LG.labels[LG.labels >= first_token_disambig_id] = 0
- assert isinstance(LG.aux_labels, k2.RaggedInt)
- LG.aux_labels.values()[LG.aux_labels.values() >= first_word_disambig_id] = 0
+ assert isinstance(LG.aux_labels, k2.RaggedTensor)
+ 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)
+ LG.aux_labels = LG.aux_labels.remove_values_eq(0)
logging.info("Arc sorting LG")
LG = k2.arc_sort(LG)
diff --git a/egs/yesno/ASR/tdnn/README.md b/egs/yesno/ASR/tdnn/README.md
new file mode 100644
index 000000000..2b6116f0a
--- /dev/null
+++ b/egs/yesno/ASR/tdnn/README.md
@@ -0,0 +1,8 @@
+
+## How to run this recipe
+
+You can find detailed instructions by visiting
+
+
+It describes how to run this recipe and how to use
+a pre-trained model with `./pretrained.py`.
diff --git a/egs/yesno/ASR/tdnn/decode.py b/egs/yesno/ASR/tdnn/decode.py
index b600c182c..325acf316 100755
--- a/egs/yesno/ASR/tdnn/decode.py
+++ b/egs/yesno/ASR/tdnn/decode.py
@@ -20,6 +20,7 @@ from icefall.utils import (
get_texts,
setup_logger,
store_transcripts,
+ str2bool,
write_error_stats,
)
@@ -44,6 +45,17 @@ def get_parser():
"consecutive checkpoints before the checkpoint specified by "
"'--epoch'. ",
)
+
+ parser.add_argument(
+ "--export",
+ type=str2bool,
+ default=False,
+ help="""When enabled, the averaged model is saved to
+ tdnn/exp/pretrained.pt. Note: only model.state_dict() is saved.
+ pretrained.pt contains a dict {"model": model.state_dict()},
+ which can be loaded by `icefall.checkpoint.load_checkpoint()`.
+ """,
+ )
return parser
@@ -99,10 +111,10 @@ def decode_one_batch(
feature = batch["inputs"]
assert feature.ndim == 3
feature = feature.to(device)
- # at entry, feature is [N, T, C]
+ # at entry, feature is (N, T, C)
nnet_output = model(feature)
- # nnet_output is [N, T, C]
+ # nnet_output is (N, T, C)
batch_size = nnet_output.shape[0]
supervision_segments = torch.tensor(
@@ -279,6 +291,13 @@ def main():
logging.info(f"averaging {filenames}")
model.load_state_dict(average_checkpoints(filenames))
+ if params.export:
+ logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt")
+ torch.save(
+ {"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt"
+ )
+ return
+
model.to(device)
model.eval()
diff --git a/egs/yesno/ASR/tdnn/pretrained.py b/egs/yesno/ASR/tdnn/pretrained.py
new file mode 100755
index 000000000..fb92110e3
--- /dev/null
+++ b/egs/yesno/ASR/tdnn/pretrained.py
@@ -0,0 +1,209 @@
+#!/usr/bin/env python3
+# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import logging
+import math
+from typing import List
+
+import k2
+import kaldifeat
+import torch
+import torchaudio
+from model import Tdnn
+from torch.nn.utils.rnn import pad_sequence
+
+from icefall.decode import get_lattice, one_best_decoding
+from icefall.utils import AttributeDict, get_texts
+
+
+def get_parser():
+ parser = argparse.ArgumentParser(
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter
+ )
+
+ parser.add_argument(
+ "--checkpoint",
+ type=str,
+ required=True,
+ help="Path to the checkpoint. "
+ "The checkpoint is assumed to be saved by "
+ "icefall.checkpoint.save_checkpoint().",
+ )
+
+ parser.add_argument(
+ "--words-file",
+ type=str,
+ required=True,
+ help="Path to words.txt",
+ )
+
+ parser.add_argument(
+ "--HLG", type=str, required=True, help="Path to HLG.pt."
+ )
+
+ parser.add_argument(
+ "sound_files",
+ type=str,
+ nargs="+",
+ help="The input sound file(s) to transcribe. "
+ "Supported formats are those supported by torchaudio.load(). "
+ "For example, wav and flac are supported. "
+ "The sample rate has to be 16kHz.",
+ )
+
+ return parser
+
+
+def get_params() -> AttributeDict:
+ params = AttributeDict(
+ {
+ "feature_dim": 23,
+ "num_classes": 4, # [, 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()
diff --git a/egs/yesno/ASR/tdnn/train.py b/egs/yesno/ASR/tdnn/train.py
index 04e1ab698..0f5506d38 100755
--- a/egs/yesno/ASR/tdnn/train.py
+++ b/egs/yesno/ASR/tdnn/train.py
@@ -60,6 +60,16 @@ def get_parser():
help="Number of epochs to train.",
)
+ parser.add_argument(
+ "--start-epoch",
+ type=int,
+ default=0,
+ help="""Resume training from from this epoch.
+ If it is positive, it will load checkpoint from
+ tdnn/exp/epoch-{start_epoch-1}.pt
+ """,
+ )
+
return parser
@@ -92,8 +102,6 @@ def get_params() -> AttributeDict:
- start_epoch: If it is not zero, load checkpoint `start_epoch-1`
and continue training from that checkpoint.
- - num_epochs: Number of epochs to train.
-
- best_train_loss: Best training loss so far. It is used to select
the model that has the lowest training loss. It is
updated during the training.
@@ -260,13 +268,13 @@ def compute_loss(
"""
device = graph_compiler.device
feature = batch["inputs"]
- # at entry, feature is [N, T, C]
+ # 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]
+ # nnet_output is (N, T, C)
# NOTE: We need `encode_supervisions` to sort sequences with
# different duration in decreasing order, required by
@@ -420,6 +428,19 @@ def train_one_epoch(
f"batch size: {batch_size}"
)
+ if tb_writer is not None:
+ tb_writer.add_scalar(
+ "train/current_loss",
+ loss_cpu / params.train_frames,
+ params.batch_idx_train,
+ )
+
+ tb_writer.add_scalar(
+ "train/tot_avg_loss",
+ tot_avg_loss,
+ params.batch_idx_train,
+ )
+
if batch_idx > 0 and batch_idx % params.valid_interval == 0:
compute_validation_loss(
params=params,
@@ -434,6 +455,12 @@ def train_one_epoch(
f" best valid loss: {params.best_valid_loss:.4f} "
f"best valid epoch: {params.best_valid_epoch}"
)
+ if tb_writer is not None:
+ tb_writer.add_scalar(
+ "train/valid_loss",
+ params.valid_loss,
+ params.batch_idx_train,
+ )
params.train_loss = tot_loss / tot_frames
diff --git a/icefall/decode.py b/icefall/decode.py
index bcc869e99..e678e4622 100644
--- a/icefall/decode.py
+++ b/icefall/decode.py
@@ -15,44 +15,12 @@
# limitations under the License.
import logging
-from typing import Dict, List, Optional, Tuple, Union
+from typing import Dict, List, Optional, Union
import k2
-import kaldialign
import torch
-import torch.nn as nn
-from icefall.lexicon import Lexicon
-
-
-def _get_random_paths(
- lattice: k2.Fsa,
- num_paths: int,
- use_double_scores: bool = True,
- scale: float = 1.0,
-):
- """
- Args:
- lattice:
- The decoding lattice, returned by :func:`get_lattice`.
- num_paths:
- It specifies the size `n` in n-best. Note: Paths are selected randomly
- and those containing identical word sequences are remove dand only one
- of them is kept.
- use_double_scores:
- True to use double precision floating point in the computation.
- False to use single precision.
- scale:
- It's the scale applied to the lattice.scores. A smaller value
- yields more unique paths.
- Returns:
- Return a k2.RaggedInt with 3 axes [seq][path][arc_pos]
- """
- saved_scores = lattice.scores.clone()
- lattice.scores *= scale
- path = k2.random_paths(lattice, num_paths=num_paths, use_double_scores=True)
- lattice.scores = saved_scores
- return path
+from icefall.utils import get_texts
def _intersect_device(
@@ -67,7 +35,7 @@ def _intersect_device(
CUDA OOM error.
The arguments and return value of this function are the same as
- k2.intersect_device.
+ :func:`k2.intersect_device`.
