mirror of
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188 lines
4.5 KiB
ReStructuredText
188 lines
4.5 KiB
ReStructuredText
yesno
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=====
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This page shows you how to run the ``yesno`` recipe.
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.. HINT::
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We assume you have read the page :ref:`install icefall` and have setup
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the environment for ``icefall``.
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.. HINT::
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You **don't** need a **GPU** to run this recipe. It can be run on a **CPU**.
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The training time takes less than 30 **seconds** and you will get
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the following WER::
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[test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ]
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Data preparation
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----------------
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.. code-block:: bash
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$ cd egs/yesno/ASR
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$ ./prepare.sh
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The script ``./prepare.sh`` handles the data preparation for you, automagically.
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All you need to do is to run it.
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The data preparation contains several stages, you can use the following two
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options:
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- ``--stage``
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- ``--stop-stage``
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to control which stage(s) should be run. By default, all stages are executed.
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For example,
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.. code-block:: bash
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$ cd egs/yesno/ASR
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$ ./prepare.sh --stage 0 --stop-stage 0
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means to run only stage 0.
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To run stage 2 to stage 5, use:
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.. code-block:: bash
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$ ./prepare.sh --stage 2 --stop-stage 5
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Training
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--------
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We provide only a TDNN model, contained in
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the `tdnn <https://github.com/k2-fsa/icefall/tree/master/egs/yesno/ASR/tdnn>`_
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folder, for ``yesno``.
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The command to run the training part is:
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.. code-block:: bash
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$ cd egs/yesno/ASR
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$ export CUDA_VISIBLE_DEVICES=""
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$ ./tdnn/train.py
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By default, it will run ``15`` epochs. Training logs and checkpoints are saved
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in ``tdnn/exp``.
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In ``tdnn/exp``, you will find the following files:
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- ``epoch-0.pt``, ``epoch-1.pt``, ...
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These are checkpoint files, containing model parameters and optimizer ``state_dict``.
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To resume training from some checkpoint, say ``epoch-10.pt``, you can use:
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.. code-block:: bash
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$ ./tdnn/train.py --start-epoch 11
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- ``tensorboard/``
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This folder contains TensorBoard logs. Training loss, validation loss, learning
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rate, etc, are recorded in these logs. You can visualize them by:
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.. code-block:: bash
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$ cd tdnn/exp/tensorboard
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$ tensorboard dev upload --logdir . --description "TDNN training for yesno with icefall"
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It will print something like below:
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.. code-block::
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TensorFlow installation not found - running with reduced feature set.
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Upload started and will continue reading any new data as it's added to the logdir.
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To stop uploading, press Ctrl-C.
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New experiment created. View your TensorBoard at: https://tensorboard.dev/experiment/yKUbhb5wRmOSXYkId1z9eg/
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[2021-08-23T23:49:41] Started scanning logdir.
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[2021-08-23T23:49:42] Total uploaded: 135 scalars, 0 tensors, 0 binary objects
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Listening for new data in logdir...
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Note there is a URL in the above output, click it and you will see
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the following screenshot:
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.. figure:: images/yesno-tdnn-tensorboard-log.png
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:width: 600
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:alt: TensorBoard screenshot
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:align: center
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:target: https://tensorboard.dev/experiment/yKUbhb5wRmOSXYkId1z9eg/
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TensorBoard screenshot.
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- ``log/log-train-xxxx``
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It is the detailed training log in text format, same as the one
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you saw printed to the console during training.
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To see available training options, you can use:
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.. code-block:: bash
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$ ./tdnn/train.py --help
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.. NOTE::
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By default, ``./tdnn/train.py`` uses GPU 0 for training if GPUs are available.
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If you have two GPUs, say, GPU 0 and GPU 1, and you want to use GPU 1 for
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training, you can run:
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.. code-block:: bash
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$ export CUDA_VISIBLE_DEVICES="1"
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$ ./tdnn/train.py
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Since the ``yesno`` dataset is very small, containing only 30 sound files
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for training, and the model in use is also very small, we use:
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.. code-block:: bash
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$ export CUDA_VISIBLE_DEVICES=""
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so that ``./tdnn/train.py`` uses CPU during training.
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If you don't have GPUs, then you don't need to
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run ``export CUDA_VISIBLE_DEVICES=""``.
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Decoding
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--------
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The decoding part uses checkpoints saved by the training part, so you have
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to run the training part first.
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The command for decoding is:
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.. code-block:: bash
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$ export CUDA_VISIBLE_DEVICES=""
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$ ./tdnn/decode.py
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You will see the WER in the output log.
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Decoded results are saved in ``tdnn/exp``.
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Colab notebook
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--------------
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We do provide a colab notebook for this recipe.
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|yesno colab notebook|
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.. |yesno colab notebook| image:: https://colab.research.google.com/assets/colab-badge.svg
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:target: https://colab.research.google.com/drive/1tIjjzaJc3IvGyKiMCDWO-TSnBgkcuN3B?usp=sharing
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Use a pre-trained model
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-----------------------
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TODO
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