| 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 | |