"""
num_fsas = b_fsas.shape[0]
if num_fsas <= batch_size:
@@ -86,8 +54,8 @@ def _intersect_device(
for start, end in splits:
indexes = torch.arange(start, end).to(b_to_a_map)
- fsas = k2.index(b_fsas, indexes)
- b_to_a = k2.index(b_to_a_map, indexes)
+ fsas = k2.index_fsa(b_fsas, indexes)
+ b_to_a = k2.index_select(b_to_a_map, indexes)
path_lattice = k2.intersect_device(
a_fsas, fsas, b_to_a_map=b_to_a, sorted_match_a=sorted_match_a
)
@@ -108,10 +76,9 @@ def get_lattice(
) -> k2.Fsa:
"""Get the decoding lattice from a decoding graph and neural
network output.
-
Args:
nnet_output:
- It is the output of a neural model of shape `[N, T, C]`.
+ It is the output of a neural model of shape `(N, T, C)`.
HLG:
An Fsa, the decoding graph. See also `compile_HLG.py`.
supervision_segments:
@@ -141,10 +108,12 @@ def get_lattice(
subsampling_factor:
The subsampling factor of the model.
Returns:
- A lattice containing the decoding result.
+ An FsaVec containing the decoding result. It has axes [utt][state][arc].
"""
dense_fsa_vec = k2.DenseFsaVec(
- nnet_output, supervision_segments, allow_truncate=subsampling_factor - 1
+ nnet_output,
+ supervision_segments,
+ allow_truncate=subsampling_factor - 1,
)
lattice = k2.intersect_dense_pruned(
@@ -159,8 +128,304 @@ def get_lattice(
return lattice
+class Nbest(object):
+ """
+ An Nbest object contains two fields:
+
+ (1) fsa. It is an FsaVec containing a vector of **linear** FSAs.
+ Its axes are [path][state][arc]
+ (2) shape. Its type is :class:`k2.RaggedShape`.
+ Its axes are [utt][path]
+
+ The field `shape` has two axes [utt][path]. `shape.dim0` contains
+ the number of utterances, which is also the number of rows in the
+ supervision_segments. `shape.tot_size(1)` contains the number
+ of paths, which is also the number of FSAs in `fsa`.
+
+ Caution:
+ Don't be confused by the name `Nbest`. The best in the name `Nbest`
+ has nothing to do with `best scores`. The important part is
+ `N` in `Nbest`, not `best`.
+ """
+
+ def __init__(self, fsa: k2.Fsa, shape: k2.RaggedShape) -> None:
+ """
+ Args:
+ fsa:
+ An FsaVec with axes [path][state][arc]. It is expected to contain
+ a list of **linear** FSAs.
+ shape:
+ A ragged shape with two axes [utt][path].
+ """
+ assert len(fsa.shape) == 3, f"fsa.shape: {fsa.shape}"
+ assert shape.num_axes == 2, f"num_axes: {shape.num_axes}"
+
+ if fsa.shape[0] != shape.tot_size(1):
+ raise ValueError(
+ f"{fsa.shape[0]} vs {shape.tot_size(1)}\n"
+ "Number of FSAs in `fsa` does not match the given shape"
+ )
+
+ self.fsa = fsa
+ self.shape = shape
+
+ def __str__(self):
+ s = "Nbest("
+ s += f"Number of utterances:{self.shape.dim0}, "
+ s += f"Number of Paths:{self.fsa.shape[0]})"
+ return s
+
+ @staticmethod
+ def from_lattice(
+ lattice: k2.Fsa,
+ num_paths: int,
+ use_double_scores: bool = True,
+ lattice_score_scale: float = 0.5,
+ ) -> "Nbest":
+ """Construct an Nbest object by **sampling** `num_paths` from a lattice.
+
+ Each sampled path is a linear FSA.
+
+ We assume `lattice.labels` contains token IDs and `lattice.aux_labels`
+ contains word IDs.
+
+ Args:
+ lattice:
+ An FsaVec with axes [utt][state][arc].
+ num_paths:
+ Number of paths to **sample** from the lattice
+ using :func:`k2.random_paths`.
+ use_double_scores:
+ True to use double precision in :func:`k2.random_paths`.
+ False to use single precision.
+ scale:
+ Scale `lattice.score` before passing it to :func:`k2.random_paths`.
+ A smaller value leads to more unique paths at the risk of being not
+ to sample the path with the best score.
+ Returns:
+ Return an Nbest instance.
+ """
+ saved_scores = lattice.scores.clone()
+ lattice.scores *= lattice_score_scale
+ # path is a ragged tensor with dtype torch.int32.
+ # It has three axes [utt][path][arc_pos]
+ path = k2.random_paths(
+ lattice, num_paths=num_paths, use_double_scores=use_double_scores
+ )
+ lattice.scores = saved_scores
+
+ # word_seq is a k2.RaggedTensor sharing the same shape as `path`
+ # but it contains word IDs. Note that it also contains 0s and -1s.
+ # The last entry in each sublist is -1.
+ # It axes is [utt][path][word_id]
+ if isinstance(lattice.aux_labels, torch.Tensor):
+ word_seq = k2.ragged.index(lattice.aux_labels, path)
+ else:
+ word_seq = lattice.aux_labels.index(path)
+ word_seq = word_seq.remove_axis(word_seq.num_axes - 2)
+
+ # Each utterance has `num_paths` paths but some of them transduces
+ # to the same word sequence, so we need to remove repeated word
+ # sequences within an utterance. After removing repeats, each utterance
+ # contains different number of paths
+ #
+ # `new2old` is a 1-D torch.Tensor mapping from the output path index
+ # to the input path index.
+ _, _, new2old = word_seq.unique(
+ need_num_repeats=False, need_new2old_indexes=True
+ )
+
+ # kept_path is a ragged tensor with dtype torch.int32.
+ # It has axes [utt][path][arc_pos]
+ kept_path, _ = path.index(new2old, axis=1, need_value_indexes=False)
+
+ # utt_to_path_shape has axes [utt][path]
+ utt_to_path_shape = kept_path.shape.get_layer(0)
+
+ # Remove the utterance axis.
+ # Now kept_path has only two axes [path][arc_pos]
+ kept_path = kept_path.remove_axis(0)
+
+ # labels is a ragged tensor with 2 axes [path][token_id]
+ # Note that it contains -1s.
+ labels = k2.ragged.index(lattice.labels.contiguous(), kept_path)
+
+ # Remove -1 from labels as we will use it to construct a linear FSA
+ labels = labels.remove_values_eq(-1)
+
+ if isinstance(lattice.aux_labels, k2.RaggedTensor):
+ # lattice.aux_labels is a ragged tensor with dtype torch.int32.
+ # It has 2 axes [arc][word], so aux_labels is also a ragged tensor
+ # with 2 axes [arc][word]
+ aux_labels, _ = lattice.aux_labels.index(
+ indexes=kept_path.values, axis=0, need_value_indexes=False
+ )
+ else:
+ assert isinstance(lattice.aux_labels, torch.Tensor)
+ aux_labels = k2.index_select(lattice.aux_labels, kept_path.values)
+ # aux_labels is a 1-D torch.Tensor. It also contains -1 and 0.
+
+ fsa = k2.linear_fsa(labels)
+ fsa.aux_labels = aux_labels
+ # Caution: fsa.scores are all 0s.
+ # `fsa` has only one extra attribute: aux_labels.
+ return Nbest(fsa=fsa, shape=utt_to_path_shape)
+
+ def intersect(self, lattice: k2.Fsa, use_double_scores=True) -> "Nbest":
+ """Intersect this Nbest object with a lattice, get 1-best
+ path from the resulting FsaVec, and return a new Nbest object.
+
+ The purpose of this function is to attach scores to an Nbest.
+
+ Args:
+ lattice:
+ An FsaVec with axes [utt][state][arc]. If it has `aux_labels`, then
+ we assume its `labels` are token IDs and `aux_labels` are word IDs.
+ If it has only `labels`, we assume its `labels` are word IDs.
+ use_double_scores:
+ True to use double precision when computing shortest path.
+ False to use single precision.
+ Returns:
+ Return a new Nbest. This new Nbest shares the same shape with `self`,
+ while its `fsa` is the 1-best path from intersecting `self.fsa` and
+ `lattice`. Also, its `fsa` has non-zero scores and inherits attributes
+ for `lattice`.
+ """
+ # Note: We view each linear FSA as a word sequence
+ # and we use the passed lattice to give each word sequence a score.
+ #
+ # We are not viewing each linear FSAs as a token sequence.
+ #
+ # So we use k2.invert() here.
