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fix more typos (#1340)
* fix more typos * fix typo * fix typo * fix typo
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@ -67,7 +67,7 @@ To run stage 2 to stage 5, use:
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.. HINT::
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.. HINT::
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A 3-gram language model will be downloaded from huggingface, we assume you have
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A 3-gram language model will be downloaded from huggingface, we assume you have
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intalled and initialized ``git-lfs``. If not, you could install ``git-lfs`` by
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installed and initialized ``git-lfs``. If not, you could install ``git-lfs`` by
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.. code-block:: bash
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.. code-block:: bash
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@ -67,7 +67,7 @@ To run stage 2 to stage 5, use:
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.. HINT::
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.. HINT::
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A 3-gram language model will be downloaded from huggingface, we assume you have
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A 3-gram language model will be downloaded from huggingface, we assume you have
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intalled and initialized ``git-lfs``. If not, you could install ``git-lfs`` by
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installed and initialized ``git-lfs``. If not, you could install ``git-lfs`` by
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.. code-block:: bash
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.. code-block:: bash
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@ -418,7 +418,7 @@ The following shows two examples (for two types of checkpoints):
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- ``beam_search`` : It implements Algorithm 1 in https://arxiv.org/pdf/1211.3711.pdf and
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- ``beam_search`` : It implements Algorithm 1 in https://arxiv.org/pdf/1211.3711.pdf and
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`espnet/nets/beam_search_transducer.py <https://github.com/espnet/espnet/blob/master/espnet/nets/beam_search_transducer.py#L247>`_
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`espnet/nets/beam_search_transducer.py <https://github.com/espnet/espnet/blob/master/espnet/nets/beam_search_transducer.py#L247>`_
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is used as a reference. Basicly, it keeps topk states for each frame, and expands the kept states with their own contexts to
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is used as a reference. Basically, it keeps topk states for each frame, and expands the kept states with their own contexts to
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next frame.
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next frame.
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- ``modified_beam_search`` : It implements the same algorithm as ``beam_search`` above, but it
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- ``modified_beam_search`` : It implements the same algorithm as ``beam_search`` above, but it
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@ -1,6 +1,6 @@
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.. _train_nnlm:
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.. _train_nnlm:
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Train an RNN langugage model
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Train an RNN language model
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======================================
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======================================
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If you have enough text data, you can train a neural network language model (NNLM) to improve
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If you have enough text data, you can train a neural network language model (NNLM) to improve
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