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marcoyang1998 2023-08-29 08:46:37 +00:00
parent aba0aa17b9
commit 5633b5d16e
7 changed files with 25 additions and 7 deletions
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5
_sources/decoding-with-langugage-models/LODR.rst.txt
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@ -71,9 +71,12 @@ As the initial step, let's download the pre-trained model.
.. code-block:: bash
$ GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
$ pushd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$ cd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$ git lfs pull --include "pretrained.pt"
$ ln -s pretrained.pt epoch-99.pt # create a symbolic link so that the checkpoint can be loaded
$ cd ../data/lang_bpe_500
$ git lfs pull --include bpe.model
$ cd ../../..
To test the model, let's have a look at the decoding results **without** using LM. This can be done via the following command:

5
_sources/decoding-with-langugage-models/rescoring.rst.txt
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@ -34,9 +34,12 @@ As the initial step, let's download the pre-trained model.
.. code-block:: bash
$ GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
$ pushd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$ cd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$ git lfs pull --include "pretrained.pt"
$ ln -s pretrained.pt epoch-99.pt # create a symbolic link so that the checkpoint can be loaded
$ cd ../data/lang_bpe_500
$ git lfs pull --include bpe.model
$ cd ../../..
As usual, we first test the model's performance without external LM. This can be done via the following command:

5
_sources/decoding-with-langugage-models/shallow-fusion.rst.txt
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@ -32,9 +32,12 @@ As the initial step, let's download the pre-trained model.
.. code-block:: bash
$ GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
$ pushd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$ cd icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$ git lfs pull --include "pretrained.pt"
$ ln -s pretrained.pt epoch-99.pt # create a symbolic link so that the checkpoint can be loaded
$ cd ../data/lang_bpe_500
$ git lfs pull --include bpe.model
$ cd ../../..
To test the model, let's have a look at the decoding results without using LM. This can be done via the following command:

5
decoding-with-langugage-models/LODR.html
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@ -146,9 +146,12 @@ If you want to train your model from scratch, please have a look at <a class="re
The testing scenario here is intra-domain (we decode the model trained on <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a> on <a class="reference external" href="https://www.openslr.org/12">LibriSpeech</a> testing sets).</p>
<p>As the initial step, let’s download the pre-trained model.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nv">GIT_LFS_SKIP_SMUDGE</span><span class="o">=</span><span class="m">1</span><span class="w"> </span>git<span class="w"> </span>clone<span class="w"> </span>https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
$<span class="w"> </span><span class="nb">pushd</span><span class="w"> </span>icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$<span class="w"> </span>git<span class="w"> </span>lfs<span class="w"> </span>pull<span class="w"> </span>--include<span class="w"> </span><span class="s2">&quot;pretrained.pt&quot;</span>
$<span class="w"> </span>ln<span class="w"> </span>-s<span class="w"> </span>pretrained.pt<span class="w"> </span>epoch-99.pt<span class="w"> </span><span class="c1"># create a symbolic link so that the checkpoint can be loaded</span>
$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>../data/lang_bpe_500
$<span class="w"> </span>git<span class="w"> </span>lfs<span class="w"> </span>pull<span class="w"> </span>--include<span class="w"> </span>bpe.model
$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>../../..
</pre></div>
</div>
<p>To test the model, let’s have a look at the decoding results <strong>without</strong> using LM. This can be done via the following command:</p>

5
decoding-with-langugage-models/rescoring.html
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@ -118,9 +118,12 @@ to any other domains (e.g <a class="reference external" href="https://github.com
If you want to train your model from scratch, please have a look at <a class="reference internal" href="../recipes/Non-streaming-ASR/librispeech/pruned_transducer_stateless.html#non-streaming-librispeech-pruned-transducer-stateless"><span class="std std-ref">Pruned transducer statelessX</span></a>.</p>
<p>As the initial step, let’s download the pre-trained model.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nv">GIT_LFS_SKIP_SMUDGE</span><span class="o">=</span><span class="m">1</span><span class="w"> </span>git<span class="w"> </span>clone<span class="w"> </span>https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
$<span class="w"> </span><span class="nb">pushd</span><span class="w"> </span>icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$<span class="w"> </span>git<span class="w"> </span>lfs<span class="w"> </span>pull<span class="w"> </span>--include<span class="w"> </span><span class="s2">&quot;pretrained.pt&quot;</span>
$<span class="w"> </span>ln<span class="w"> </span>-s<span class="w"> </span>pretrained.pt<span class="w"> </span>epoch-99.pt<span class="w"> </span><span class="c1"># create a symbolic link so that the checkpoint can be loaded</span>
$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>../data/lang_bpe_500
$<span class="w"> </span>git<span class="w"> </span>lfs<span class="w"> </span>pull<span class="w"> </span>--include<span class="w"> </span>bpe.model
$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>../../..
</pre></div>
</div>
<p>As usual, we first test the model’s performance without external LM. This can be done via the following command:</p>

5
decoding-with-langugage-models/shallow-fusion.html
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@ -117,9 +117,12 @@ to any other domains (e.g <a class="reference external" href="https://github.com
If you want to train your model from scratch, please have a look at <a class="reference internal" href="../recipes/Non-streaming-ASR/librispeech/pruned_transducer_stateless.html#non-streaming-librispeech-pruned-transducer-stateless"><span class="std std-ref">Pruned transducer statelessX</span></a>.</p>
<p>As the initial step, let’s download the pre-trained model.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$<span class="w"> </span><span class="nv">GIT_LFS_SKIP_SMUDGE</span><span class="o">=</span><span class="m">1</span><span class="w"> </span>git<span class="w"> </span>clone<span class="w"> </span>https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
$<span class="w"> </span><span class="nb">pushd</span><span class="w"> </span>icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$<span class="w"> </span>git<span class="w"> </span>lfs<span class="w"> </span>pull<span class="w"> </span>--include<span class="w"> </span><span class="s2">&quot;pretrained.pt&quot;</span>
$<span class="w"> </span>ln<span class="w"> </span>-s<span class="w"> </span>pretrained.pt<span class="w"> </span>epoch-99.pt<span class="w"> </span><span class="c1"># create a symbolic link so that the checkpoint can be loaded</span>
$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>../data/lang_bpe_500
$<span class="w"> </span>git<span class="w"> </span>lfs<span class="w"> </span>pull<span class="w"> </span>--include<span class="w"> </span>bpe.model
$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>../../..
</pre></div>
</div>
<p>To test the model, let’s have a look at the decoding results without using LM. This can be done via the following command:</p>

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