+
+ # We use a word fsa to intersect with k2.invert(lattice)
+ word_fsa = k2.invert(self.fsa)
+
+ if hasattr(lattice, "aux_labels"):
+ # delete token IDs as it is not needed
+ del word_fsa.aux_labels
+
+ word_fsa.scores.zero_()
+ word_fsa_with_epsilon_loops = k2.remove_epsilon_and_add_self_loops(
+ word_fsa
+ )
+
+ path_to_utt_map = self.shape.row_ids(1)
+
+ if hasattr(lattice, "aux_labels"):
+ # lattice has token IDs as labels and word IDs as aux_labels.
+ # inv_lattice has word IDs as labels and token IDs as aux_labels
+ inv_lattice = k2.invert(lattice)
+ inv_lattice = k2.arc_sort(inv_lattice)
+ else:
+ inv_lattice = k2.arc_sort(lattice)
+
+ if inv_lattice.shape[0] == 1:
+ path_lattice = _intersect_device(
+ inv_lattice,
+ word_fsa_with_epsilon_loops,
+ b_to_a_map=torch.zeros_like(path_to_utt_map),
+ sorted_match_a=True,
+ )
+ else:
+ path_lattice = _intersect_device(
+ inv_lattice,
+ word_fsa_with_epsilon_loops,
+ b_to_a_map=path_to_utt_map,
+ sorted_match_a=True,
+ )
+
+ # path_lattice has word IDs as labels and token IDs as aux_labels
+ path_lattice = k2.top_sort(k2.connect(path_lattice))
+
+ one_best = k2.shortest_path(
+ path_lattice, use_double_scores=use_double_scores
+ )
+
+ one_best = k2.invert(one_best)
+ # Now one_best has token IDs as labels and word IDs as aux_labels
+
+ return Nbest(fsa=one_best, shape=self.shape)
+
+ def compute_am_scores(self) -> k2.RaggedTensor:
+ """Compute AM scores of each linear FSA (i.e., each path within
+ an utterance).
+
+ Hint:
+ `self.fsa.scores` contains two parts: acoustic scores (AM scores)
+ and n-gram language model scores (LM scores).
+
+ Caution:
+ We require that ``self.fsa`` has an attribute ``lm_scores``.
+
+ Returns:
+ Return a ragged tensor with 2 axes [utt][path_scores].
+ Its dtype is torch.float64.
+ """
+ saved_scores = self.fsa.scores
+
+ # The `scores` of every arc consists of `am_scores` and `lm_scores`
+ self.fsa.scores = self.fsa.scores - self.fsa.lm_scores
+
+ am_scores = self.fsa.get_tot_scores(
+ use_double_scores=True, log_semiring=False
+ )
+ self.fsa.scores = saved_scores
+
+ return k2.RaggedTensor(self.shape, am_scores)
+
+ def compute_lm_scores(self) -> k2.RaggedTensor:
+ """Compute LM scores of each linear FSA (i.e., each path within
+ an utterance).
+
+ Hint:
+ `self.fsa.scores` contains two parts: acoustic scores (AM scores)
+ and n-gram language model scores (LM scores).
+
+ Caution:
+ We require that ``self.fsa`` has an attribute ``lm_scores``.
+
+ Returns:
+ Return a ragged tensor with 2 axes [utt][path_scores].
+ Its dtype is torch.float64.
+ """
+ saved_scores = self.fsa.scores
+
+ # The `scores` of every arc consists of `am_scores` and `lm_scores`
+ self.fsa.scores = self.fsa.lm_scores
+
+ lm_scores = self.fsa.get_tot_scores(
+ use_double_scores=True, log_semiring=False
+ )
+ self.fsa.scores = saved_scores
+
+ return k2.RaggedTensor(self.shape, lm_scores)
+
+ def tot_scores(self) -> k2.RaggedTensor:
+ """Get total scores of FSAs in this Nbest.
+
+ Note:
+ Since FSAs in Nbest are just linear FSAs, log-semiring
+ and tropical semiring produce the same total scores.
+
+ Returns:
+ Return a ragged tensor with two axes [utt][path_scores].
+ Its dtype is torch.float64.
+ """
+ scores = self.fsa.get_tot_scores(
+ use_double_scores=True, log_semiring=False
+ )
+ return k2.RaggedTensor(self.shape, scores)
+
+ def build_levenshtein_graphs(self) -> k2.Fsa:
+ """Return an FsaVec with axes [utt][state][arc]."""
+ word_ids = get_texts(self.fsa, return_ragged=True)
+ return k2.levenshtein_graph(word_ids)
+
+
def one_best_decoding(
- lattice: k2.Fsa, use_double_scores: bool = True
+ lattice: k2.Fsa,
+ use_double_scores: bool = True,
) -> k2.Fsa:
"""Get the best path from a lattice.
@@ -181,200 +446,143 @@ def nbest_decoding(
lattice: k2.Fsa,
num_paths: int,
use_double_scores: bool = True,
- scale: float = 1.0,
+ lattice_score_scale: float = 1.0,
) -> k2.Fsa:
"""It implements something like CTC prefix beam search using n-best lists.
- The basic idea is to first extra n-best paths from the given lattice,
- build a word seqs from these paths, and compute the total scores
- of these sequences in the log-semiring. The one with the max score
+ The basic idea is to first extract `num_paths` paths from the given lattice,
+ build a word sequence from these paths, and compute the total scores
+ of the word sequence in the tropical semiring. The one with the max score
is used as the decoding output.
Caution:
Don't be confused by `best` in the name `n-best`. Paths are selected
- randomly, not by ranking their scores.
+ **randomly**, not by ranking their scores.
+
+ Hint:
+ This decoding method is for demonstration only and it does
+ not produce a lower WER than :func:`one_best_decoding`.
Args:
lattice:
- The decoding lattice, returned by :func:`get_lattice`.
+ The decoding lattice, e.g., can be the return value of
+ :func:`get_lattice`. It has 3 axes [utt][state][arc].
num_paths:
It specifies the size `n` in n-best. Note: Paths are selected randomly
- and those containing identical word sequences are remove dand only one
+ and those containing identical word sequences are removed and only one
of them is kept.
use_double_scores:
True to use double precision floating point in the computation.
False to use single precision.
- scale:
- It's the scale applied to the lattice.scores. A smaller value
- yields more unique paths.
+ lattice_score_scale:
+ It's the scale applied to the `lattice.scores`. A smaller value
+ leads to more unique paths at the risk of missing the correct path.
Returns:
- An FsaVec containing linear FSAs.
+ An FsaVec containing **linear** FSAs. It axes are [utt][state][arc].
"""
- path = _get_random_paths(
+ nbest = Nbest.from_lattice(
lattice=lattice,
num_paths=num_paths,
use_double_scores=use_double_scores,
- scale=scale,
+ lattice_score_scale=lattice_score_scale,
)
+ # nbest.fsa.scores contains 0s
- # word_seq is a k2.RaggedInt sharing the same shape as `path`
- # but it contains word IDs. Note that it also contains 0s and -1s.
- # The last entry in each sublist is -1.
- word_seq = k2.index(lattice.aux_labels, path)
- # Note: the above operation supports also the case when
- # lattice.aux_labels is a ragged tensor. In that case,
- # `remove_axis=True` is used inside the pybind11 binding code,
- # so the resulting `word_seq` still has 3 axes, like `path`.
- # The 3 axes are [seq][path][word_id]
+ nbest = nbest.intersect(lattice)
+ # now nbest.fsa.scores gets assigned
- # Remove 0 (epsilon) and -1 from word_seq
- word_seq = k2.ragged.remove_values_leq(word_seq, 0)
+ # max_indexes contains the indexes for the path with the maximum score
+ # within an utterance.
+ max_indexes = nbest.tot_scores().argmax()
- # Remove sequences with identical word sequences.
- #
- # k2.ragged.unique_sequences will reorder paths within a seq.
- # `new2old` is a 1-D torch.Tensor mapping from the output path index
- # to the input path index.
- # new2old.numel() == unique_word_seqs.tot_size(1)
- unique_word_seq, _, new2old = k2.ragged.unique_sequences(
- word_seq, need_num_repeats=False, need_new2old_indexes=True
- )
- # Note: unique_word_seq still has the same axes as word_seq
-
- seq_to_path_shape = k2.ragged.get_layer(unique_word_seq.shape(), 0)
-
- # path_to_seq_map is a 1-D torch.Tensor.
- # path_to_seq_map[i] is the seq to which the i-th path belongs
- path_to_seq_map = seq_to_path_shape.row_ids(1)
-
- # Remove the seq axis.
