Important

You are viewing the NeMo 2.0 documentation. This release introduces significant changes to the API and a new library, NeMo Run. We are currently porting all features from NeMo 1.0 to 2.0. For documentation on previous versions or features not yet available in 2.0, please refer to the NeMo 24.07 documentation.

ASR Language Modeling and Customization#

Language models have shown to help the accuracy of ASR models. NeMo supports the following two approaches to incorporate language models into the ASR models:

It is possible to use both approaches on the same ASR model.

N-gram Language Modeling#

In this approach, an N-gram LM is trained on text data, then it is used in fusion with beam search decoding to find the best candidates. The beam search decoders in NeMo support language models trained with KenLM library ( kpu/kenlm). The beam search decoders and KenLM library are not installed by default in NeMo. You need to install them to be able to use beam search decoding and N-gram LM. Please refer to scripts/asr_language_modeling/ngram_lm/install_beamsearch_decoders.sh on how to install them. Alternatively, you can build Docker image scripts/installers/Dockerfile.ngramtools with all the necessary dependencies.

NeMo supports both character-based and BPE-based models for N-gram LMs. An N-gram LM can be used with beam search decoders on top of the ASR models to produce more accurate candidates. The beam search decoder would incorporate the scores produced by the N-gram LM into its score calculations as the following:

final_score = acoustic_score + beam_alpha*lm_score + beam_beta*seq_length

where acoustic_score is the score predicted by the acoustic encoder and lm_score is the one estimated by the LM. The parameter ‘beam_alpha’ determines the weight given to the N-gram language model, while ‘beam_beta’ is a penalty term that accounts for sequence length in the scores. A larger ‘beam_alpha’ places more emphasis on the language model and less on the acoustic model. Negative values for ‘beam_beta’ penalize longer sequences, encouraging the decoder to prefer shorter predictions. Conversely, positive values for ‘beam_beta’ favor longer candidates.

Train N-gram LM#

The script to train an N-gram language model with KenLM can be found at: scripts/asr_language_modeling/ngram_lm/train_kenlm.py.

This script trains an N-gram language model with the KenLM library which can then be used with the beam search decoders on top of the ASR models. This script also supports both character-level and BPE-level encodings and models which are detected automatically from the model type.

You can train the N-gram model using the following:

python train_kenlm.py nemo_model_file=<path to the .nemo file of the model> \
                          train_paths=<list of paths to the training text or JSON manifest files> \
                          kenlm_bin_path=<path to the bin folder of KenLM library> \
                          kenlm_model_file=<path to store the binary KenLM model> \
                          ngram_length=<order of N-gram model> \
                          preserve_arpa=true

The train_paths parameter allows for various input types, such as a list of text files, JSON manifests, or directories, to be used as the training data. If the file’s extension is anything other than .json, it assumes that data format is plain text. For plain text format, each line should contain one sample. For the JSON manifests, the file must contain JSON-formatted samples per each line like this:

{"audio_filepath": "/data_path/file1.wav", "text": "The transcript of the audio file."}

This code extracts the text field from each line to create the training text file. After the N-gram model is trained, it is stored at the path specified by kenlm_model_file.

The following is the list of the arguments for the training script:

Argument

Type

Default

Description

nemo_model_file

str

Required

The path to .nemo file of the ASR model, or name of a pretrained NeMo model to extract a tokenizer.

train_paths

List[str]

Required

List of training files or folders. Files can be a plain text file or “.json” manifest or “.json.gz”.

kenlm_model_file

str

Required

The path to store the KenLM binary model file.

kenlm_bin_path

str

Required

The path to the bin folder of KenLM. It is a folder named bin under where KenLM is installed.

ngram_length**

int

Required

Specifies order of N-gram LM.

ngram_prune

List[int]

[0]

List of thresholds to prune N-grams. Example: [0,0,1]. See Pruning section on the https://kheafield.com/code/kenlm/estimation

cache_path

str

""

Cache path to save tokenized files.

preserve_arpa

bool

False

Whether to preserve the intermediate ARPA file after construction of the BIN file.

verbose

int

1

Verbose level.

..note:: It is recommended that you use 6 as the order of the N-gram model for BPE-based models. Higher orders may require re-compiling KenLM to support them.

