File size: 1,477 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 |
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
|