- # Now unique_word_seq has only two axes [path][word]
- unique_word_seq = k2.ragged.remove_axis(unique_word_seq, 0)
-
- # word_fsa is an FsaVec with axes [path][state][arc]
- word_fsa = k2.linear_fsa(unique_word_seq)
-
- # add epsilon self loops since we will use
- # k2.intersect_device, which treats epsilon as a normal symbol
- word_fsa_with_epsilon_loops = k2.add_epsilon_self_loops(word_fsa)
-
- # lattice has token IDs as labels and word IDs as aux_labels.
- # inv_lattice has word IDs as labels and token IDs as aux_labels
- inv_lattice = k2.invert(lattice)
- inv_lattice = k2.arc_sort(inv_lattice)
-
- path_lattice = _intersect_device(
- inv_lattice,
- word_fsa_with_epsilon_loops,
- b_to_a_map=path_to_seq_map,
- sorted_match_a=True,
- )
- # path_lat has word IDs as labels and token IDs as aux_labels
-
- path_lattice = k2.top_sort(k2.connect(path_lattice))
-
- tot_scores = path_lattice.get_tot_scores(
- use_double_scores=use_double_scores, log_semiring=False
- )
-
- # RaggedFloat currently supports float32 only.
- # If Ragged is wrapped, we can use k2.RaggedDouble here
- ragged_tot_scores = k2.RaggedFloat(
- seq_to_path_shape, tot_scores.to(torch.float32)
- )
-
- argmax_indexes = k2.ragged.argmax_per_sublist(ragged_tot_scores)
-
- # Since we invoked `k2.ragged.unique_sequences`, which reorders
- # the index from `path`, we use `new2old` here to convert argmax_indexes
- # to the indexes into `path`.
- #
- # Use k2.index here since argmax_indexes' dtype is torch.int32
- best_path_indexes = k2.index(new2old, argmax_indexes)
-
- path_2axes = k2.ragged.remove_axis(path, 0)
-
- # best_path is a k2.RaggedInt with 2 axes [path][arc_pos]
- best_path = k2.index(path_2axes, best_path_indexes)
-
- # labels is a k2.RaggedInt with 2 axes [path][token_id]
- # Note that it contains -1s.
- labels = k2.index(lattice.labels.contiguous(), best_path)
-
- labels = k2.ragged.remove_values_eq(labels, -1)
-
- # lattice.aux_labels is a k2.RaggedInt tensor with 2 axes, so
- # aux_labels is also a k2.RaggedInt with 2 axes
- aux_labels = k2.index(lattice.aux_labels, best_path.values())
-
- best_path_fsa = k2.linear_fsa(labels)
- best_path_fsa.aux_labels = aux_labels
- return best_path_fsa
+ best_path = k2.index_fsa(nbest.fsa, max_indexes)
+ return best_path
-def compute_am_and_lm_scores(
+def nbest_oracle(
lattice: k2.Fsa,
- word_fsa_with_epsilon_loops: k2.Fsa,
- path_to_seq_map: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
- """Compute AM scores of n-best lists (represented as word_fsas).
+ num_paths: int,
+ ref_texts: List[str],
+ word_table: k2.SymbolTable,
+ use_double_scores: bool = True,
+ lattice_score_scale: float = 0.5,
+ oov: str = "",
+) -> Dict[str, List[List[int]]]:
+ """Select the best hypothesis given a lattice and a reference transcript.
+
+ The basic idea is to extract `num_paths` paths from the given lattice,
+ unique them, and select the one that has the minimum edit distance with
+ the corresponding reference transcript as the decoding output.
+
+ The decoding result returned from this function is the best result that
+ we can obtain using n-best decoding with all kinds of rescoring techniques.
+
+ This function is useful to tune the value of `lattice_score_scale`.
Args:
lattice:
- An FsaVec, e.g., the return value of :func:`get_lattice`
- It must have the attribute `lm_scores`.
- word_fsa_with_epsilon_loops:
- An FsaVec representing an n-best list. Note that it has been processed
- by `k2.add_epsilon_self_loops`.
- path_to_seq_map:
- A 1-D torch.Tensor with dtype torch.int32. path_to_seq_map[i] indicates
- which sequence the i-th Fsa in word_fsa_with_epsilon_loops belongs to.
- path_to_seq_map.numel() == word_fsas_with_epsilon_loops.arcs.dim0().
- Returns:
- Return a tuple containing two 1-D torch.Tensors: (am_scores, lm_scores).
- Each tensor's `numel()' equals to `word_fsas_with_epsilon_loops.shape[0]`
+ An FsaVec with axes [utt][state][arc].
+ Note: We assume its `aux_labels` contains word IDs.
+ num_paths:
+ The size of `n` in n-best.
+ ref_texts:
+ A list of reference transcript. Each entry contains space(s)
+ separated words
+ word_table:
+ It is the word symbol table.
+ use_double_scores:
+ True to use double precision for computation. False to use
+ single precision.
+ lattice_score_scale:
+ It's the scale applied to the lattice.scores. A smaller value
+ yields more unique paths.
+ oov:
+ The out of vocabulary word.
+ Return:
+ Return a dict. Its key contains the information about the parameters
+ when calling this function, while its value contains the decoding output.
+ `len(ans_dict) == len(ref_texts)`
"""
- assert len(lattice.shape) == 3
- assert hasattr(lattice, "lm_scores")
+ device = lattice.device
- # k2.compose() currently does not support b_to_a_map. To void
- # replicating `lats`, we use k2.intersect_device here.
- #
- # lattice has token IDs as `labels` and word IDs as aux_labels, so we
- # need to invert it here.
- inv_lattice = k2.invert(lattice)
-
- # Now the `labels` of inv_lattice are word IDs (a 1-D torch.Tensor)
- # and its `aux_labels` are token IDs ( a k2.RaggedInt with 2 axes)
-
- # Remove its `aux_labels` since it is not needed in the
- # following computation
- del inv_lattice.aux_labels
- inv_lattice = k2.arc_sort(inv_lattice)
-
- path_lattice = _intersect_device(
- inv_lattice,
- word_fsa_with_epsilon_loops,
- b_to_a_map=path_to_seq_map,
- sorted_match_a=True,
+ nbest = Nbest.from_lattice(
+ lattice=lattice,
+ num_paths=num_paths,
+ use_double_scores=use_double_scores,
+ lattice_score_scale=lattice_score_scale,
)
- path_lattice = k2.top_sort(k2.connect(path_lattice))
+ hyps = nbest.build_levenshtein_graphs()
- # The `scores` of every arc consists of `am_scores` and `lm_scores`
- path_lattice.scores = path_lattice.scores - path_lattice.lm_scores
+ oov_id = word_table[oov]
+ word_ids_list = []
+ for text in ref_texts:
+ word_ids = []
+ for word in text.split():
+ if word in word_table:
+ word_ids.append(word_table[word])
+ else:
+ word_ids.append(oov_id)
+ word_ids_list.append(word_ids)
- am_scores = path_lattice.get_tot_scores(
- use_double_scores=True, log_semiring=False
+ refs = k2.levenshtein_graph(word_ids_list, device=device)
+
+ levenshtein_alignment = k2.levenshtein_alignment(
+ refs=refs,
+ hyps=hyps,
+ hyp_to_ref_map=nbest.shape.row_ids(1),
+ sorted_match_ref=True,
)
- path_lattice.scores = path_lattice.lm_scores
-
- lm_scores = path_lattice.get_tot_scores(
- use_double_scores=True, log_semiring=False
+ tot_scores = levenshtein_alignment.get_tot_scores(
+ use_double_scores=False, log_semiring=False
)
+ ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores)
- return am_scores.to(torch.float32), lm_scores.to(torch.float32)
+ max_indexes = ragged_tot_scores.argmax()
+
+ best_path = k2.index_fsa(nbest.fsa, max_indexes)
+ return best_path
def rescore_with_n_best_list(
@@ -382,34 +590,32 @@ def rescore_with_n_best_list(
G: k2.Fsa,
num_paths: int,
lm_scale_list: List[float],
- scale: float = 1.0,
+ lattice_score_scale: float = 1.0,
+ use_double_scores: bool = True,
) -> Dict[str, k2.Fsa]:
- """Decode using n-best list with LM rescoring.
-
- `lattice` is a decoding lattice with 3 axes. This function first
- extracts `num_paths` paths from `lattice` for each sequence using
- `k2.random_paths`. The `am_scores` of these paths are computed.