Evaluate by Beam Search Decoding and N-gram LM#

NeMo’s beam search decoders are capable of using the KenLM’s N-gram models to find the best candidates. The script to evaluate an ASR model with beam search decoding and N-gram models can be found at scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py.

This script has a large number of possible argument overrides; therefore, it is recommended that you use python eval_beamsearch_ngram.py --help to see the full list of arguments.

You can evaluate an ASR model using the following:

python eval_beamsearch_ngram.py nemo_model_file=<path to the .nemo file of the model> \
       input_manifest=<path to the evaluation JSON manifest file \
       kenlm_model_file=<path to the binary KenLM model> \
       beam_width=[<list of the beam widths, separated with commas>] \
       beam_alpha=[<list of the beam alphas, separated with commas>] \
       beam_beta=[<list of the beam betas, separated with commas>] \
       preds_output_folder=<optional folder to store the predictions> \
       probs_cache_file=null \
       decoding_mode=beamsearch_ngram \
       decoding_strategy="<Beam library such as beam, pyctcdecode or flashlight>"

It can evaluate a model in the following three modes by setting the argument –decoding_mode:

  • greedy: Just greedy decoding is done and no beam search decoding is performed.

  • beamsearch: The beam search decoding is done, but without using the N-gram language model. Final results are equivalent to setting the weight of LM (beam_beta) to zero.

  • beamsearch_ngram: The beam search decoding is done with N-gram LM.

In beamsearch mode, the evaluation is performed using beam search decoding without any language model. The performance is reported in terms of Word Error Rate (WER) and Character Error Rate (CER). Moreover, when the best candidate is selected among the candidates, it is also reported as the best WER/CER. This can serve as an indicator of the quality of the predicted candidates.

The script initially loads the ASR model and predicts the outputs of the model’s encoder as log probabilities. This part is computed in batches on a device specified by –device, which can be either a CPU (–device=cpu) or a single GPU (–device=cuda:0). The batch size for this part is specified by –acoustic_batch_size. Using the largest feasible batch size can speed up the calculation of log probabilities. Additionally, you can use –use_amp to accelerate the calculation and allow for larger –acoustic_batch_size values. Currently, multi-GPU support is not available for calculating log probabilities. However, using –probs_cache_file can help. This option stores the log probabilities produced by the model’s encoder in a pickle file, allowing you to skip the first step in future runs.

The following is the list of the important arguments for the evaluation script:

Argument

Type

Default

Description

nemo_model_file

str

Required

The path of the .nemo file of the ASR model to extract the tokenizer.

input_manifest

str

Required

Path to the training file, it can be a text file or JSON manifest.

kenlm_model_file

str

Required

The path to store the KenLM binary model file.

preds_output_folder

str

None

The path to an optional folder to store the predictions.

probs_cache_file

str

None

The cache file for storing the outputs of the model.

acoustic_batch_size

int

16

The batch size to calculate log probabilities.

use_amp

bool

False

Whether to use AMP if available to calculate log probabilities.

device

str

cuda

The device to load the model onto to calculate log probabilities. It can cpu, cuda, cuda:0, cuda:1, …

decoding_mode

str

beamsearch_ngram

The decoding scheme to be used for evaluation.

beam_width

float

Required

List of the width or list of the widths of the beam search decoding.

beam_alpha

float

Required

List of the alpha parameter for the beam search decoding.

beam_beta

float

Required

List of the beta parameter for the beam search decoding.

beam_batch_size

int

128

The batch size to be used for beam search decoding. Larger batch size can be a little faster, but uses larger memory.

decoding_strategy

str

beam

String argument for type of decoding strategy for the model.

decoding

Dict Config

BeamCTC InferConfig

Subdict of beam search configs. Values found via python eval_beamsearch_ngram.py –help

text_processing.do_lowercase

bool

False

Whether to make the training text all lower case.

text_processing.punctuation_marks

str

""

String with punctuation marks to process. Example: “.,?”

text_processing.rm_punctuation

bool

False

Whether to remove punctuation marks from text.

text_processing.separate_punctuation

bool

True

Whether to separate punctuation with the previous word by space.

The width of the beam search (–beam_width) specifies the number of top candidates or predictions the beam search decoder will consider. Larger beam widths result in more accurate but slower predictions.

