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update documentation for shallow fusion
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@ -51,7 +51,7 @@ To test the model, let's have a look at the decoding results without using LM. T
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The following WERs are achieved on test-clean and test-other:
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.. code-block:: bash
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.. code-block:: text
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$ For test-clean, WER of different settings are:
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$ beam_size_4 3.11 best for test-clean
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@ -81,6 +81,7 @@ To use shallow fusion for decoding, we can execute the following command:
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$ exp_dir=./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
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$ lm_dir=./icefall-librispeech-rnn-lm/exp
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$ lm_scale=0.29
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$ ./pruned_transducer_stateless7_streaming/decode.py \
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--epoch 99 \
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--avg 1 \
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@ -95,7 +96,7 @@ To use shallow fusion for decoding, we can execute the following command:
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--lm-type rnn \
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--lm-exp-dir $lm_dir \
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--lm-epoch 99 \
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--lm-scale 0.29 \
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--lm-scale $lm_scale \
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--lm-avg 1 \
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--rnn-lm-embedding-dim 2048 \
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--rnn-lm-hidden-dim 2048 \
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@ -118,7 +119,7 @@ between ``rnn`` or ``transformer``. The following three arguments are associated
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The decoding result obtained with the above command are shown below.
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.. code-block:: bash
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.. code-block:: text
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$ For test-clean, WER of different settings are:
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$ beam_size_4 2.77 best for test-clean
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@ -137,8 +138,30 @@ A few parameters can be tuned to further boost the performance of shallow fusion
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The number of active paths in the search beam. It controls the trade-off between decoding efficiency and accuracy.
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Here, we also show how `--beam-size` effect the WER and decoding time:
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.. list-table:: WERs and decoding time (on test-clean) of shallow fusion with different beam sizes
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:widths: 25 25 50
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:header-rows: 1
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* - Beam size
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- test-clean
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- test-other
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- Decoding time on test-clean (s)
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* - 4
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- 2.77
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- 7.08
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- 262
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* - 8
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- 2.62
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- 6.65
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- 352
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* - 12
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- 2.58
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- 6.65
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- 488
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As we see, a larger beam size during shallow fusion improves the WER, but is also slower.
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