deploy: eef47adee9aa765f41cd63a8d57049b02849f3ad

_sources/decoding-with-langugage-models/LODR.rst.txt

@@ -56,7 +56,7 @@ during decoding for transducer model:
     \lambda_1 \log p_{\text{Target LM}}\left(y_u|\mathit{x},y_{1:u-1}\right) -
     \lambda_2 \log p_{\text{bi-gram}}\left(y_u|\mathit{x},y_{1:u-1}\right)
 
-In LODR, an additional bi-gram LM estimated on the source domain (e.g training corpus) is required. Comared to DR,
+In LODR, an additional bi-gram LM estimated on the source domain (e.g training corpus) is required. Compared to DR,
 the only difference lies in the choice of source domain LM. According to the original `paper <https://arxiv.org/abs/2203.16776>`_,
 LODR achieves similar performance compared DR in both intra-domain and cross-domain settings.
 As a bi-gram is much faster to evaluate, LODR is usually much faster.

_sources/model-export/export-ncnn-conv-emformer.rst.txt

@@ -125,7 +125,7 @@ Python code. We have also set up ``PATH`` so that you can use
 .. caution::
 
   Please don't use `<https://github.com/tencent/ncnn>`_.
-  We have made some modifications to the offical `ncnn`_.
+  We have made some modifications to the official `ncnn`_.
 
   We will synchronize `<https://github.com/csukuangfj/ncnn>`_ periodically
   with the official one.

decoding-with-langugage-models/LODR.html

@@ -136,7 +136,7 @@ during decoding for transducer model:</p>
 \log p_{rnnt}\left(y_u|\mathit{x},y_{1:u-1}\right) +
 \lambda_1 \log p_{\text{Target LM}}\left(y_u|\mathit{x},y_{1:u-1}\right) -
 \lambda_2 \log p_{\text{bi-gram}}\left(y_u|\mathit{x},y_{1:u-1}\right)\]</div>
-<p>In LODR, an additional bi-gram LM estimated on the source domain (e.g training corpus) is required. Comared to DR,
+<p>In LODR, an additional bi-gram LM estimated on the source domain (e.g training corpus) is required. Compared to DR,
 the only difference lies in the choice of source domain LM. According to the original <a class="reference external" href="https://arxiv.org/abs/2203.16776">paper</a>,
 LODR achieves similar performance compared DR in both intra-domain and cross-domain settings.
 As a bi-gram is much faster to evaluate, LODR is usually much faster.</p>

model-export/export-ncnn-conv-emformer.html

@@ -226,7 +226,7 @@ Python code. We have also set up <code class="docutils literal notranslate"><spa
 <div class="admonition caution">
 <p class="admonition-title">Caution</p>
 <p>Please don’t use <a class="reference external" href="https://github.com/tencent/ncnn">https://github.com/tencent/ncnn</a>.
-We have made some modifications to the offical <a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a>.</p>
+We have made some modifications to the official <a class="reference external" href="https://github.com/tencent/ncnn">ncnn</a>.</p>
 <p>We will synchronize <a class="reference external" href="https://github.com/csukuangfj/ncnn">https://github.com/csukuangfj/ncnn</a> periodically
 with the official one.</p>
 </div>