- For each path, its `lm_scores` is computed using `G` (which is an LM).
- The final `tot_scores` is the sum of `am_scores` and `lm_scores`.
- The path with the largest `tot_scores` within a sequence is used
- as the decoding output.
+ """Rescore an n-best list with an n-gram LM.
+ The path with the maximum score is used as the decoding output.
Args:
lattice:
- An FsaVec. It can be the return value of :func:`get_lattice`.
+ An FsaVec with axes [utt][state][arc]. It must have the following
+ attributes: ``aux_labels`` and ``lm_scores``. Its labels are
+ token IDs and ``aux_labels`` word IDs.
G:
- An FsaVec representing the language model (LM). Note that it
- is an FsaVec, but it contains only one Fsa.
+ An FsaVec containing only a single FSA. It is an n-gram LM.
num_paths:
- It is the size `n` in `n-best` list.
+ Size of nbest list.
lm_scale_list:
- A list containing lm_scale values.
- scale:
- It's the scale applied to the lattice.scores. A smaller value
- yields more unique paths.
+ A list of float representing LM score scales.
+ lattice_score_scale:
+ Scale to be applied to ``lattice.score`` when sampling paths
+ using ``k2.random_paths``.
+ use_double_scores:
+ True to use double precision during computation. False to use
+ single precision.
Returns:
A dict of FsaVec, whose key is an lm_scale and the value is the
- best decoding path for each sequence in the lattice.
+ best decoding path for each utterance in the lattice.
"""
device = lattice.device
@@ -421,112 +627,32 @@ def rescore_with_n_best_list(
assert G.device == device
assert hasattr(G, "aux_labels") is False
- path = _get_random_paths(
+ nbest = Nbest.from_lattice(
lattice=lattice,
num_paths=num_paths,
- use_double_scores=True,
- scale=scale,
+ use_double_scores=use_double_scores,
+ lattice_score_scale=lattice_score_scale,
)
+ # nbest.fsa.scores are all 0s at this point
- # word_seq is a k2.RaggedInt sharing the same shape as `path`
- # but it contains word IDs. Note that it also contains 0s and -1s.
- # The last entry in each sublist is -1.
- word_seq = k2.index(lattice.aux_labels, path)
+ nbest = nbest.intersect(lattice)
+ # Now nbest.fsa has its scores set
+ assert hasattr(nbest.fsa, "lm_scores")
- # Remove epsilons and -1 from word_seq
- word_seq = k2.ragged.remove_values_leq(word_seq, 0)
+ am_scores = nbest.compute_am_scores()
- # Remove paths that has identical word sequences.
- #
- # unique_word_seq is still a k2.RaggedInt with 3 axes [seq][path][word]
- # except that there are no repeated paths with the same word_seq
- # within a sequence.
- #
- # num_repeats is also a k2.RaggedInt with 2 axes containing the
- # multiplicities of each path.
- # num_repeats.num_elements() == unique_word_seqs.tot_size(1)
- #
- # Since k2.ragged.unique_sequences will reorder paths within a seq,
- # `new2old` is a 1-D torch.Tensor mapping from the output path index
- # to the input path index.
- # new2old.numel() == unique_word_seqs.tot_size(1)
- unique_word_seq, num_repeats, new2old = k2.ragged.unique_sequences(
- word_seq, need_num_repeats=True, need_new2old_indexes=True
- )
-
- seq_to_path_shape = k2.ragged.get_layer(unique_word_seq.shape(), 0)
-
- # path_to_seq_map is a 1-D torch.Tensor.
- # path_to_seq_map[i] is the seq to which the i-th path
- # belongs.
- path_to_seq_map = seq_to_path_shape.row_ids(1)
-
- # Remove the seq axis.
- # Now unique_word_seq has only two axes [path][word]
- unique_word_seq = k2.ragged.remove_axis(unique_word_seq, 0)
-
- # word_fsa is an FsaVec with axes [path][state][arc]
- word_fsa = k2.linear_fsa(unique_word_seq)
-
- word_fsa_with_epsilon_loops = k2.add_epsilon_self_loops(word_fsa)
-
- am_scores, _ = compute_am_and_lm_scores(
- lattice, word_fsa_with_epsilon_loops, path_to_seq_map
- )
-
- # Now compute lm_scores
- b_to_a_map = torch.zeros_like(path_to_seq_map)
- lm_path_lattice = _intersect_device(
- G,
- word_fsa_with_epsilon_loops,
- b_to_a_map=b_to_a_map,
- sorted_match_a=True,
- )
- lm_path_lattice = k2.top_sort(k2.connect(lm_path_lattice))
- lm_scores = lm_path_lattice.get_tot_scores(
- use_double_scores=True, log_semiring=False
- )
-
- path_2axes = k2.ragged.remove_axis(path, 0)
+ nbest = nbest.intersect(G)
+ # Now nbest contains only lm scores
+ lm_scores = nbest.tot_scores()
ans = dict()
for lm_scale in lm_scale_list:
- tot_scores = am_scores / lm_scale + lm_scores
-
- # Remember that we used `k2.ragged.unique_sequences` to remove repeated
- # paths to avoid redundant computation in `k2.intersect_device`.
- # Now we use `num_repeats` to correct the scores for each path.
- #
- # NOTE(fangjun): It is commented out as it leads to a worse WER
- # tot_scores = tot_scores * num_repeats.values()
-
- ragged_tot_scores = k2.RaggedFloat(
- seq_to_path_shape, tot_scores.to(torch.float32)
- )
- argmax_indexes = k2.ragged.argmax_per_sublist(ragged_tot_scores)
-
- # Use k2.index here since argmax_indexes' dtype is torch.int32
- best_path_indexes = k2.index(new2old, argmax_indexes)
-
- # best_path is a k2.RaggedInt with 2 axes [path][arc_pos]
- best_path = k2.index(path_2axes, best_path_indexes)
-
- # labels is a k2.RaggedInt with 2 axes [path][phone_id]
- # Note that it contains -1s.
- labels = k2.index(lattice.labels.contiguous(), best_path)
-
- labels = k2.ragged.remove_values_eq(labels, -1)
-
- # lattice.aux_labels is a k2.RaggedInt tensor with 2 axes, so
- # aux_labels is also a k2.RaggedInt with 2 axes
- aux_labels = k2.index(lattice.aux_labels, best_path.values())
-
- best_path_fsa = k2.linear_fsa(labels)
- best_path_fsa.aux_labels = aux_labels
-
+ tot_scores = am_scores.values / lm_scale + lm_scores.values
+ tot_scores = k2.RaggedTensor(nbest.shape, tot_scores)
+ max_indexes = tot_scores.argmax()
+ best_path = k2.index_fsa(nbest.fsa, max_indexes)
key = f"lm_scale_{lm_scale}"
- ans[key] = best_path_fsa
-
+ ans[key] = best_path
return ans
@@ -534,25 +660,40 @@ def rescore_with_whole_lattice(
lattice: k2.Fsa,
G_with_epsilon_loops: k2.Fsa,
lm_scale_list: Optional[List[float]] = None,
+ use_double_scores: bool = True,
) -> Union[k2.Fsa, Dict[str, k2.Fsa]]:
- """Use whole lattice to rescore.
+ """Intersect the lattice with an n-gram LM and use shortest path
+ to decode.
+
+ The input lattice is obtained by intersecting `HLG` with
+ a DenseFsaVec, where the `G` in `HLG` is in general a 3-gram LM.
+ The input `G_with_epsilon_loops` is usually a 4-gram LM. You can consider
+ this function as a second pass decoding. In the first pass decoding, we
+ use a small G, while we use a larger G in the second pass decoding.
Args:
lattice:
- An FsaVec It can be the return value of :func:`get_lattice`.
+ An FsaVec with axes [utt][state][arc]. Its `aux_lables` are word IDs.
+ It must have an attribute `lm_scores`.
G_with_epsilon_loops:
- An FsaVec representing the language model (LM). Note that it
- is an FsaVec, but it contains only one Fsa.
+ An FsaVec containing only a single FSA. It contains epsilon self-loops.
+ It is an acceptor and its labels are word IDs.
lm_scale_list:
- A list containing lm_scale values or None.
+ Optional. If none, return the intersection of `lattice` and
+ `G_with_epsilon_loops`.
+ If not None, it contains a list of values to scale LM scores.
+ For each scale, there is a corresponding decoding result contained in
+ the resulting dict.
+ use_double_scores:
+ True to use double precision in the computation.
+ False to use single precision.
