mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-12 19:42:19 +00:00
Merge branch 'master' into docs
This commit is contained in:
pkufool
commit
be2795cff2
1
.flake8
1
.flake8
@ -5,7 +5,6 @@ max-line-length = 80
|
||||
per-file-ignores =
|
||||
# line too long
|
||||
egs/librispeech/ASR/conformer_ctc/conformer.py: E501,
|
||||
egs/librispeech/ASR/conformer_ctc/decode.py: E501,
|
||||
|
||||
exclude =
|
||||
.git,
|
||||
|
||||
2
.github/workflows/run-yesno-recipe.yml
vendored
2
.github/workflows/run-yesno-recipe.yml
vendored
@ -56,7 +56,7 @@ jobs:
|
||||
run: |
|
||||
python3 -m pip install --upgrade pip black flake8
|
||||
python3 -m pip install -U pip
|
||||
python3 -m pip install k2==1.4.dev20210822+cpu.torch1.7.1 -f https://k2-fsa.org/nightly/
|
||||
python3 -m pip install k2==1.7.dev20210908+cpu.torch1.7.1 -f https://k2-fsa.org/nightly/
|
||||
python3 -m pip install torchaudio==0.7.2
|
||||
python3 -m pip install git+https://github.com/lhotse-speech/lhotse
|
||||
|
||||
|
||||
2
.github/workflows/style_check.yml
vendored
2
.github/workflows/style_check.yml
vendored
@ -45,7 +45,7 @@ jobs:
|
||||
|
||||
- name: Install Python dependencies
|
||||
run: |
|
||||
python3 -m pip install --upgrade pip black flake8
|
||||
python3 -m pip install --upgrade pip black==21.6b0 flake8==3.9.2
|
||||
|
||||
- name: Run flake8
|
||||
shell: bash
|
||||
|
||||
3
.github/workflows/test.yml
vendored
3
.github/workflows/test.yml
vendored
@ -32,7 +32,8 @@ jobs:
|
||||
os: [ubuntu-18.04, macos-10.15]
|
||||
python-version: [3.6, 3.7, 3.8, 3.9]
|
||||
torch: ["1.8.1"]
|
||||
k2-version: ["1.4.dev20210822"]
|
||||
k2-version: ["1.7.dev20210908"]
|
||||
|
||||
fail-fast: false
|
||||
|
||||
steps:
|
||||
|
||||
2
.gitignore
vendored
2
.gitignore
vendored
@ -4,4 +4,4 @@ path.sh
|
||||
exp
|
||||
exp*/
|
||||
*.pt
|
||||
download/
|
||||
download
|
||||
|
||||
@ -1 +1,2 @@
|
||||
sphinx_rtd_theme
|
||||
sphinx
|
||||
|
||||
@ -16,7 +16,6 @@
|
||||
|
||||
import sphinx_rtd_theme
|
||||
|
||||
|
||||
# -- Project information -----------------------------------------------------
|
||||
|
||||
project = "icefall"
|
||||
|
||||
67
docs/source/contributing/code-style.rst
Normal file
67
docs/source/contributing/code-style.rst
Normal file
@ -0,0 +1,67 @@
|
||||
.. _follow the code style:
|
||||
|
||||
Follow the code style
|
||||
=====================
|
||||
|
||||
We use the following tools to make the code style to be as consistent as possible:
|
||||
|
||||
- `black <https://github.com/psf/black>`_, to format the code
|
||||
- `flake8 <https://github.com/PyCQA/flake8>`_, to check the style and quality of the code
|
||||
- `isort <https://github.com/PyCQA/isort>`_, to sort ``imports``
|
||||
|
||||
The following versions of the above tools are used:
|
||||
|
||||
- ``black == 12.6b0``
|
||||
- ``flake8 == 3.9.2``
|
||||
- ``isort == 5.9.2``
|
||||
|
||||
After running the following commands:
|
||||
|
||||
.. code-block::
|
||||
|
||||
$ git clone https://github.com/k2-fsa/icefall
|
||||
$ cd icefall
|
||||
$ pip install pre-commit
|
||||
$ pre-commit install
|
||||
|
||||
it will run the following checks whenever you run ``git commit``, **automatically**:
|
||||
|
||||
.. figure:: images/pre-commit-check.png
|
||||
:width: 600
|
||||
:align: center
|
||||
|
||||
pre-commit hooks invoked by ``git commit`` (Failed).
|
||||
|
||||
If any of the above checks failed, your ``git commit`` was not successful.
|
||||
Please fix any issues reported by the check tools.
|
||||
|
||||
.. HINT::
|
||||
|
||||
Some of the check tools, i.e., ``black`` and ``isort`` will modify
|
||||
the files to be commited **in-place**. So please run ``git status``
|
||||
after failure to see which file has been modified by the tools
|
||||
before you make any further changes.
|
||||
|
||||
After fixing all the failures, run ``git commit`` again and
|
||||
it should succeed this time:
|
||||
|
||||
.. figure:: images/pre-commit-check-success.png
|
||||
:width: 600
|
||||
:align: center
|
||||
|
||||
pre-commit hooks invoked by ``git commit`` (Succeeded).
|
||||
|
||||
If you want to check the style of your code before ``git commit``, you
|
||||
can do the following:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ cd icefall
|
||||
$ pip install black==21.6b0 flake8==3.9.2 isort==5.9.2
|
||||
$ black --check your_changed_file.py
|
||||
$ black your_changed_file.py # modify it in-place
|
||||
$
|
||||
$ flake8 your_changed_file.py
|
||||
$
|
||||
$ isort --check your_changed_file.py # modify it in-place
|
||||
$ isort your_changed_file.py
|
||||
45
docs/source/contributing/doc.rst
Normal file
45
docs/source/contributing/doc.rst
Normal file
@ -0,0 +1,45 @@
|
||||
Contributing to Documentation
|
||||
=============================
|
||||
|
||||
We use `sphinx <https://www.sphinx-doc.org/en/master/>`_
|
||||
for documentation.
|
||||
|
||||
Before writing documentation, you have to prepare the environment:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ cd docs
|
||||
$ pip install -r requirements.txt
|
||||
|
||||
After setting up the environment, you are ready to write documentation.
|
||||
Please refer to `reStructuredText Primer <https://www.sphinx-doc.org/en/master/usage/restructuredtext/basics.html>`_
|
||||
if you are not familiar with ``reStructuredText``.
|
||||
|
||||
After writing some documentation, you can build the documentation **locally**
|
||||
to preview what it looks like if it is published:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ cd docs
|
||||
$ make html
|
||||
|
||||
The generated documentation is in ``docs/build/html`` and can be viewed
|
||||
with the following commands:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
$ cd docs/build/html
|
||||
$ python3 -m http.server
|
||||
|
||||
It will print::
|
||||
|
||||
Serving HTTP on 0.0.0.0 port 8000 (http://0.0.0.0:8000/) ...
