deploy: 3fb99400cf2c691f5c666fecd1415340820364a6

_sources/contributing/how-to-create-a-recipe.rst.txt

@@ -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::
 

_sources/recipes/Streaming-ASR/introduction.rst.txt

@@ -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

_sources/recipes/Streaming-ASR/librispeech/pruned_transducer_stateless.rst.txt

@@ -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")``.

contributing/how-to-create-a-recipe.html

@@ -101,7 +101,7 @@
 <h1>How to create a recipe<a class="headerlink" href="#how-to-create-a-recipe" title="Permalink to this heading"></a></h1>
 <div class="admonition hint">
 <p class="admonition-title">Hint</p>
-<p>Please read <a class="reference internal" href="code-style.html#follow-the-code-style"><span class="std std-ref">Follow the code style</span></a> to adjust your code sytle.</p>
+<p>Please read <a class="reference internal" href="code-style.html#follow-the-code-style"><span class="std std-ref">Follow the code style</span></a> to adjust your code style.</p>
 </div>
 <div class="admonition caution">
 <p class="admonition-title">Caution</p>

recipes/Streaming-ASR/introduction.html

@@ -133,7 +133,7 @@ See <a class="reference internal" href="librispeech/pruned_transducer_stateless.
 <p class="admonition-title">Hint</p>
 <p>If you want to modify a non-streaming conformer recipe to support both streaming and non-streaming, please refer
 to <a class="reference external" href="https://github.com/k2-fsa/icefall/pull/454">this pull request</a>.  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.</p>
+you have to re-train it with the extra arguments mentioned in the docs above to get a streaming model.</p>
 </div>
 </section>
 <section id="streaming-emformer">

recipes/Streaming-ASR/librispeech/pruned_transducer_stateless.html

@@ -652,7 +652,7 @@ can try decoding with <code class="docutils literal notranslate"><span class="pr
 of each frame as the decoding result.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">beam_search</span></code> :  It implements Algorithm 1 in <a class="reference external" href="https://arxiv.org/pdf/1211.3711.pdf">https://arxiv.org/pdf/1211.3711.pdf</a> and
 <a class="reference external" href="https://github.com/espnet/espnet/blob/master/espnet/nets/beam_search_transducer.py#L247">espnet/nets/beam_search_transducer.py</a>
-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.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">modified_beam_search</span></code> : It implements the same algorithm as <code class="docutils literal notranslate"><span class="pre">beam_search</span></code> above, but it
 runs in batch mode with <code class="docutils literal notranslate"><span class="pre">--max-sym-per-frame=1</span></code> being hardcoded.</p></li>
@@ -726,7 +726,7 @@ command to extract <code class="docutils literal notranslate"><span class="pre">
 <div class="admonition caution">
 <p class="admonition-title">Caution</p>
 <p><code class="docutils literal notranslate"><span class="pre">--streaming-model</span></code> and <code class="docutils literal notranslate"><span class="pre">--causal-convolution</span></code> require to be True to export
-a streaming mdoel.</p>
+a streaming model.</p>
 </div>
 <p>It will generate a file <code class="docutils literal notranslate"><span class="pre">./pruned_transducer_stateless4/exp/pretrained.pt</span></code>.</p>
 <div class="admonition hint">
@@ -768,7 +768,7 @@ can run:</p>
 <div class="admonition caution">
 <p class="admonition-title">Caution</p>
 <p><code class="docutils literal notranslate"><span class="pre">--streaming-model</span></code> and <code class="docutils literal notranslate"><span class="pre">--causal-convolution</span></code> require to be True to export
-a streaming mdoel.</p>
+a streaming model.</p>
 </div>
 <p>It will generate a file <code class="docutils literal notranslate"><span class="pre">cpu_jit.pt</span></code> in the given <code class="docutils literal notranslate"><span class="pre">exp_dir</span></code>. You can later
 load it by <code class="docutils literal notranslate"><span class="pre">torch.jit.load(&quot;cpu_jit.pt&quot;)</span></code>.</p>