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fix typos (#1336)

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* fix typo

* fix typo

* Update pruned_transducer_stateless.rst
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hairyputtar 2023-10-24 13:17:25 +05:30 committed by GitHub
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2
docs/source/contributing/how-to-create-a-recipe.rst
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@ -3,7 +3,7 @@ How to create a recipe
.. HINT::
Please read :ref:`follow the code style` to adjust your code sytle.
Please read :ref:`follow the code style` to adjust your code style.
.. CAUTION::

2
docs/source/recipes/Streaming-ASR/introduction.rst
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@ -32,7 +32,7 @@ In icefall, we implement the streaming conformer the way just like what `WeNet <
.. HINT::
If you want to modify a non-streaming conformer recipe to support both streaming and non-streaming, please refer
to `this pull request <https://github.com/k2-fsa/icefall/pull/454>`_. After adding the code needed by streaming training,
you have to re-train it with the extra arguments metioned in the docs above to get a streaming model.
you have to re-train it with the extra arguments mentioned in the docs above to get a streaming model.
Streaming Emformer

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docs/source/recipes/Streaming-ASR/librispeech/pruned_transducer_stateless.rst
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@ -584,7 +584,7 @@ The following shows two examples (for the two types of checkpoints):
- ``beam_search`` : It implements Algorithm 1 in https://arxiv.org/pdf/1211.3711.pdf and
`espnet/nets/beam_search_transducer.py <https://github.com/espnet/espnet/blob/master/espnet/nets/beam_search_transducer.py#L247>`_
is used as a reference. Basicly, it keeps topk states for each frame, and expands the kept states with their own contexts to
is used as a reference. Basically, it keeps topk states for each frame, and expands the kept states with their own contexts to
next frame.
- ``modified_beam_search`` : It implements the same algorithm as ``beam_search`` above, but it
@ -648,7 +648,7 @@ command to extract ``model.state_dict()``.
.. caution::
``--streaming-model`` and ``--causal-convolution`` require to be True to export
a streaming mdoel.
a streaming model.
It will generate a file ``./pruned_transducer_stateless4/exp/pretrained.pt``.
@ -697,7 +697,7 @@ Export model using ``torch.jit.script()``
.. caution::
``--streaming-model`` and ``--causal-convolution`` require to be True to export
a streaming mdoel.
a streaming model.
It will generate a file ``cpu_jit.pt`` in the given ``exp_dir``. You can later
load it by ``torch.jit.load("cpu_jit.pt")``.