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915e8e399c
- Added CHiME-4 dataset integration in asr_datamodule.py - Added Hugging Face upload script - Added RIR augmentation - Added Self-Distillation Training
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35 lines
1.4 KiB
ReStructuredText
Executable File
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. To train an external language model,
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please refer to this tutorial: :ref:`train_nnlm`.
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The following decoding methods with external langugage models are available:
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.. list-table::
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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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