deploy: e2fcb42f5f176d9e39eb38506ab99d0a3adaf202

_sources/recipes/RNN-LM/librispeech/lm-training.rst.txt

@@ -77,7 +77,7 @@ The next step is to train the RNNLM model. The training command is as follows:
         --use-fp16 0 \
         --tie-weights 1 \
         --embedding-dim 2048 \
-        --hidden_dim 2048 \
+        --hidden-dim 2048 \
         --num-layers 3 \
         --batch-size 300 \
         --lm-data rnn_lm/data/lang_bpe_500/sorted_lm_data.pt \
@@ -93,12 +93,3 @@ The next step is to train the RNNLM model. The training command is as follows:
 .. note::
 
     The training of RNNLM can take a long time (usually a couple of days).
-
-
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-
-
-
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-

recipes/RNN-LM/librispeech/lm-training.html

@@ -162,7 +162,7 @@ $<span class="w"> </span>./rnn_lm/train.py<span class="w"> </span><span class="s
 <span class="w">    </span>--use-fp16<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="se">\</span>
 <span class="w">    </span>--tie-weights<span class="w"> </span><span class="m">1</span><span class="w"> </span><span class="se">\</span>
 <span class="w">    </span>--embedding-dim<span class="w"> </span><span class="m">2048</span><span class="w"> </span><span class="se">\</span>
-<span class="w">    </span>--hidden_dim<span class="w"> </span><span class="m">2048</span><span class="w"> </span><span class="se">\</span>
+<span class="w">    </span>--hidden-dim<span class="w"> </span><span class="m">2048</span><span class="w"> </span><span class="se">\</span>
 <span class="w">    </span>--num-layers<span class="w"> </span><span class="m">3</span><span class="w"> </span><span class="se">\</span>
 <span class="w">    </span>--batch-size<span class="w"> </span><span class="m">300</span><span class="w"> </span><span class="se">\</span>
 <span class="w">    </span>--lm-data<span class="w"> </span>rnn_lm/data/lang_bpe_500/sorted_lm_data.pt<span class="w"> </span><span class="se">\</span>