|
||||
|
||||
Open your browser, go to `<http://0.0.0.0:8000/>`_, and you will see
|
||||
the following:
|
||||
|
||||
.. figure:: images/doc-contrib.png
|
||||
:width: 600
|
||||
:align: center
|
||||
|
||||
View generated documentation locally with ``python3 -m http.server``.
|
||||
156
docs/source/contributing/how-to-create-a-recipe.rst
Normal file
156
docs/source/contributing/how-to-create-a-recipe.rst
Normal file
@ -0,0 +1,156 @@
|
||||
How to create a recipe
|
||||
======================
|
||||
|
||||
.. HINT::
|
||||
|
||||
Please read :ref:`follow the code style` to adjust your code sytle.
|
||||
|
||||
.. CAUTION::
|
||||
|
||||
``icefall`` is designed to be as Pythonic as possible. Please use
|
||||
Python in your recipe if possible.
|
||||
|
||||
Data Preparation
|
||||
----------------
|
||||
|
||||
We recommend you to prepare your training/test/validate dataset
|
||||
with `lhotse <https://github.com/lhotse-speech/lhotse>`_.
|
||||
|
||||
Please refer to `<https://lhotse.readthedocs.io/en/latest/index.html>`_
|
||||
for how to create a recipe in ``lhotse``.
|
||||
|
||||
.. HINT::
|
||||
|
||||
The ``yesno`` recipe in ``lhotse`` is a very good example.
|
||||
|
||||
Please refer to `<https://github.com/lhotse-speech/lhotse/pull/380>`_,
|
||||
which shows how to add a new recipe to ``lhotse``.
|
||||
|
||||
Suppose you would like to add a recipe for a dataset named ``foo``.
|
||||
You can do the following:
|
||||
|
||||
.. code-block::
|
||||
|
||||
$ cd egs
|
||||
$ mkdir -p foo/ASR
|
||||
$ cd foo/ASR
|
||||
$ touch prepare.sh
|
||||
$ chmod +x prepare.sh
|
||||
|
||||
If your dataset is very simple, please follow
|
||||
`egs/yesno/ASR/prepare.sh <https://github.com/k2-fsa/icefall/blob/master/egs/yesno/ASR/prepare.sh>`_
|
||||
to write your own ``prepare.sh``.
|
||||
Otherwise, please refer to
|
||||
`egs/librispeech/ASR/prepare.sh <https://github.com/k2-fsa/icefall/blob/master/egs/yesno/ASR/prepare.sh>`_
|
||||
to prepare your data.
|
||||
|
||||
|
||||
Training
|
||||
--------
|
||||
|
||||
Assume you have a fancy model, called ``bar`` for the ``foo`` recipe, you can
|
||||
organize your files in the following way:
|
||||
|
||||
.. code-block::
|
||||
|
||||
$ cd egs/foo/ASR
|
||||
$ mkdir bar
|
||||
$ cd bar
|
||||
$ touch README.md model.py train.py decode.py asr_datamodule.py pretrained.py
|
||||
|
||||
For instance , the ``yesno`` recipe has a ``tdnn`` model and its directory structure
|
||||
looks like the following:
|
||||
|
||||
.. code-block:: bash
|
||||
|
||||
egs/yesno/ASR/tdnn/
|
||||
|-- README.md
|
||||
|-- asr_datamodule.py
|
||||
|-- decode.py
|
||||
|-- model.py
|
||||
|-- pretrained.py
|
||||
`-- train.py
|
||||
|
||||
**File description**:
|
||||
|
||||
- ``README.md``
|
||||
|
||||
It contains information of this recipe, e.g., how to run it, what the WER is, etc.
|
||||
|
||||
- ``asr_datamodule.py``
|
||||
|
||||
It provides code to create PyTorch dataloaders with train/test/validation dataset.
|
||||
|
||||
- ``decode.py``
|
||||
|
||||
It takes as inputs the checkpoints saved during the training stage to decode the test
|
||||
dataset(s).
|
||||
|
||||
- ``model.py``
|
||||
|
||||
It contains the definition of your fancy neural network model.
|
||||
|
||||
- ``pretrained.py``
|
||||
|
||||
We can use this script to do inference with a pre-trained model.
|
||||
|
||||
- ``train.py``
|
||||
|
||||
It contains training code.
|
||||
|
||||
|
||||
.. HINT::
|
||||
|
||||
Please take a look at
|
||||
|
||||
- `egs/yesno/tdnn <https://github.com/k2-fsa/icefall/tree/master/egs/yesno/ASR/tdnn>`_
|
||||
- `egs/librispeech/tdnn_lstm_ctc <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/tdnn_lstm_ctc>`_
|
||||
- `egs/librispeech/conformer_ctc <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/conformer_ctc>`_
|
||||
|
||||
to get a feel what the resulting files look like.
|
||||
|
||||
.. NOTE::
|
||||
|
||||
Every model in a recipe is kept to be as self-contained as possible.
|
||||
We tolerate duplicate code among different recipes.
|
||||
|
||||
|
||||
The training stage should be invocable by:
|
||||
|
||||
.. code-block::
|
||||
|
||||
$ cd egs/foo/ASR
|
||||
$ ./bar/train.py
|
||||
$ ./bar/train.py --help
|
||||
|
||||
|
||||
Decoding
|
||||
--------
|
||||
|
||||
Please refer to
|
||||
|
||||
- `<https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/conformer_ctc/decode.py>`_
|
||||
|
||||
If your model is transformer/conformer based.
|
||||
|
||||
- `<https://github.com/k2-fsa/icefall/blob/master/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py>`_
|
||||
|
||||
If your model is TDNN/LSTM based, i.e., there is no attention decoder.
|
||||
|
||||
- `<https://github.com/k2-fsa/icefall/blob/master/egs/yesno/ASR/tdnn/decode.py>`_
|
||||
|
||||
If there is no LM rescoring.
|
||||
|
||||
The decoding stage should be invocable by:
|
||||
|
||||
.. code-block::
|
||||
|
||||
$ cd egs/foo/ASR
|
||||
$ ./bar/decode.py
|
||||
$ ./bar/decode.py --help
|
||||
|
||||
Pre-trained model
|
||||
-----------------
|
||||
|
||||
Please demonstrate how to use your model for inference in ``egs/foo/ASR/bar/pretrained.py``.
|
||||
If possible, please consider creating a Colab notebook to show that.
|
||||
BIN
docs/source/contributing/images/doc-contrib.png
Normal file
BIN
docs/source/contributing/images/doc-contrib.png
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|
After Width: | Height: | Size: 198 KiB |
BIN
docs/source/contributing/images/pre-commit-check-success.png
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BIN
docs/source/contributing/images/pre-commit-check-success.png
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|
After Width: | Height: | Size: 153 KiB |
BIN
docs/source/contributing/images/pre-commit-check.png
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BIN
docs/source/contributing/images/pre-commit-check.png
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|
After Width: | Height: | Size: 214 KiB |
22
docs/source/contributing/index.rst
Normal file
22
docs/source/contributing/index.rst
Normal file
@ -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
|
||||
@ -22,3 +22,4 @@ speech recognition recipes using `k2 <https://github.com/k2-fsa/k2>`_.