Returns:
- If lm_scale_list is not None, return a dict of FsaVec, whose key
- is a lm_scale and the value represents the best decoding path for
- each sequence in the lattice.
- If lm_scale_list is not None, return a lattice that is rescored
- with the given LM.
+ If `lm_scale_list` is None, return a new lattice which is the intersection
+ result of `lattice` and `G_with_epsilon_loops`.
+ Otherwise, return a dict whose key is an entry in `lm_scale_list` and the
+ value is the decoding result (i.e., an FsaVec containing linear FSAs).
"""
- assert len(lattice.shape) == 3
+ # Nbest is not used in this function
assert hasattr(lattice, "lm_scores")
assert G_with_epsilon_loops.shape == (1, None, None)
@@ -560,19 +701,22 @@ def rescore_with_whole_lattice(
lattice.scores = lattice.scores - lattice.lm_scores
# We will use lm_scores from G, so remove lats.lm_scores here
del lattice.lm_scores
- assert hasattr(lattice, "lm_scores") is False
assert hasattr(G_with_epsilon_loops, "lm_scores")
# Now, lattice.scores contains only am_scores
# inv_lattice has word IDs as labels.
- # Its aux_labels are token IDs, which is a ragged tensor k2.RaggedInt
+ # Its `aux_labels` is token IDs
inv_lattice = k2.invert(lattice)
num_seqs = lattice.shape[0]
b_to_a_map = torch.zeros(num_seqs, device=device, dtype=torch.int32)
- while True:
+
+ max_loop_count = 10
+ loop_count = 0
+ while loop_count <= max_loop_count:
+ loop_count += 1
try:
rescoring_lattice = k2.intersect_device(
G_with_epsilon_loops,
@@ -588,12 +732,15 @@ def rescore_with_whole_lattice(
f"num_arcs before pruning: {inv_lattice.arcs.num_elements()}"
)
- # NOTE(fangjun): The choice of the threshold 1e-7 is arbitrary here
- # to avoid OOM. We may need to fine tune it.
- inv_lattice = k2.prune_on_arc_post(inv_lattice, 1e-7, True)
+ # NOTE(fangjun): The choice of the threshold 1e-9 is arbitrary here
+ # to avoid OOM. You may need to fine tune it.
+ inv_lattice = k2.prune_on_arc_post(inv_lattice, 1e-9, True)
logging.info(
f"num_arcs after pruning: {inv_lattice.arcs.num_elements()}"
)
+ if loop_count > max_loop_count:
+ logging.info("Return None as the resulting lattice is too large")
+ return None
# lat has token IDs as labels
# and word IDs as aux_labels.
@@ -603,112 +750,37 @@ def rescore_with_whole_lattice(
return lat
ans = dict()
- #
- # The following implements
- # scores = (scores - lm_scores)/lm_scale + lm_scores
- # = scores/lm_scale + lm_scores*(1 - 1/lm_scale)
- #
saved_am_scores = lat.scores - lat.lm_scores
for lm_scale in lm_scale_list:
am_scores = saved_am_scores / lm_scale
lat.scores = am_scores + lat.lm_scores
- best_path = k2.shortest_path(lat, use_double_scores=True)
+ best_path = k2.shortest_path(lat, use_double_scores=use_double_scores)
key = f"lm_scale_{lm_scale}"
ans[key] = best_path
return ans
-def nbest_oracle(
- lattice: k2.Fsa,
- num_paths: int,
- ref_texts: List[str],
- lexicon: Lexicon,
- scale: float = 1.0,
-) -> Dict[str, List[List[int]]]:
- """Select the best hypothesis given a lattice and a reference transcript.
-
- The basic idea is to extract n paths from the given lattice, unique them,
- and select the one that has the minimum edit distance with the corresponding
- reference transcript as the decoding output.
-
- The decoding result returned from this function is the best result that
- we can obtain using n-best decoding with all kinds of rescoring techniques.
-
- Args:
- lattice:
- An FsaVec. It can be the return value of :func:`get_lattice`.
- Note: We assume its aux_labels contain word IDs.
- num_paths:
- The size of `n` in n-best.
- ref_texts:
- A list of reference transcript. Each entry contains space(s)
- separated words
- lexicon:
- It is used to convert word IDs to word symbols.
- scale:
- It's the scale applied to the lattice.scores. A smaller value
- yields more unique paths.
- Return:
- Return a dict. Its key contains the information about the parameters
- when calling this function, while its value contains the decoding output.
- `len(ans_dict) == len(ref_texts)`
- """
- path = _get_random_paths(
- lattice=lattice,
- num_paths=num_paths,
- use_double_scores=True,
- scale=scale,
- )
-
- word_seq = k2.index(lattice.aux_labels, path)
- word_seq = k2.ragged.remove_values_leq(word_seq, 0)
- unique_word_seq, _, _ = k2.ragged.unique_sequences(
- word_seq, need_num_repeats=False, need_new2old_indexes=False
- )
- unique_word_ids = k2.ragged.to_list(unique_word_seq)
- assert len(unique_word_ids) == len(ref_texts)
- # unique_word_ids[i] contains all hypotheses of the i-th utterance
-
- results = []
- for hyps, ref in zip(unique_word_ids, ref_texts):
- # Note hyps is a list-of-list ints
- # Each sublist contains a hypothesis
- ref_words = ref.strip().split()
- # CAUTION: We don't convert ref_words to ref_words_ids
- # since there may exist OOV words in ref_words
- best_hyp_words = None
- min_error = float("inf")
- for hyp_words in hyps:
- hyp_words = [lexicon.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
- best_hyp_words = hyp_words
- results.append(best_hyp_words)
-
- return {f"nbest_{num_paths}_scale_{scale}_oracle": results}
-
-
def rescore_with_attention_decoder(
lattice: k2.Fsa,
num_paths: int,
- model: nn.Module,
+ model: torch.nn.Module,
memory: torch.Tensor,
memory_key_padding_mask: Optional[torch.Tensor],
sos_id: int,
eos_id: int,
- scale: float = 1.0,
+ lattice_score_scale: float = 1.0,
ngram_lm_scale: Optional[float] = None,
attention_scale: Optional[float] = None,
+ use_double_scores: bool = True,
) -> Dict[str, k2.Fsa]:
- """This function extracts n paths from the given lattice and uses
- an attention decoder to rescore them. The path with the highest
- score is used as the decoding output.
+ """This function extracts `num_paths` paths from the given lattice and uses
+ an attention decoder to rescore them. The path with the highest score is
+ the decoding output.
Args:
lattice:
- An FsaVec. It can be the return value of :func:`get_lattice`.
+ An FsaVec with axes [utt][state][arc].
num_paths:
Number of paths to extract from the given lattice for rescoring.
model:
@@ -717,16 +789,16 @@ def rescore_with_attention_decoder(
memory:
The encoder memory of the given model. It is the output of
the last torch.nn.TransformerEncoder layer in the given model.
- Its shape is `[T, N, C]`.
+ Its shape is `(T, N, C)`.
memory_key_padding_mask:
- The padding mask for memory with shape [N, T].
+ The padding mask for memory with shape `(N, T)`.
sos_id:
The token ID for SOS.
eos_id:
The token ID for EOS.
- scale:
- It's the scale applied to the lattice.scores. A smaller value
- yields more unique paths.
+ lattice_score_scale:
+ It's the scale applied to `lattice.scores`. A smaller value
+ leads to more unique paths at the risk of missing the correct path.
ngram_lm_scale:
Optional. It specifies the scale for n-gram LM scores.
attention_scale:
@@ -734,97 +806,47 @@ def rescore_with_attention_decoder(
Returns:
A dict of FsaVec, whose key contains a string
ngram_lm_scale_attention_scale and the value is the
- best decoding path for each sequence in the lattice.
+ best decoding path for each utterance in the lattice.
"""
- # First, extract `num_paths` paths for each sequence.
- # path is a k2.RaggedInt with axes [seq][path][arc_pos]
- path = _get_random_paths(
+ nbest = Nbest.from_lattice(
lattice=lattice,
num_paths=num_paths,
- use_double_scores=True,
- scale=scale,
+ use_double_scores=use_double_scores,
+ lattice_score_scale=lattice_score_scale,
)
+ # nbest.fsa.scores are all 0s at this point
- # word_seq is a k2.RaggedInt sharing the same shape as `path`
- # but it contains word IDs. Note that it also contains 0s and -1s.
- # The last entry in each sublist is -1.
