File size: 6,827 Bytes
d0afc2c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 |
.. _shallow_fusion:
Shallow fusion for Transducer
=================================
External language models (LM) are commonly used to improve WERs for E2E ASR models.
This tutorial shows you how to perform ``shallow fusion`` with an external LM
to improve the word-error-rate of a transducer model.
.. note::
This tutorial is based on the recipe
`pruned_transducer_stateless7_streaming <https://github.com/k2-fsa/icefall/tree/master/egs/librispeech/ASR/pruned_transducer_stateless7_streaming>`_,
which is a streaming transducer model trained on `LibriSpeech`_.
However, you can easily apply shallow fusion to other recipes.
If you encounter any problems, please open an issue here `icefall <https://github.com/k2-fsa/icefall/issues>`_.
.. note::
For simplicity, the training and testing corpus in this tutorial is the same (`LibriSpeech`_). However, you can change the testing set
to any other domains (e.g `GigaSpeech`_) and use an external LM trained on that domain.
.. HINT::
We recommend you to use a GPU for decoding.
For illustration purpose, we will use a pre-trained ASR model from this `link <https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29>`__.
If you want to train your model from scratch, please have a look at :ref:`non_streaming_librispeech_pruned_transducer_stateless`.
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
$ 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:
.. code-block:: bash
$ exp_dir=./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp/
$ ./pruned_transducer_stateless7_streaming/decode.py \
--epoch 99 \
--avg 1 \
--use-averaged-model False \
--exp-dir $exp_dir \
--bpe-model ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model \
--max-duration 600 \
--decode-chunk-len 32 \
--decoding-method modified_beam_search
The following WERs are achieved on test-clean and test-other:
.. code-block:: text
$ For test-clean, WER of different settings are:
$ beam_size_4 3.11 best for test-clean
$ For test-other, WER of different settings are:
$ beam_size_4 7.93 best for test-other
These are already good numbers! But we can further improve it by using shallow fusion with external LM.
Training a language model usually takes a long time, we can download a pre-trained LM from this `link <https://huggingface.co/ezerhouni/icefall-librispeech-rnn-lm>`__.
.. code-block:: bash
$ # download the external LM
$ GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/ezerhouni/icefall-librispeech-rnn-lm
$ # create a symbolic link so that the checkpoint can be loaded
$ pushd icefall-librispeech-rnn-lm/exp
$ git lfs pull --include "pretrained.pt"
$ ln -s pretrained.pt epoch-99.pt
$ popd
.. note::
This is an RNN LM trained on the LibriSpeech text corpus. So it might not be ideal for other corpus.
You may also train a RNN LM from scratch. Please refer to this `script <https://github.com/k2-fsa/icefall/blob/master/icefall/rnn_lm/train.py>`__
for training a RNN LM and this `script <https://github.com/k2-fsa/icefall/blob/master/icefall/transformer_lm/train.py>`__ to train a transformer LM.
To use shallow fusion for decoding, we can execute the following command:
.. code-block:: bash
$ exp_dir=./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/exp
$ lm_dir=./icefall-librispeech-rnn-lm/exp
$ lm_scale=0.29
$ ./pruned_transducer_stateless7_streaming/decode.py \
--epoch 99 \
--avg 1 \
--use-averaged-model False \
--beam-size 4 \
--exp-dir $exp_dir \
--max-duration 600 \
--decode-chunk-len 32 \
--decoding-method modified_beam_search_lm_shallow_fusion \
--bpe-model ./icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29/data/lang_bpe_500/bpe.model \
--use-shallow-fusion 1 \
--lm-type rnn \
--lm-exp-dir $lm_dir \
--lm-epoch 99 \
--lm-scale $lm_scale \
--lm-avg 1 \
--rnn-lm-embedding-dim 2048 \
--rnn-lm-hidden-dim 2048 \
--rnn-lm-num-layers 3 \
--lm-vocab-size 500
Note that we set ``--decoding-method modified_beam_search_lm_shallow_fusion`` and ``--use-shallow-fusion True``
to use shallow fusion. ``--lm-type`` specifies the type of neural LM we are going to use, you can either choose
between ``rnn`` or ``transformer``. The following three arguments are associated with the rnn:
- ``--rnn-lm-embedding-dim``
The embedding dimension of the RNN LM
- ``--rnn-lm-hidden-dim``
The hidden dimension of the RNN LM
- ``--rnn-lm-num-layers``
The number of RNN layers in the RNN LM.
The decoding result obtained with the above command are shown below.
.. code-block:: text
$ For test-clean, WER of different settings are:
$ beam_size_4 2.77 best for test-clean
$ For test-other, WER of different settings are:
$ beam_size_4 7.08 best for test-other
The improvement of shallow fusion is very obvious! The relative WER reduction on test-other is around 10.5%.
A few parameters can be tuned to further boost the performance of shallow fusion:
- ``--lm-scale``
Controls the scale of the LM. If too small, the external language model may not be fully utilized; if too large,
the LM score might be dominant during decoding, leading to bad WER. A typical value of this is around 0.3.
- ``--beam-size``
The number of active paths in the search beam. It controls the trade-off between decoding efficiency and accuracy.
Here, we also show how `--beam-size` effect the WER and decoding time:
.. list-table:: WERs and decoding time (on test-clean) of shallow fusion with different beam sizes
:widths: 25 25 25 25
:header-rows: 1
* - Beam size
- test-clean
- test-other
- Decoding time on test-clean (s)
* - 4
- 2.77
- 7.08
- 262
* - 8
- 2.62
- 6.65
- 352
* - 12
- 2.58
- 6.65
- 488
As we see, a larger beam size during shallow fusion improves the WER, but is also slower.
|