| .. _asr_language_modeling_and_customization: |
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| ####################################### |
| ASR Language Modeling and Customization |
| ####################################### |
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| NeMo supports decoding-time customization techniques such as *language modeling* and *word boosting*, |
| which improve transcription accuracy by incorporating external knowledge or domain-specific vocabulary—without retraining the model. |
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| Language Modeling |
| ----------------- |
|
|
| In NeMo two approaches of external language modeling are supported: |
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| - **Language Model Fusion:** |
| Language model (LM) fusion integrates scores from an external statistical n-gram model into the ASR decoder. |
| This helps guide decoding toward more likely word sequences based on text corpora. |
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|
| NeMo provides two approaches for language model shallow fusion with ASR systems: |
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| **1. NGPU-LM (Recommended for Production)** |
| GPU-accelerated LM fusion for all major model types: CTC, RNN-T, TDT, and AED models. |
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| - Customization during both greedy and beam decoding. |
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| - Fast beam decoding for all major model types, offering only 20% RTFx difference between beam and greedy decoding. |
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| - Integration with NGPU-LM GPU-based ngram LM. |
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| For details, please refer to :ref:`ngpulm_ngram_modeling` |
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| **2. KenLM (Traditional CPU-based)** |
| CPU-based LM fusion using the KenLM library. |
| |
| .. note:: |
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| These approaches, especially beam decoding, can be extremely slow and are retained in the repository primarily for backward compatibility. |
| If possible, we recommend using NGPU-LM for improved performance. |
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| For details, please refer to :ref:`ngram_modeling` |
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|
| - **Neural Rescoring:** |
| When using the neural rescoring approach, a neural network is used to score candidates. A candidate is the text transcript predicted by the ASR model’s decoder. |
| The top K candidates produced by beam search decoding (with a beam width of K) are given to a neural language model for ranking. |
| The language model assigns a score to each candidate, which is usually combined with the scores from beam search decoding to produce the final scores and rankings. |
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| For details, please refer to :ref:`neural_rescoring`. |
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| Word Boosting |
| ------------- |
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| Word boosting increases the likelihood of specific words or phrases during decoding by applying a positive bias, helping the model better recognize names, |
| uncommon terms, and custom vocabulary. |
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| - :ref:`word_boosting_gpupb` (preferred): GPU-accelerated phrase-boosting for CTC, RNN-T/TDT, and AED (Canary) models supporting greedy and beam search decoding. |
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| - :ref:`word_boosting_flashlight`: Word-boosting method for CTC models with external n-gram LM. |
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| - :ref:`word_boosting_ctcws`: Word-boosting method for hybrid (Transducer-CTC) models without LM. |
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| For details, please refer to: :ref:`word_boosting`. |
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| LM Training |
| ----------- |
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| NeMo provides tools for training n-gram language models that can be used for language model fusion or word-boosting. |
| For details, please refer to: :ref:`ngram-utils`. |
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| .. toctree:: |
| :maxdepth: 1 |
| :hidden: |
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| asr_customization/ngpulm_language_modeling_and_customization |
| asr_customization/neural_rescoring |
| asr_customization/legacy_language_modeling_and_customization |
| asr_customization/ngram_utils |
| asr_customization/word_boosting |