Note

The eval_beamsearch_ngram.py script contains the entire subconfig used for CTC Beam Decoding. Therefore it is possible to forward arguments for various beam search libraries such as flashlight and pyctcdecode via the decoding subconfig.

To learn more about evaluating the ASR models with N-gram LM, refer to the tutorial here: Offline ASR Inference with Beam Search and External Language Model Rescoring Offline ASR Inference with Beam Search and External Language Model Rescoring

Beam Search Engines#

NeMo ASR CTC supports multiple beam search engines for decoding. The default engine is beam, which is the OpenSeq2Seq decoding library.

OpenSeq2Seq (beam)#

CPU-based beam search engine that is quite efficient and supports char and subword models. It requires a character/subword KenLM model to be provided.

The config for this decoding library is described above.

Flashlight (flashlight)#

Flashlight is a C++ library for ASR decoding provided at flashlight/flashlight. It is a CPU- and CUDA-based beam search engine that is quite efficient and supports char and subword models. It requires an ARPA KenLM file.

It supports several advanced features, such as lexicon-based decoding, lexicon-free decoding, beam pruning threshold, and more.

@dataclass
class FlashlightConfig:
    lexicon_path: Optional[str] = None
    boost_path: Optional[str] = None
    beam_size_token: int = 16
    beam_threshold: float = 20.0
    unk_weight: float = -math.inf
    sil_weight: float = 0.0

# Lexicon-based decoding
python eval_beamsearch_ngram.py ... \
       decoding_strategy="flashlight" \
       decoding.beam.flashlight_cfg.lexicon_path='/path/to/lexicon.lexicon' \
       decoding.beam.flashlight_cfg.beam_size_token = 32 \
       decoding.beam.flashlight_cfg.beam_threshold = 25.0

# Lexicon-free decoding
python eval_beamsearch_ngram.py ... \
       decoding_strategy="flashlight" \
       decoding.beam.flashlight_cfg.beam_size_token = 32 \
       decoding.beam.flashlight_cfg.beam_threshold = 25.0
PyCTCDecode (pyctcdecode)#

PyCTCDecode is a Python library for ASR decoding provided at kensho-technologies/pyctcdecode. It is a CPU-based beam search engine that is somewhat efficient for a pure Python library, and supports char and subword models. It requires a character/subword KenLM ARPA / BINARY model to be provided.

It has advanced features, such as word boosting, which can be useful for transcript customization.

@dataclass
 class PyCTCDecodeConfig:
     beam_prune_logp: float = -10.0
     token_min_logp: float = -5.0
     prune_history: bool = False
     hotwords: Optional[List[str]] = None
     hotword_weight: float = 10.0

# PyCTCDecoding
python eval_beamsearch_ngram.py ... \
       decoding_strategy="pyctcdecode" \
       decoding.beam.pyctcdecode_cfg.beam_prune_logp = -10. \
       decoding.beam.pyctcdecode_cfg.token_min_logp = -5. \
       decoding.beam.pyctcdecode_cfg.hotwords=[<List of str words>] \
       decoding.beam.pyctcdecode_cfg.hotword_weight=10.0

Beam Search ngram Decoding for Transducer Models (RNNT and HAT)#

You can also find a similar script to evaluate an RNNT/HAT model with beam search decoding and N-gram models at: scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram_transducer.py

python eval_beamsearch_ngram_transducer.py nemo_model_file=<path to the .nemo file of the model> \
        input_manifest=<path to the evaluation JSON manifest file \
        kenlm_model_file=<path to the binary KenLM model> \
        beam_width=[<list of the beam widths, separated with commas>] \
        beam_alpha=[<list of the beam alphas, separated with commas>] \
        preds_output_folder=<optional folder to store the predictions> \
        probs_cache_file=null \
        decoding_strategy=<greedy_batch or maes decoding>
        maes_prefix_alpha=[<list of the maes prefix alphas, separated with commas>] \
        maes_expansion_gamma=[<list of the maes expansion gammas, separated with commas>] \
        hat_subtract_ilm=<in case of HAT model: subtract internal LM or not (True/False)> \
        hat_ilm_weight=[<in case of HAT model: list of the HAT internal LM weights, separated with commas>] \

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.