|
||||
|
||||
installation/index
|
||||
recipes/index
|
||||
contributing/index
|
||||
|
||||
@ -1 +1 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="122" height="20" role="img" aria-label="device: CPU | CUDA"><title>device: CPU | CUDA</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="122" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="45" height="20" fill="#555"/><rect x="45" width="77" height="20" fill="#fe7d37"/><rect width="122" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="235" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="350">device</text><text x="235" y="140" transform="scale(.1)" fill="#fff" textLength="350">device</text><text aria-hidden="true" x="825" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="670">CPU | CUDA</text><text x="825" y="140" transform="scale(.1)" fill="#fff" textLength="670">CPU | CUDA</text></g></svg>
|
||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="122" height="20" role="img" aria-label="device: CPU | CUDA"><title>device: CPU | CUDA</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="122" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="45" height="20" fill="#555"/><rect x="45" width="77" height="20" fill="#fe7d37"/><rect width="122" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="235" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="350">device</text><text x="235" y="140" transform="scale(.1)" fill="#fff" textLength="350">device</text><text aria-hidden="true" x="825" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="670">CPU | CUDA</text><text x="825" y="140" transform="scale(.1)" fill="#fff" textLength="670">CPU | CUDA</text></g></svg>
|
||||
|
||||
|
Before Width: | Height: | Size: 1.1 KiB After Width: | Height: | Size: 1.1 KiB |
1
docs/source/installation/images/k2-v-1.7.svg
Normal file
1
docs/source/installation/images/k2-v-1.7.svg
Normal file
@ -0,0 +1 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="80" height="20" role="img" aria-label="k2: >= v1.7"><title>k2: >= v1.7</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="80" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="23" height="20" fill="#555"/><rect x="23" width="57" height="20" fill="blueviolet"/><rect width="80" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="125" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="130">k2</text><text x="125" y="140" transform="scale(.1)" fill="#fff" textLength="130">k2</text><text aria-hidden="true" x="505" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="470">>= v1.7</text><text x="505" y="140" transform="scale(.1)" fill="#fff" textLength="470">>= v1.7</text></g></svg>
|
||||
|
After Width: | Height: | Size: 1.1 KiB |
@ -1 +1 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="114" height="20" role="img" aria-label="os: Linux | macOS"><title>os: Linux | macOS</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="114" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="23" height="20" fill="#555"/><rect x="23" width="91" height="20" fill="#ff69b4"/><rect width="114" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="125" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="130">os</text><text x="125" y="140" transform="scale(.1)" fill="#fff" textLength="130">os</text><text aria-hidden="true" x="675" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="810">Linux | macOS</text><text x="675" y="140" transform="scale(.1)" fill="#fff" textLength="810">Linux | macOS</text></g></svg>
|
||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="114" height="20" role="img" aria-label="os: Linux | macOS"><title>os: Linux | macOS</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="114" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="23" height="20" fill="#555"/><rect x="23" width="91" height="20" fill="#ff69b4"/><rect width="114" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="125" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="130">os</text><text x="125" y="140" transform="scale(.1)" fill="#fff" textLength="130">os</text><text aria-hidden="true" x="675" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="810">Linux | macOS</text><text x="675" y="140" transform="scale(.1)" fill="#fff" textLength="810">Linux | macOS</text></g></svg>
|
||||
|
||||
|
Before Width: | Height: | Size: 1.1 KiB After Width: | Height: | Size: 1.1 KiB |
@ -1 +1 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="170" height="20" role="img" aria-label="python: 3.6 | 3.7 | 3.8 | 3.9"><title>python: 3.6 | 3.7 | 3.8 | 3.9</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="170" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="49" height="20" fill="#555"/><rect x="49" width="121" height="20" fill="#007ec6"/><rect width="170" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="255" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="390">python</text><text x="255" y="140" transform="scale(.1)" fill="#fff" textLength="390">python</text><text aria-hidden="true" x="1085" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="1110">3.6 | 3.7 | 3.8 | 3.9</text><text x="1085" y="140" transform="scale(.1)" fill="#fff" textLength="1110">3.6 | 3.7 | 3.8 | 3.9</text></g></svg>
|
||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="170" height="20" role="img" aria-label="python: 3.6 | 3.7 | 3.8 | 3.9"><title>python: 3.6 | 3.7 | 3.8 | 3.9</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="170" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="49" height="20" fill="#555"/><rect x="49" width="121" height="20" fill="#007ec6"/><rect width="170" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="255" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="390">python</text><text x="255" y="140" transform="scale(.1)" fill="#fff" textLength="390">python</text><text aria-hidden="true" x="1085" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="1110">3.6 | 3.7 | 3.8 | 3.9</text><text x="1085" y="140" transform="scale(.1)" fill="#fff" textLength="1110">3.6 | 3.7 | 3.8 | 3.9</text></g></svg>
|
||||
|
||||
|
Before Width: | Height: | Size: 1.2 KiB After Width: | Height: | Size: 1.2 KiB |
@ -1 +1 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="286" height="20" role="img" aria-label="torch: 1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0"><title>torch: 1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="286" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="39" height="20" fill="#555"/><rect x="39" width="247" height="20" fill="#97ca00"/><rect width="286" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="205" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="290">torch</text><text x="205" y="140" transform="scale(.1)" fill="#fff" textLength="290">torch</text><text aria-hidden="true" x="1615" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="2370">1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0</text><text x="1615" y="140" transform="scale(.1)" fill="#fff" textLength="2370">1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0</text></g></svg>
|
||||
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="286" height="20" role="img" aria-label="torch: 1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0"><title>torch: 1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0</title><linearGradient id="s" x2="0" y2="100%"><stop offset="0" stop-color="#bbb" stop-opacity=".1"/><stop offset="1" stop-opacity=".1"/></linearGradient><clipPath id="r"><rect width="286" height="20" rx="3" fill="#fff"/></clipPath><g clip-path="url(#r)"><rect width="39" height="20" fill="#555"/><rect x="39" width="247" height="20" fill="#97ca00"/><rect width="286" height="20" fill="url(#s)"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="110"><text aria-hidden="true" x="205" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="290">torch</text><text x="205" y="140" transform="scale(.1)" fill="#fff" textLength="290">torch</text><text aria-hidden="true" x="1615" y="150" fill="#010101" fill-opacity=".3" transform="scale(.1)" textLength="2370">1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0</text><text x="1615" y="140" transform="scale(.1)" fill="#fff" textLength="2370">1.6.0 | 1.7.0 | 1.7.1 | 1.8.0 | 1.8.1 | 1.9.0</text></g></svg>
|
||||
|
||||
|
Before Width: | Height: | Size: 1.3 KiB After Width: | Height: | Size: 1.3 KiB |
@ -7,6 +7,7 @@ Installation
|
||||
- |device|
|
||||
- |python_versions|
|
||||
- |torch_versions|
|
||||
- |k2_versions|
|
||||
|
||||
.. |os| image:: ./images/os-Linux_macOS-ff69b4.svg
|
||||
:alt: Supported operating systems
|
||||
@ -20,7 +21,10 @@ Installation
|
||||
.. |torch_versions| image:: ./images/torch-1.6.0_1.7.0_1.7.1_1.8.0_1.8.1_1.9.0-green.svg
|
||||
:alt: Supported PyTorch versions
|
||||
|
||||
icefall depends on `k2 <https://github.com/k2-fsa/k2>`_ and
|
||||
.. |k2_versions| image:: ./images/k2-v-1.7.svg
|
||||
:alt: Supported k2 versions
|
||||
|
||||
``icefall`` depends on `k2 <https://github.com/k2-fsa/k2>`_ and
|
||||
`lhotse <https://github.com/lhotse-speech/lhotse>`_.