- word_seq = k2.index(lattice.aux_labels, path)
+ nbest = nbest.intersect(lattice)
+ # Now nbest.fsa has its scores set.
+ # Also, nbest.fsa inherits the attributes from `lattice`.
+ assert hasattr(nbest.fsa, "lm_scores")
- # Remove epsilons and -1 from word_seq
- word_seq = k2.ragged.remove_values_leq(word_seq, 0)
+ am_scores = nbest.compute_am_scores()
+ ngram_lm_scores = nbest.compute_lm_scores()
- # Remove paths that has identical word sequences.
- #
- # unique_word_seq is still a k2.RaggedInt with 3 axes [seq][path][word]
- # except that there are no repeated paths with the same word_seq
- # within a sequence.
- #
- # num_repeats is also a k2.RaggedInt with 2 axes containing the
- # multiplicities of each path.
- # num_repeats.num_elements() == unique_word_seqs.tot_size(1)
- #
- # Since k2.ragged.unique_sequences will reorder paths within a seq,
- # `new2old` is a 1-D torch.Tensor mapping from the output path index
- # to the input path index.
- # new2old.numel() == unique_word_seq.tot_size(1)
- unique_word_seq, num_repeats, new2old = k2.ragged.unique_sequences(
- word_seq, need_num_repeats=True, need_new2old_indexes=True
- )
+ # The `tokens` attribute is set inside `compile_hlg.py`
+ assert hasattr(nbest.fsa, "tokens")
+ assert isinstance(nbest.fsa.tokens, torch.Tensor)
- seq_to_path_shape = k2.ragged.get_layer(unique_word_seq.shape(), 0)
-
- # path_to_seq_map is a 1-D torch.Tensor.
- # path_to_seq_map[i] is the seq to which the i-th path
- # belongs.
- path_to_seq_map = seq_to_path_shape.row_ids(1)
-
- # Remove the seq axis.
- # Now unique_word_seq has only two axes [path][word]
- unique_word_seq = k2.ragged.remove_axis(unique_word_seq, 0)
-
- # word_fsa is an FsaVec with axes [path][state][arc]
- word_fsa = k2.linear_fsa(unique_word_seq)
-
- word_fsa_with_epsilon_loops = k2.add_epsilon_self_loops(word_fsa)
-
- am_scores, ngram_lm_scores = compute_am_and_lm_scores(
- lattice, word_fsa_with_epsilon_loops, path_to_seq_map
- )
- # Now we use the attention decoder to compute another
- # score: attention_scores.
- #
- # To do that, we have to get the input and output for the attention
- # decoder.
-
- # CAUTION: The "tokens" attribute is set in the file
- # local/compile_hlg.py
- token_seq = k2.index(lattice.tokens, path)
-
- # Remove epsilons and -1 from token_seq
- token_seq = k2.ragged.remove_values_leq(token_seq, 0)
-
- # Remove the seq axis.
- token_seq = k2.ragged.remove_axis(token_seq, 0)
-
- token_seq, _ = k2.ragged.index(
- token_seq, indexes=new2old, axis=0, need_value_indexes=False
- )
-
- # Now word in unique_word_seq has its corresponding token IDs.
- token_ids = k2.ragged.to_list(token_seq)
-
- num_word_seqs = new2old.numel()
-
- path_to_seq_map_long = path_to_seq_map.to(torch.long)
- expanded_memory = memory.index_select(1, path_to_seq_map_long)
+ path_to_utt_map = nbest.shape.row_ids(1).to(torch.long)
+ # the shape of memory is (T, N, C), so we use axis=1 here
+ expanded_memory = memory.index_select(1, path_to_utt_map)
if memory_key_padding_mask is not None:
+ # The shape of memory_key_padding_mask is (N, T), so we
+ # use axis=0 here.
expanded_memory_key_padding_mask = memory_key_padding_mask.index_select(
- 0, path_to_seq_map_long
+ 0, path_to_utt_map
)
else:
expanded_memory_key_padding_mask = None
+ # remove axis corresponding to states.
+ tokens_shape = nbest.fsa.arcs.shape().remove_axis(1)
+ tokens = k2.RaggedTensor(tokens_shape, nbest.fsa.tokens)
+ tokens = tokens.remove_values_leq(0)
+ token_ids = tokens.tolist()
+
nll = model.decoder_nll(
memory=expanded_memory,
memory_key_padding_mask=expanded_memory_key_padding_mask,
@@ -833,55 +855,36 @@ def rescore_with_attention_decoder(
eos_id=eos_id,
)
assert nll.ndim == 2
- assert nll.shape[0] == num_word_seqs
+ assert nll.shape[0] == len(token_ids)
attention_scores = -nll.sum(dim=1)
- assert attention_scores.ndim == 1
- assert attention_scores.numel() == num_word_seqs
if ngram_lm_scale is None:
- ngram_lm_scale_list = [0.1, 0.3, 0.5, 0.6, 0.7, 0.9, 1.0]
+ ngram_lm_scale_list = [0.01, 0.05, 0.08]
+ ngram_lm_scale_list += [0.1, 0.3, 0.5, 0.6, 0.7, 0.9, 1.0]
ngram_lm_scale_list += [1.1, 1.2, 1.3, 1.5, 1.7, 1.9, 2.0]
else:
ngram_lm_scale_list = [ngram_lm_scale]
if attention_scale is None:
- attention_scale_list = [0.1, 0.3, 0.5, 0.6, 0.7, 0.9, 1.0]
+ attention_scale_list = [0.01, 0.05, 0.08]
+ attention_scale_list += [0.1, 0.3, 0.5, 0.6, 0.7, 0.9, 1.0]
attention_scale_list += [1.1, 1.2, 1.3, 1.5, 1.7, 1.9, 2.0]
else:
attention_scale_list = [attention_scale]
- path_2axes = k2.ragged.remove_axis(path, 0)
-
ans = dict()
for n_scale in ngram_lm_scale_list:
for a_scale in attention_scale_list:
tot_scores = (
- am_scores
- + n_scale * ngram_lm_scores
+ am_scores.values
+ + n_scale * ngram_lm_scores.values
+ a_scale * attention_scores
)
- ragged_tot_scores = k2.RaggedFloat(seq_to_path_shape, tot_scores)
- argmax_indexes = k2.ragged.argmax_per_sublist(ragged_tot_scores)
-
- best_path_indexes = k2.index(new2old, argmax_indexes)
-
- # best_path is a k2.RaggedInt with 2 axes [path][arc_pos]
- best_path = k2.index(path_2axes, best_path_indexes)
-
- # labels is a k2.RaggedInt with 2 axes [path][token_id]
- # Note that it contains -1s.
- labels = k2.index(lattice.labels.contiguous(), best_path)
-
- labels = k2.ragged.remove_values_eq(labels, -1)
-
- # lattice.aux_labels is a k2.RaggedInt tensor with 2 axes, so
- # aux_labels is also a k2.RaggedInt with 2 axes
- aux_labels = k2.index(lattice.aux_labels, best_path.values())
-
- best_path_fsa = k2.linear_fsa(labels)
- best_path_fsa.aux_labels = aux_labels
+ ragged_tot_scores = k2.RaggedTensor(nbest.shape, tot_scores)
+ max_indexes = ragged_tot_scores.argmax()
+ best_path = k2.index_fsa(nbest.fsa, max_indexes)
key = f"ngram_lm_scale_{n_scale}_attention_scale_{a_scale}"
- ans[key] = best_path_fsa
+ ans[key] = best_path
return ans
diff --git a/icefall/graph_compiler.py b/icefall/graph_compiler.py
index 23ac247e8..b4c87d964 100644
--- a/icefall/graph_compiler.py
+++ b/icefall/graph_compiler.py
@@ -106,7 +106,7 @@ class CtcTrainingGraphCompiler(object):
word_ids_list = []
for text in texts:
word_ids = []
- for word in text.split(" "):
+ for word in text.split():
if word in self.word_table:
word_ids.append(self.word_table[word])
else:
diff --git a/icefall/lexicon.py b/icefall/lexicon.py
index f1127c7cf..6730bac49 100644
--- a/icefall/lexicon.py
+++ b/icefall/lexicon.py
@@ -157,7 +157,7 @@ class BpeLexicon(Lexicon):
lang_dir / "lexicon.txt"
)
- def convert_lexicon_to_ragged(self, filename: str) -> k2.RaggedInt:
+ def convert_lexicon_to_ragged(self, filename: str) -> k2.RaggedTensor:
"""Read a BPE lexicon from file and convert it to a
k2 ragged tensor.
