mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-09 18:12:19 +00:00
35 lines
1.4 KiB
ReStructuredText
35 lines
1.4 KiB
ReStructuredText
Decoding with language models
|
|
=============================
|
|
|
|
This section describes how to use external langugage models
|
|
during decoding to improve the WER of transducer models. To train an external language model,
|
|
please refer to this tutorial: :ref:`train_nnlm`.
|
|
|
|
The following decoding methods with external langugage models are available:
|
|
|
|
|
|
.. list-table::
|
|
:widths: 25 50
|
|
:header-rows: 1
|
|
|
|
* - Decoding method
|
|
- beam=4
|
|
* - ``modified_beam_search``
|
|
- Beam search (i.e. really n-best decoding, the "beam" is the value of n), similar to the original RNN-T paper. Note, this method does not use language model.
|
|
* - ``modified_beam_search_lm_shallow_fusion``
|
|
- As ``modified_beam_search``, but interpolate RNN-T scores with language model scores, also known as shallow fusion
|
|
* - ``modified_beam_search_LODR``
|
|
- As ``modified_beam_search_lm_shallow_fusion``, but subtract score of a (BPE-symbol-level) bigram backoff language model used as an approximation to the internal language model of RNN-T.
|
|
* - ``modified_beam_search_lm_rescore``
|
|
- As ``modified_beam_search``, but rescore the n-best hypotheses with external language model (e.g. RNNLM) and re-rank them.
|
|
* - ``modified_beam_search_lm_rescore_LODR``
|
|
- As ``modified_beam_search_lm_rescore``, but also subtract the score of a (BPE-symbol-level) bigram backoff language model during re-ranking.
|
|
|
|
|
|
.. toctree::
|
|
:maxdepth: 2
|
|
|
|
shallow-fusion
|
|
LODR
|
|
rescoring
|