Train Neural Rescorer#

An example script to train such a language model with Transformer can be found at examples/nlp/language_modeling/transformer_lm.py. It trains a TransformerLMModel which can be used as a neural rescorer for an ASR system. Full documentation on language models training is available at:

Language Modeling

You can also use a pretrained language model from the Hugging Face library, such as Transformer-XL and GPT, instead of training your model. Models like BERT and RoBERTa are not supported by this script because they are trained as Masked Language Models. As a result, they are not efficient or effective for scoring sentences out of the box.

Evaluation#

Given a trained TransformerLMModel .nemo file or a pretrained HF model, the script available at scripts/asr_language_modeling/neural_rescorer/eval_neural_rescorer.py can be used to re-score beams obtained with ASR model. You need the .tsv file containing the candidates produced by the acoustic model and the beam search decoding to use this script. The candidates can be the result of just the beam search decoding or the result of fusion with an N-gram LM. You can generate this file by specifying –preds_output_folder for scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py.

The neural rescorer would rescore the beams/candidates by using two parameters of rescorer_alpha and rescorer_beta, as follows:

final_score = beam_search_score + rescorer_alpha*neural_rescorer_score + rescorer_beta*seq_length

The parameter rescorer_alpha specifies the importance placed on the neural rescorer model, while rescorer_beta is a penalty term that accounts for sequence length in the scores. These parameters have similar effects to beam_alpha and beam_beta in the beam search decoder and N-gram language model.

Use the following steps to evaluate a neural LM:

  1. Obtain .tsv file with beams and their corresponding scores. Scores can be from a regular beam search decoder or in fusion with an N-gram LM scores. For a given beam size beam_size and a number of examples for evaluation num_eval_examples, it should contain (num_eval_examples x beam_size) lines of form beam_candidate_text t score. This file can be generated by scripts/asr_language_modeling/ngram_lm/eval_beamsearch_ngram.py

  2. Rescore the candidates by scripts/asr_language_modeling/neural_rescorer/eval_neural_rescorer.py.

python eval_neural_rescorer.py
    --lm_model=[path to .nemo file of the LM or the name of a HF pretrained model]
    --beams_file=[path to beams .tsv file]
    --beam_size=[size of the beams]
    --eval_manifest=[path to eval manifest .json file]
    --batch_size=[batch size used for inference on the LM model]
    --alpha=[the value for the parameter rescorer_alpha]
    --beta=[the value for the parameter rescorer_beta]
    --scores_output_file=[the optional path to store the rescored candidates]

The candidates, along with their new scores, are stored at the file specified by –scores_output_file.

The following is the list of the arguments for the evaluation script:

Argument

Type

Default

Description

lm_model

str

Required

The path of the ‘.nemo’ file of an ASR model, or the name of a Hugging Face pretrained model like ‘transfo-xl-wt103’ or ‘gpt2’.

eval_manifest

str

Required

Path to the evaluation manifest file (.json manifest file).

beams_file

str

Required

Path to beams file (.tsv) containing the candidates and their scores.

beam_size

int

Required

The width of the beams (number of candidates) generated by the decoder.

alpha

float

None

The value for parameter rescorer_alpha Not passing value would enable linear search for rescorer_alpha.

beta

float

None

The value for parameter rescorer_beta Not passing value would enable linear search for rescorer_beta.

batch_size

int

16

The batch size used to calculate the scores.

max_seq_length

int

512

Maximum sequence length (in tokens) for the input.

scores_output_file

str

None

The optional file to store the rescored beams.

use_amp

bool

False

Whether to use AMP if available calculate the scores.

device

str

cuda

The device to load LM model onto to calculate the scores It can be ‘cpu’, ‘cuda’, ‘cuda:0’, ‘cuda:1’, …

Word Boosting#

The Flashlight decoder supports word boosting during CTC decoding using a KenLM binary and corresponding lexicon. Word boosting only works in lexicon-decoding mode and does not function in lexicon-free mode. It allows you to bias the decoder for certain words by manually increasing or decreasing the probability of emitting specific words. This can be very helpful if you have uncommon or industry-specific terms that you want to ensure are transcribed correctly.

For more information, go to word boosting

To use word boosting in NeMo, create a simple tab-separated text file. Each line should contain a word to be boosted, followed by a tab, and then the boosted score for that word.

