yesno ===== This page shows you how to run the ``yesno`` recipe. .. 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 time takes less than 30 **seconds** and you will get the following WER:: [test_set] %WER 0.42% [1 / 240, 0 ins, 1 del, 0 sub ] Data preparation ---------------- .. code-block:: $ 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 $ ./tdnn/train.py By default, it will run ``15`` epochs. Training logs and checkpoints are saved in ``tdnn/exp``. To see the training options, you can use: .. code-block:: bash $ ./tdnn/train.py --help 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 $ ./tdnn/decode.py You will see the WER in the output log. Decoding results are saved in ``tdnn/exp``. 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 Use a pre-trained model ----------------------- TODO