|
||||
|
||||
We recommend you to install ``k2`` first, as ``k2`` is bound to
|
||||
@ -32,12 +36,16 @@ installs its dependency PyTorch, which can be reused by ``lhotse``.
|
||||
--------------
|
||||
|
||||
Please refer to `<https://k2.readthedocs.io/en/latest/installation/index.html>`_
|
||||
to install `k2`.
|
||||
to install ``k2``.
|
||||
|
||||
.. CAUTION::
|
||||
|
||||
You need to install ``k2`` with a version at least **v1.7**.
|
||||
|
||||
.. HINT::
|
||||
|
||||
If you have already installed PyTorch and don't want to replace it,
|
||||
please install a version of k2 that is compiled against the version
|
||||
please install a version of ``k2`` that is compiled against the version
|
||||
of PyTorch you are using.
|
||||
|
||||
(2) Install lhotse
|
||||
@ -50,10 +58,15 @@ to install ``lhotse``.
|
||||
|
||||
Install ``lhotse`` also installs its dependency `torchaudio <https://github.com/pytorch/audio>`_.
|
||||
|
||||
.. CAUTION::
|
||||
|
||||
If you have installed ``torchaudio``, please consider uninstalling it before
|
||||
installing ``lhotse``. Otherwise, it may update your already installed PyTorch.
|
||||
|
||||
(3) Download icefall
|
||||
--------------------
|
||||
|
||||
icefall is a collection of Python scripts, so you don't need to install it
|
||||
``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``
|
||||
@ -202,22 +215,6 @@ The following shows an example about setting up the environment.
|
||||
valtree-3.1.0 lhotse-0.8.0.dev-2a1410b-clean lilcom-1.1.1 numpy-1.21.2 packaging-21.0 pycparser-2.20 pyparsing-2.4.7 pyyaml-5.4.1 sor
|
||||
tedcontainers-2.4.0 toolz-0.11.1 torchaudio-0.9.0 tqdm-4.62.1
|
||||
|
||||
**NOTE**: After installing ``lhotse``, you will encounter the following error:
|
||||
|
||||
.. code-block::
|
||||
|
||||
$ lhotse download --help
|
||||
-bash: /ceph-fj/fangjun/test-icefall/bin/lhotse: python: bad interpreter: No such file or directory
|
||||
|
||||
The correct fix is:
|
||||
|
||||
.. code-block::
|
||||
|
||||
echo '#!/usr/bin/env python3' | cat - $(which lhotse) > /tmp/lhotse-bin
|
||||
chmod +x /tmp/lhotse-bin
|
||||
mv /tmp/lhotse-bin $(which lhotse)
|
||||
|
||||
|
||||
(5) Download icefall
|
||||
~~~~~~~~~~~~~~~~~~~~
|
||||
|
||||
@ -383,7 +380,7 @@ Now let us run the training part:
|
||||
|
||||
.. CAUTION::
|
||||
|
||||
We use ``export CUDA_VISIBLE_DEVICES=""`` so that icefall uses CPU
|
||||
We use ``export CUDA_VISIBLE_DEVICES=""`` so that ``icefall`` uses CPU
|
||||
even if there are GPUs available.
|
||||
|
||||
The training log is given below:
|
||||
|
||||
@ -15,4 +15,3 @@ We may add recipes for other tasks as well in the future.
|
||||
yesno
|
||||
|
||||
librispeech
|
||||
|
||||
|
||||
@ -303,7 +303,7 @@ The commonly used options are:
|
||||
|
||||
- ``--lattice-score-scale``
|
||||
|
||||
It is used to scaled down lattice scores so that we can more unique
|
||||
It is used to scale down lattice scores so that there are more unique
|
||||
paths for rescoring.
|
||||
|
||||
- ``--max-duration``
|
||||
@ -314,7 +314,7 @@ The commonly used options are:
|
||||
Pre-trained Model
|
||||
-----------------
|
||||
|
||||
We have uploaded the pre-trained model to
|
||||
We have uploaded a pre-trained model to
|
||||
`<https://huggingface.co/pkufool/icefall_asr_librispeech_conformer_ctc>`_.
|
||||
|
||||
We describe how to use the pre-trained model to transcribe a sound file or
|
||||
@ -324,7 +324,7 @@ Install kaldifeat
|
||||
~~~~~~~~~~~~~~~~~
|
||||
|
||||
`kaldifeat <https://github.com/csukuangfj/kaldifeat>`_ is used to
|
||||
extract features for a single sound file or multiple soundfiles
|
||||
extract features for a single sound file or multiple sound files
|
||||
at the same time.
|
||||
|
||||
Please refer to `<https://github.com/csukuangfj/kaldifeat>`_ for installation.
|
||||
@ -397,7 +397,7 @@ After downloading, you will have the following files:
|
||||
|
||||
- ``data/lm/G_4_gram.pt``
|
||||
|
||||
It is a 4-gram LM, useful for LM rescoring.
|
||||
It is a 4-gram LM, used for n-gram LM rescoring.
|
||||
|
||||
- ``exp/pretrained.pt``
|
||||
|
||||
@ -556,7 +556,7 @@ Its output is:
|
||||
HLG decoding + LM rescoring + attention decoder rescoring
|
||||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
It uses an n-gram LM to rescore the decoding lattice, extracts
|
||||
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.