@@ -200,19 +200,18 @@ class BpeLexicon(Lexicon):
)
values = torch.tensor(token_ids, dtype=torch.int32)
- return k2.RaggedInt(shape, values)
+ return k2.RaggedTensor(shape, values)
- def words_to_piece_ids(self, words: List[str]) -> k2.RaggedInt:
+ def words_to_piece_ids(self, words: List[str]) -> k2.RaggedTensor:
"""Convert a list of words to a ragged tensor contained
word piece IDs.
"""
word_ids = [self.word_table[w] for w in words]
word_ids = torch.tensor(word_ids, dtype=torch.int32)
- ragged, _ = k2.ragged.index(
- self.ragged_lexicon,
+ ragged, _ = self.ragged_lexicon.index(
indexes=word_ids,
- need_value_indexes=False,
axis=0,
+ need_value_indexes=False,
)
return ragged
diff --git a/icefall/utils.py b/icefall/utils.py
index 2994c2d47..23b4dd6c7 100644
--- a/icefall/utils.py
+++ b/icefall/utils.py
@@ -26,7 +26,6 @@ from pathlib import Path
from typing import Dict, Iterable, List, TextIO, Tuple, Union
import k2
-import k2.ragged as k2r
import kaldialign
import torch
import torch.distributed as dist
@@ -147,12 +146,20 @@ def get_env_info():
}
-# See
-# https://stackoverflow.com/questions/4984647/accessing-dict-keys-like-an-attribute # noqa
class AttributeDict(dict):
- __slots__ = ()
- __getattr__ = dict.__getitem__
- __setattr__ = dict.__setitem__
+ def __getattr__(self, key):
+ if key in self:
+ return self[key]
+ raise AttributeError(f"No such attribute '{key}'")
+
+ def __setattr__(self, key, value):
+ self[key] = value
+
+ def __delattr__(self, key):
+ if key in self:
+ del self[key]
+ return
+ raise AttributeError(f"No such attribute '{key}'")
def encode_supervisions(
@@ -187,7 +194,9 @@ def encode_supervisions(
return supervision_segments, texts
-def get_texts(best_paths: k2.Fsa) -> List[List[int]]:
+def get_texts(
+ best_paths: k2.Fsa, return_ragged: bool = False
+) -> Union[List[List[int]], k2.RaggedTensor]:
"""Extract the texts (as word IDs) from the best-path FSAs.
Args:
best_paths:
@@ -195,30 +204,35 @@ def get_texts(best_paths: k2.Fsa) -> List[List[int]]:
containing multiple FSAs, which is expected to be the result
of k2.shortest_path (otherwise the returned values won't
be meaningful).
+ return_ragged:
+ True to return a ragged tensor with two axes [utt][word_id].
+ False to return a list-of-list word IDs.
Returns:
Returns a list of lists of int, containing the label sequences we
decoded.
"""
- if isinstance(best_paths.aux_labels, k2.RaggedInt):
+ if isinstance(best_paths.aux_labels, k2.RaggedTensor):
# remove 0's and -1's.
- aux_labels = k2r.remove_values_leq(best_paths.aux_labels, 0)
- aux_shape = k2r.compose_ragged_shapes(
- best_paths.arcs.shape(), aux_labels.shape()
- )
+ aux_labels = best_paths.aux_labels.remove_values_leq(0)
+ # TODO: change arcs.shape() to arcs.shape
+ aux_shape = best_paths.arcs.shape().compose(aux_labels.shape)
# remove the states and arcs axes.
- aux_shape = k2r.remove_axis(aux_shape, 1)
- aux_shape = k2r.remove_axis(aux_shape, 1)
- aux_labels = k2.RaggedInt(aux_shape, aux_labels.values())
+ aux_shape = aux_shape.remove_axis(1)
+ aux_shape = aux_shape.remove_axis(1)
+ aux_labels = k2.RaggedTensor(aux_shape, aux_labels.values)
else:
# remove axis corresponding to states.
- aux_shape = k2r.remove_axis(best_paths.arcs.shape(), 1)
- aux_labels = k2.RaggedInt(aux_shape, best_paths.aux_labels)
+ aux_shape = best_paths.arcs.shape().remove_axis(1)
+ aux_labels = k2.RaggedTensor(aux_shape, best_paths.aux_labels)
# remove 0's and -1's.
- aux_labels = k2r.remove_values_leq(aux_labels, 0)
+ aux_labels = aux_labels.remove_values_leq(0)
- assert aux_labels.num_axes() == 2
- return k2r.to_list(aux_labels)
+ assert aux_labels.num_axes == 2
+ if return_ragged:
+ return aux_labels
+ else:
+ return aux_labels.tolist()
def store_transcripts(
diff --git a/test/test_bpe_graph_compiler.py b/test/test_bpe_graph_compiler.py
index 67d300b7d..e58c4f1c6 100755
--- a/test/test_bpe_graph_compiler.py
+++ b/test/test_bpe_graph_compiler.py
@@ -16,9 +16,10 @@
# limitations under the License.
+from pathlib import Path
+
from icefall.bpe_graph_compiler import BpeCtcTrainingGraphCompiler
from icefall.lexicon import BpeLexicon
-from pathlib import Path
def test():
diff --git a/test/test_decode.py b/test/test_decode.py
new file mode 100644
index 000000000..7ef127781
--- /dev/null
+++ b/test/test_decode.py
@@ -0,0 +1,62 @@
+#!/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 run this file in one of the two ways:
+
+ (1) cd icefall; pytest test/test_decode.py
+ (2) cd icefall; ./test/test_decode.py
+"""
+
+import k2
+from icefall.decode import Nbest
+
+
+def test_nbest_from_lattice():
+ s = """
+ 0 1 1 10 0.1
+ 0 1 5 10 0.11
+ 0 1 2 20 0.2
+ 1 2 3 30 0.3
+ 1 2 4 40 0.4
+ 2 3 -1 -1 0.5
+ 3
+ """
+ lattice = k2.Fsa.from_str(s, acceptor=False)
+ lattice = k2.Fsa.from_fsas([lattice, lattice])
+
+ nbest = Nbest.from_lattice(
+ lattice=lattice,
+ num_paths=10,
+ use_double_scores=True,
+ lattice_score_scale=0.5,
+ )
+ # each lattice has only 4 distinct paths that have different word sequences:
+ # 10->30
+ # 10->40
+ # 20->30
+ # 20->40
+ #
+ # So there should be only 4 paths for each lattice in the Nbest object
+ assert nbest.fsa.shape[0] == 4 * 2
+ assert nbest.shape.row_splits(1).tolist() == [0, 4, 8]
+
+ nbest2 = nbest.intersect(lattice)
+ tot_scores = nbest2.tot_scores()
+ argmax = tot_scores.argmax()
+ best_path = k2.index_fsa(nbest2.fsa, argmax)
+ print(best_path[0])
diff --git a/test/test_utils.py b/test/test_utils.py
index 2dd79689f..7ac52b289 100644
--- a/test/test_utils.py
+++ b/test/test_utils.py
@@ -60,7 +60,7 @@ def test_get_texts_ragged():
4
"""
)
- fsa1.aux_labels = k2.RaggedInt("[ [1 3 0 2] [] [4 0 1] [-1]]")
+ fsa1.aux_labels = k2.RaggedTensor("[ [1 3 0 2] [] [4 0 1] [-1]]")
fsa2 = k2.Fsa.from_str(
"""
@@ -70,7 +70,7 @@ def test_get_texts_ragged():
3
"""
)
- fsa2.aux_labels = k2.RaggedInt("[[3 0 5 0 8] [0 9 7 0] [-1]]")
+ fsa2.aux_labels = k2.RaggedTensor("[[3 0 5 0 8] [0 9 7 0] [-1]]")
fsas = k2.Fsa.from_fsas([fsa1, fsa2])
texts = get_texts(fsas)
assert texts == [[1, 3, 2, 4, 1], [3, 5, 8, 9, 7]]
@@ -108,3 +108,14 @@ def test_attribute_dict():
assert s["b"] == 20
s.c = 100
assert s["c"] == 100
+ assert hasattr(s, "a")
+ assert hasattr(s, "b")
+ assert getattr(s, "a") == 10
+ del s.a
+ assert hasattr(s, "a") is False
+ setattr(s, "c", 100)
+ s.c = 100
+ try:
+ del s.a
+ except AttributeError as ex:
+ print(f"Caught exception: {ex}")
+