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34 lines
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ReStructuredText
34 lines
1.5 KiB
ReStructuredText
Decoding with language models
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=============================
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This section describes how to use external langugage models
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during decoding to improve the WER of transducer models.
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The following decoding methods with external langugage models are available:
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.. list-table:: LM-rescoring-based methods vs shallow-fusion-based methods (The numbers in each field is WER on test-clean, WER on test-other and decoding time on test-clean)
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:widths: 25 50
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:header-rows: 1
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* - Decoding method
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- beam=4
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* - ``modified_beam_search``
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- 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.
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* - ``modified_beam_search_lm_shallow_fusion``
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- As ``modified_beam_search``, but interpolate RNN-T scores with language model scores, also known as shallow fusion
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* - ``modified_beam_search_LODR``
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- 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.
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* - ``modified_beam_search_lm_rescore``
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- As ``modified_beam_search``, but rescore the n-best hypotheses with external language model (e.g. RNNLM) and re-rank them.
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* - ``modified_beam_search_lm_rescore_LODR``
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- As ``modified_beam_search_lm_rescore``, but also subtract the score of a (BPE-symbol-level) bigram backoff language model during re-ranking.
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.. toctree::
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:maxdepth: 2
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shallow-fusion
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LODR
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rescoring
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