|
||||
|
||||
|
||||
@ -209,7 +209,7 @@ After downloading, you will have the following files:
|
||||
|-- 1221-135766-0001.flac
|
||||
|-- 1221-135766-0002.flac
|
||||
`-- trans.txt
|
||||
|
||||
|
||||
6 directories, 10 files
|
||||
|
||||
|
||||
@ -256,14 +256,14 @@ The output is:
|
||||
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
|
||||
|
||||
|
||||
@ -297,14 +297,14 @@ The decoding output is:
|
||||
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
|
||||
|
||||
|
||||
|
||||
@ -21,6 +21,32 @@ 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
|
||||
@ -43,4 +69,3 @@ We searched the lm_score_scale for best results, the scales that produced the WE
|
||||
|--|--|
|
||||
|test-clean|0.8|
|
||||
|test-other|0.9|
|
||||
|
||||
|
||||
@ -45,6 +45,7 @@ from icefall.utils import (
|
||||
get_texts,
|
||||
setup_logger,
|
||||
store_transcripts,
|
||||
str2bool,
|
||||
write_error_stats,
|
||||
)
|
||||
|
||||
@ -78,16 +79,16 @@ def get_parser():
|
||||
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.
|
||||
- (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.
|
||||
- (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.
|
||||
@ -116,6 +117,17 @@ def get_parser():
|
||||
""",
|
||||
)
|
||||
|
||||
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
|
||||
|
||||
|
||||
@ -541,6 +553,13 @@ def main():
|
||||
logging.info(f"averaging {filenames}")
|
||||
model.load_state_dict(average_checkpoints(filenames))
|
||||
|
||||
if params.export:
|
||||
logging.info(f"Export averaged model to {params.exp_dir}/pretrained.pt")
|
||||
torch.save(
|
||||
{"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt"
|
||||
)
|
||||
return
|
||||
|
||||
model.to(device)
|
||||
model.eval()
|
||||
num_param = sum([p.numel() for p in model.parameters()])
|
||||
|
||||
@ -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():
|
||||
|
||||
@ -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 = {
|
||||
|
||||
@ -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.data[LG.aux_labels.data >= first_word_disambig_id] = 0
|
||||
|
||||
LG = k2.remove_epsilon(LG)
|
||||
logging.info(f"LG shape after k2.remove_epsilon: {LG.shape}")
|
||||
|
||||
LG = k2.connect(LG)
|
||||
LG.aux_labels = k2.ragged.remove_values_eq(LG.aux_labels, 0)
|
||||
LG.aux_labels = LG.aux_labels.remove_values_eq(0)
|
||||
|
||||
logging.info("Arc sorting LG")
|
||||
LG = k2.arc_sort(LG)
|
||||
|
||||
@ -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).",
|
||||
)
|
||||
|
||||
@ -42,8 +42,8 @@ from icefall.utils import (
|
||||
get_texts,
|
||||
setup_logger,
|
||||
store_transcripts,
|
||||
write_error_stats,
|
||||
str2bool,
|
||||
write_error_stats,
|
||||
)
|
||||
|
||||
|
||||
@ -98,9 +98,11 @@ def get_params() -> AttributeDict:
|
||||
# - nbest
|
||||
# - nbest-rescoring
|
||||
# - whole-lattice-rescoring
|
||||
"method": "1best",
|
||||
"method": "whole-lattice-rescoring",
|
||||
# "method": "1best",
|
||||
# "method": "nbest",
|
||||
# num_paths is used when method is "nbest" and "nbest-rescoring"
|
||||
"num_paths": 30,
|
||||
"num_paths": 100,
|
||||
}
|
||||
)
|
||||
return params
|
||||
@ -424,6 +426,7 @@ def main():
|
||||
torch.save(
|
||||
{"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt"
|
||||
)
|
||||
return
|
||||
|
||||
model.to(device)
|
||||
model.eval()
|
||||
|
||||
0
egs/librispeech/ASR/tdnn_lstm_ctc/pretrained.py
Normal file → Executable file
0
egs/librispeech/ASR/tdnn_lstm_ctc/pretrained.py
Normal file → Executable file
@ -10,5 +10,5 @@ get the following WER:
|
||||
```
|
||||
|
||||
Please refer to
|
||||
<https://icefal1.readthedocs.io/en/latest/recipes/yesno.html>
|
||||
<https://icefall.readthedocs.io/en/latest/recipes/yesno.html>
|
||||
for detailed instructions.
|
||||
|
||||
@ -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.data[LG.aux_labels.data >= first_word_disambig_id] = 0
|
||||
|
||||
LG = k2.remove_epsilon(LG)
|
||||
logging.info(f"LG shape after k2.remove_epsilon: {LG.shape}")
|
||||
|
||||
LG = k2.connect(LG)
|
||||
LG.aux_labels = k2.ragged.remove_values_eq(LG.aux_labels, 0)
|
||||
LG.aux_labels = LG.aux_labels.remove_values_eq(0)
|
||||
|
||||
logging.info("Arc sorting LG")
|
||||
LG = k2.arc_sort(LG)
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
## How to run this recipe
|
||||
|
||||
You can find detailed instructions by visiting
|
||||
<https://icefal1.readthedocs.io/en/latest/recipes/yesno.html>
|
||||
<https://icefall.readthedocs.io/en/latest/recipes/yesno.html>
|
||||
|
||||
It describes how to run this recipe and how to use
|
||||
a pre-trained model with `./pretrained.py`.
|
||||
|
||||
@ -296,6 +296,7 @@ def main():
|
||||
torch.save(
|
||||
{"model": model.state_dict()}, f"{params.exp_dir}/pretrained.pt"
|
||||
)
|
||||
return
|
||||
|
||||
model.to(device)
|
||||
model.eval()
|
||||
|
||||
@ -84,8 +84,8 @@ def _intersect_device(
|
||||
for start, end in splits:
|
||||
indexes = torch.arange(start, end).to(b_to_a_map)
|
||||
|
||||
fsas = k2.index(b_fsas, indexes)
|
||||
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
|
||||
)
|
||||
@ -215,18 +215,16 @@ def nbest_decoding(
|
||||
scale=scale,
|
||||
)
|
||||
|
||||
# word_seq is a k2.RaggedInt sharing the same shape as `path`
|
||||
# word_seq is a k2.RaggedTensor sharing the same shape as `path`
|
||||
# but it contains word IDs. Note that it also contains 0s and -1s.
|
||||
# The last entry in each sublist is -1.
|
||||
word_seq = k2.index(lattice.aux_labels, path)
|
||||
# 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]
|
||||
if isinstance(lattice.aux_labels, torch.Tensor):
|
||||
word_seq = k2.ragged.index(lattice.aux_labels, path)
|
||||
else:
|
||||
word_seq = lattice.aux_labels.index(path, remove_axis=True)
|
||||
|
||||
# Remove 0 (epsilon) and -1 from word_seq
|
||||
word_seq = k2.ragged.remove_values_leq(word_seq, 0)
|
||||
word_seq = word_seq.remove_values_leq(0)
|
||||
|
||||
# Remove sequences with identical word sequences.
|
||||
#
|
||||
@ -234,12 +232,12 @@ def nbest_decoding(
|
||||
# `new2old` is a 1-D torch.Tensor mapping from the output path index
|
||||
# to the input path index.
|
||||
# new2old.numel() == unique_word_seqs.tot_size(1)
|
||||
unique_word_seq, _, new2old = k2.ragged.unique_sequences(
|
||||
word_seq, need_num_repeats=False, need_new2old_indexes=True
|
||||
unique_word_seq, _, new2old = word_seq.unique(
|
||||
need_num_repeats=False, need_new2old_indexes=True
|
||||
)
|
||||
# Note: unique_word_seq still has the same axes as word_seq
|
||||
|
||||
seq_to_path_shape = k2.ragged.get_layer(unique_word_seq.shape(), 0)
|
||||
seq_to_path_shape = unique_word_seq.shape.get_layer(0)
|
||||
|
||||
# path_to_seq_map is a 1-D torch.Tensor.
|
||||
# path_to_seq_map[i] is the seq to which the i-th path belongs
|
||||
@ -247,7 +245,7 @@ def nbest_decoding(
|
||||
|
||||
# Remove the seq axis.
|
||||
# Now unique_word_seq has only two axes [path][word]
|
||||
unique_word_seq = k2.ragged.remove_axis(unique_word_seq, 0)
|
||||
unique_word_seq = unique_word_seq.remove_axis(0)
|
||||
|
||||
# word_fsa is an FsaVec with axes [path][state][arc]
|
||||
word_fsa = k2.linear_fsa(unique_word_seq)
|
||||
@ -275,35 +273,35 @@ def nbest_decoding(
|
||||
use_double_scores=use_double_scores, log_semiring=False
|
||||
)
|
||||
|
||||
# RaggedFloat currently supports float32 only.
|
||||
# If Ragged<double> is wrapped, we can use k2.RaggedDouble here
|
||||
ragged_tot_scores = k2.RaggedFloat(
|
||||
seq_to_path_shape, tot_scores.to(torch.float32)
|
||||
)
|
||||
ragged_tot_scores = k2.RaggedTensor(seq_to_path_shape, tot_scores)
|
||||
|
||||
argmax_indexes = k2.ragged.argmax_per_sublist(ragged_tot_scores)
|
||||
argmax_indexes = ragged_tot_scores.argmax()
|
||||
|
||||
# Since we invoked `k2.ragged.unique_sequences`, which reorders
|
||||
# the index from `path`, we use `new2old` here to convert argmax_indexes
|
||||
# to the indexes into `path`.
|
||||
#
|
||||
# Use k2.index here since argmax_indexes' dtype is torch.int32
|
||||
best_path_indexes = k2.index(new2old, argmax_indexes)
|
||||
best_path_indexes = k2.index_select(new2old, argmax_indexes)
|
||||
|
||||
path_2axes = k2.ragged.remove_axis(path, 0)
|
||||
path_2axes = path.remove_axis(0)
|
||||
|
||||
# best_path is a k2.RaggedInt with 2 axes [path][arc_pos]
|
||||
best_path = k2.index(path_2axes, best_path_indexes)
|
||||
# best_path is a k2.RaggedTensor with 2 axes [path][arc_pos]
|
||||
best_path, _ = path_2axes.index(
|
||||
indexes=best_path_indexes, axis=0, need_value_indexes=False
|
||||
)
|
||||
|
||||
# labels is a k2.RaggedInt with 2 axes [path][token_id]
|
||||
# labels is a k2.RaggedTensor with 2 axes [path][token_id]
|
||||
# Note that it contains -1s.
|
||||
labels = k2.index(lattice.labels.contiguous(), best_path)
|
||||
labels = k2.ragged.index(lattice.labels.contiguous(), best_path)
|
||||
|
||||
labels = k2.ragged.remove_values_eq(labels, -1)
|
||||
labels = labels.remove_values_eq(-1)
|
||||
|
||||
# lattice.aux_labels is a k2.RaggedInt tensor with 2 axes, so
|
||||
# aux_labels is also a k2.RaggedInt with 2 axes
|
||||
aux_labels = k2.index(lattice.aux_labels, best_path.values())
|
||||
# lattice.aux_labels is a k2.RaggedTensor with 2 axes, so
|
||||
# aux_labels is also a k2.RaggedTensor with 2 axes
|
||||
aux_labels, _ = lattice.aux_labels.index(
|
||||
indexes=best_path.data, axis=0, need_value_indexes=False
|
||||
)
|
||||
|
||||
best_path_fsa = k2.linear_fsa(labels)
|
||||
best_path_fsa.aux_labels = aux_labels
|
||||
@ -426,33 +424,36 @@ def rescore_with_n_best_list(
|
||||
scale=scale,
|
||||
)
|
||||
|
||||
# word_seq is a k2.RaggedInt sharing the same shape as `path`
|
||||
# word_seq is a k2.RaggedTensor sharing the same shape as `path`
|
||||
# but it contains word IDs. Note that it also contains 0s and -1s.
|
||||
# The last entry in each sublist is -1.
|
||||
word_seq = k2.index(lattice.aux_labels, path)
|
||||
if isinstance(lattice.aux_labels, torch.Tensor):
|
||||
word_seq = k2.ragged.index(lattice.aux_labels, path)
|
||||
else:
|
||||
word_seq = lattice.aux_labels.index(path, remove_axis=True)
|
||||
|
||||
# Remove epsilons and -1 from word_seq
|
||||
word_seq = k2.ragged.remove_values_leq(word_seq, 0)
|
||||
word_seq = word_seq.remove_values_leq(0)
|
||||
|
||||
# Remove paths that has identical word sequences.
|
||||
#
|
||||
# unique_word_seq is still a k2.RaggedInt with 3 axes [seq][path][word]
|
||||
# unique_word_seq is still a k2.RaggedTensor with 3 axes [seq][path][word]
|
||||
# except that there are no repeated paths with the same word_seq
|
||||
# within a sequence.
|
||||
#
|
||||
# num_repeats is also a k2.RaggedInt with 2 axes containing the
|
||||
# num_repeats is also a k2.RaggedTensor with 2 axes containing the
|
||||
# multiplicities of each path.
|
||||
# num_repeats.num_elements() == unique_word_seqs.tot_size(1)
|
||||
# num_repeats.numel() == unique_word_seqs.tot_size(1)
|
||||
#
|
||||
# Since k2.ragged.unique_sequences will reorder paths within a seq,
|
||||
# `new2old` is a 1-D torch.Tensor mapping from the output path index
|
||||
# to the input path index.
|
||||
# new2old.numel() == unique_word_seqs.tot_size(1)
|
||||
unique_word_seq, num_repeats, new2old = k2.ragged.unique_sequences(
|
||||
word_seq, need_num_repeats=True, need_new2old_indexes=True
|
||||
unique_word_seq, num_repeats, new2old = word_seq.unique(
|
||||
need_num_repeats=True, need_new2old_indexes=True
|
||||
)
|
||||
|
||||
seq_to_path_shape = k2.ragged.get_layer(unique_word_seq.shape(), 0)
|
||||
seq_to_path_shape = unique_word_seq.shape.get_layer(0)
|
||||
|
||||
# path_to_seq_map is a 1-D torch.Tensor.
|
||||
# path_to_seq_map[i] is the seq to which the i-th path
|
||||
@ -461,7 +462,7 @@ def rescore_with_n_best_list(
|
||||
|
||||
# Remove the seq axis.
|
||||
# Now unique_word_seq has only two axes [path][word]
|
||||
unique_word_seq = k2.ragged.remove_axis(unique_word_seq, 0)
|
||||
unique_word_seq = unique_word_seq.remove_axis(0)
|
||||
|
||||
# word_fsa is an FsaVec with axes [path][state][arc]
|
||||
word_fsa = k2.linear_fsa(unique_word_seq)
|
||||
@ -485,39 +486,42 @@ def rescore_with_n_best_list(
|
||||
use_double_scores=True, log_semiring=False
|
||||
)
|
||||
|
||||
path_2axes = k2.ragged.remove_axis(path, 0)
|
||||
path_2axes = path.remove_axis(0)
|
||||
|
||||
ans = dict()
|
||||
for lm_scale in lm_scale_list:
|
||||
tot_scores = am_scores / lm_scale + lm_scores
|
||||
|
||||
# Remember that we used `k2.ragged.unique_sequences` to remove repeated
|
||||
# Remember that we used `k2.RaggedTensor.unique` to remove repeated
|
||||
# paths to avoid redundant computation in `k2.intersect_device`.
|
||||
# Now we use `num_repeats` to correct the scores for each path.
|
||||
#
|
||||
# NOTE(fangjun): It is commented out as it leads to a worse WER
|
||||
# tot_scores = tot_scores * num_repeats.values()
|
||||
|
||||
ragged_tot_scores = k2.RaggedFloat(
|
||||
seq_to_path_shape, tot_scores.to(torch.float32)
|
||||
)
|
||||
argmax_indexes = k2.ragged.argmax_per_sublist(ragged_tot_scores)
|
||||
ragged_tot_scores = k2.RaggedTensor(seq_to_path_shape, tot_scores)
|
||||
argmax_indexes = ragged_tot_scores.argmax()
|
||||
|
||||
# Use k2.index here since argmax_indexes' dtype is torch.int32
|
||||
best_path_indexes = k2.index(new2old, argmax_indexes)
|
||||
best_path_indexes = k2.index_select(new2old, argmax_indexes)
|
||||
|
||||
# best_path is a k2.RaggedInt with 2 axes [path][arc_pos]
|
||||
best_path = k2.index(path_2axes, best_path_indexes)
|
||||
best_path, _ = path_2axes.index(
|
||||
indexes=best_path_indexes, axis=0, need_value_indexes=False
|
||||
)
|
||||
|
||||
# labels is a k2.RaggedInt with 2 axes [path][phone_id]
|
||||
# labels is a k2.RaggedTensor with 2 axes [path][phone_id]
|
||||
# Note that it contains -1s.
|
||||
labels = k2.index(lattice.labels.contiguous(), best_path)
|
||||
labels = k2.ragged.index(lattice.labels.contiguous(), best_path)
|
||||
|
||||
labels = k2.ragged.remove_values_eq(labels, -1)
|
||||
labels = labels.remove_values_eq(-1)
|
||||
|
||||
# lattice.aux_labels is a k2.RaggedInt tensor with 2 axes, so
|
||||
# aux_labels is also a k2.RaggedInt with 2 axes
|
||||
aux_labels = k2.index(lattice.aux_labels, best_path.values())
|
||||
# lattice.aux_labels is a k2.RaggedTensor tensor with 2 axes, so
|
||||
# aux_labels is also a k2.RaggedTensor with 2 axes
|
||||
|
||||
aux_labels, _ = lattice.aux_labels.index(
|
||||
indexes=best_path.data, axis=0, need_value_indexes=False
|
||||
)
|
||||
|
||||
best_path_fsa = k2.linear_fsa(labels)
|
||||
best_path_fsa.aux_labels = aux_labels
|
||||
@ -659,12 +663,16 @@ def nbest_oracle(
|
||||
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
|
||||
if isinstance(lattice.aux_labels, torch.Tensor):
|
||||
word_seq = k2.ragged.index(lattice.aux_labels, path)
|
||||
else:
|
||||
word_seq = lattice.aux_labels.index(path, remove_axis=True)
|
||||
|
||||
word_seq = word_seq.remove_values_leq(0)
|
||||
unique_word_seq, _, _ = word_seq.unique(
|
||||
need_num_repeats=False, need_new2old_indexes=False
|
||||
)
|
||||
unique_word_ids = k2.ragged.to_list(unique_word_seq)
|
||||
unique_word_ids = unique_word_seq.tolist()
|
||||
assert len(unique_word_ids) == len(ref_texts)
|
||||
# unique_word_ids[i] contains all hypotheses of the i-th utterance
|
||||
|
||||
@ -743,33 +751,36 @@ def rescore_with_attention_decoder(
|
||||
scale=scale,
|
||||
)
|
||||
|
||||
# word_seq is a k2.RaggedInt sharing the same shape as `path`
|
||||
# word_seq is a k2.RaggedTensor sharing the same shape as `path`
|
||||
# but it contains word IDs. Note that it also contains 0s and -1s.
|
||||
# The last entry in each sublist is -1.
|
||||
word_seq = k2.index(lattice.aux_labels, path)
|
||||
if isinstance(lattice.aux_labels, torch.Tensor):
|
||||
word_seq = k2.ragged.index(lattice.aux_labels, path)
|
||||
else:
|
||||
word_seq = lattice.aux_labels.index(path, remove_axis=True)
|
||||
|
||||
# Remove epsilons and -1 from word_seq
|
||||
word_seq = k2.ragged.remove_values_leq(word_seq, 0)
|
||||
word_seq = word_seq.remove_values_leq(0)
|
||||
|
||||
# Remove paths that has identical word sequences.
|
||||
#
|
||||
# unique_word_seq is still a k2.RaggedInt with 3 axes [seq][path][word]
|
||||
# unique_word_seq is still a k2.RaggedTensor with 3 axes [seq][path][word]
|
||||
# except that there are no repeated paths with the same word_seq
|
||||
# within a sequence.
|
||||
#
|
||||
# num_repeats is also a k2.RaggedInt with 2 axes containing the
|
||||
# num_repeats is also a k2.RaggedTensor with 2 axes containing the
|
||||
# multiplicities of each path.
|
||||
# num_repeats.num_elements() == unique_word_seqs.tot_size(1)
|
||||
# num_repeats.numel() == unique_word_seqs.tot_size(1)
|
||||
#
|
||||
# Since k2.ragged.unique_sequences will reorder paths within a seq,
|
||||
# `new2old` is a 1-D torch.Tensor mapping from the output path index
|
||||
# to the input path index.
|
||||
# new2old.numel() == unique_word_seq.tot_size(1)
|
||||
unique_word_seq, num_repeats, new2old = k2.ragged.unique_sequences(
|
||||
word_seq, need_num_repeats=True, need_new2old_indexes=True
|
||||
unique_word_seq, num_repeats, new2old = word_seq.unique(
|
||||
need_num_repeats=True, need_new2old_indexes=True
|
||||
)
|
||||
|
||||
seq_to_path_shape = k2.ragged.get_layer(unique_word_seq.shape(), 0)
|
||||
seq_to_path_shape = unique_word_seq.shape.get_layer(0)
|
||||
|
||||
# path_to_seq_map is a 1-D torch.Tensor.
|
||||
# path_to_seq_map[i] is the seq to which the i-th path
|
||||
@ -778,7 +789,7 @@ def rescore_with_attention_decoder(
|
||||
|
||||
# Remove the seq axis.
|
||||
# Now unique_word_seq has only two axes [path][word]
|
||||
unique_word_seq = k2.ragged.remove_axis(unique_word_seq, 0)
|
||||
unique_word_seq = unique_word_seq.remove_axis(0)
|
||||
|
||||
# word_fsa is an FsaVec with axes [path][state][arc]
|
||||
word_fsa = k2.linear_fsa(unique_word_seq)
|
||||
@ -796,20 +807,23 @@ def rescore_with_attention_decoder(
|
||||
|
||||
# CAUTION: The "tokens" attribute is set in the file
|
||||
# local/compile_hlg.py
|
||||
token_seq = k2.index(lattice.tokens, path)
|
||||
if isinstance(lattice.tokens, torch.Tensor):
|
||||
token_seq = k2.ragged.index(lattice.tokens, path)
|
||||
else:
|
||||
token_seq = lattice.tokens.index(path, remove_axis=True)
|
||||
|
||||
# Remove epsilons and -1 from token_seq
|
||||
token_seq = k2.ragged.remove_values_leq(token_seq, 0)
|
||||
token_seq = token_seq.remove_values_leq(0)
|
||||
|
||||
# Remove the seq axis.
|
||||
token_seq = k2.ragged.remove_axis(token_seq, 0)
|
||||
token_seq = token_seq.remove_axis(0)
|
||||
|
||||
token_seq, _ = k2.ragged.index(
|
||||
token_seq, indexes=new2old, axis=0, need_value_indexes=False
|
||||
token_seq, _ = token_seq.index(
|
||||
indexes=new2old, axis=0, need_value_indexes=False
|
||||
)
|
||||
|
||||
# Now word in unique_word_seq has its corresponding token IDs.
|
||||
token_ids = k2.ragged.to_list(token_seq)
|
||||
token_ids = token_seq.tolist()
|
||||
|
||||
num_word_seqs = new2old.numel()
|
||||
|
||||
@ -849,7 +863,7 @@ def rescore_with_attention_decoder(
|
||||
else:
|
||||
attention_scale_list = [attention_scale]
|
||||
|
||||
path_2axes = k2.ragged.remove_axis(path, 0)
|
||||
path_2axes = path.remove_axis(0)
|
||||
|
||||
ans = dict()
|
||||
for n_scale in ngram_lm_scale_list:
|
||||
@ -859,23 +873,28 @@ def rescore_with_attention_decoder(
|
||||
+ n_scale * ngram_lm_scores
|
||||
+ a_scale * attention_scores
|
||||
)
|
||||
ragged_tot_scores = k2.RaggedFloat(seq_to_path_shape, tot_scores)
|
||||
argmax_indexes = k2.ragged.argmax_per_sublist(ragged_tot_scores)
|
||||
ragged_tot_scores = k2.RaggedTensor(seq_to_path_shape, tot_scores)
|
||||
argmax_indexes = ragged_tot_scores.argmax()
|
||||
|
||||
best_path_indexes = k2.index(new2old, argmax_indexes)
|
||||
best_path_indexes = k2.index_select(new2old, argmax_indexes)
|
||||
|
||||
# best_path is a k2.RaggedInt with 2 axes [path][arc_pos]
|
||||
best_path = k2.index(path_2axes, best_path_indexes)
|
||||
best_path, _ = path_2axes.index(
|
||||
indexes=best_path_indexes, axis=0, need_value_indexes=False
|
||||
)
|
||||
|
||||
# labels is a k2.RaggedInt with 2 axes [path][token_id]
|
||||
# labels is a k2.RaggedTensor with 2 axes [path][token_id]
|
||||
# Note that it contains -1s.
|
||||
labels = k2.index(lattice.labels.contiguous(), best_path)
|
||||
labels = k2.ragged.index(lattice.labels.contiguous(), best_path)
|
||||
|
||||
labels = k2.ragged.remove_values_eq(labels, -1)
|
||||
labels = labels.remove_values_eq(-1)
|
||||
|
||||
# lattice.aux_labels is a k2.RaggedInt tensor with 2 axes, so
|
||||
# aux_labels is also a k2.RaggedInt with 2 axes
|
||||
aux_labels = k2.index(lattice.aux_labels, best_path.values())
|
||||
if isinstance(lattice.aux_labels, torch.Tensor):
|
||||
aux_labels = k2.index_select(lattice.aux_labels, best_path.data)
|
||||
else:
|
||||
aux_labels, _ = lattice.aux_labels.index(
|
||||
indexes=best_path.data, axis=0, need_value_indexes=False
|
||||
)
|
||||
|
||||
best_path_fsa = k2.linear_fsa(labels)
|
||||
best_path_fsa.aux_labels = aux_labels
|
||||
|
||||
@ -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
|
||||
|
||||
@ -26,7 +26,6 @@ from pathlib import Path
|
||||
from typing import Dict, Iterable, List, TextIO, Tuple, Union
|
||||
|
||||
import k2
|
||||
import k2.ragged as k2r
|
||||
import kaldialign
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
@ -199,26 +198,25 @@ def get_texts(best_paths: k2.Fsa) -> List[List[int]]:
|
||||
Returns a list of lists of int, containing the label sequences we
|
||||
decoded.
|
||||
"""
|
||||
if isinstance(best_paths.aux_labels, k2.RaggedInt):
|
||||
if isinstance(best_paths.aux_labels, k2.RaggedTensor):
|
||||
# remove 0's and -1's.
|
||||
aux_labels = k2r.remove_values_leq(best_paths.aux_labels, 0)
|
||||
aux_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.data)
|
||||
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
|
||||
return aux_labels.tolist()
|
||||
|
||||
|
||||
def store_transcripts(
|
||||
|
||||
@ -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():
|
||||
|
||||
@ -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]]
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user