For example:

nvidia      40
geforce     50
riva        80
turing      30
badword     -100

Positive scores boost words higher in the LM decoding step so they show up more frequently, whereas negative scores squelch words so they show up less frequently. The recommended range for the boost score is +/- 20 to 100.

The boost file handles both in-vocabulary words and OOV words just fine, so you can specify both IV and OOV words with corresponding scores.

You can then pass this file to your Flashlight config object during decoding:

# Lexicon-based decoding
python eval_beamsearch_ngram.py ... \
       decoding_strategy="flashlight" \
       decoding.beam.flashlight_cfg.lexicon_path='/path/to/lexicon.lexicon' \
       decoding.beam.flashlight_cfg.boost_path='/path/to/my_boost_file.boost' \
       decoding.beam.flashlight_cfg.beam_size_token = 32 \
       decoding.beam.flashlight_cfg.beam_threshold = 25.0

Combine N-gram Language Models#

Before combining N-gram LMs, install the required OpenGrm NGram library using scripts/installers/install_opengrm.sh. Alternatively, you can use Docker image scripts/installers/Dockerfile.ngramtools with all the necessary dependencies.

Alternatively, you can use the Docker image at: scripts/asr_language_modeling/ngram_lm/ngram_merge.py, which includes all the necessary dependencies.

This script interpolates two ARPA N-gram language models and creates a KenLM binary file that can be used with the beam search decoders on top of ASR models. You can specify weights (–alpha and –beta) for each of the models (–ngram_a and –ngram_b) correspondingly: alpha * ngram_a + beta * ngram_b. This script supports both character level and BPE level encodings and models which are detected automatically from the type of the model.

To combine two N-gram models, you can use the following command:

python ngram_merge.py  --kenlm_bin_path <path to the bin folder of KenLM library> \
                --ngram_bin_path  <path to the bin folder of OpenGrm Ngram library> \
                --arpa_a <path to the ARPA N-gram model file A> \
                --alpha <weight of N-gram model A> \
                --arpa_b <path to the ARPA N-gram model file B> \
                --beta <weight of N-gram model B> \
                --out_path <path to folder to store the output files>

If you provide –test_file and –nemo_model_file, This script supports both character-level and BPE-level encodings and models, which are detected automatically based on the type of the model. Note, the result of each step during the process is cached in the temporary file in the –out_path, to speed up further run. You can use the –force flag to discard the cache and recalculate everything from scratch.

python ngram_merge.py  --kenlm_bin_path <path to the bin folder of KenLM library> \
                --ngram_bin_path  <path to the bin folder of OpenGrm Ngram library> \
                --arpa_a <path to the ARPA N-gram model file A> \
                --alpha <weight of N-gram model A> \
                --arpa_b <path to the ARPA N-gram model file B> \
                --beta <weight of N-gram model B> \
                --out_path <path to folder to store the output files>
                --nemo_model_file <path to the .nemo file of the model> \
                --test_file <path to the test file> \
                --symbols <path to symbols (.syms) file> \
                --force <flag to recalculate and rewrite all cached files>

The following is the list of the arguments for the opengrm script:

Argument

Type

Default

Description

kenlm_bin_path

str

Required

The path to the bin folder of KenLM library. It is a folder named bin under where KenLM is installed.

ngram_bin_path

str

Required

The path to the bin folder of OpenGrm Ngram. It is a folder named bin under where OpenGrm Ngram is installed.

arpa_a

str

Required

Path to the ARPA N-gram model file A.

alpha

float

Required

Weight of N-gram model A.

arpa_b

int

Required

Path to the ARPA N-gram model file B.

beta

float

Required

Weight of N-gram model B.

out_path

str

Required

Path for writing temporary and resulting files.

test_file

str

None

Path to test file to count perplexity if provided.

symbols

str

None

Path to symbols (.syms) file. Could be calculated if it is not provided.

nemo_model_file

str

None

The path to ‘.nemo’ file of the ASR model, or name of a pretrained NeMo model.

force

bool

False

Whether to recompile and rewrite all files.

WFST CTC decoding#

Weighted Finite-State Transducers (WFST) are finite-state machines with input and output symbols on each transition and some weight element of a semiring. WFSTs can act as N-gram LMs in a special type of LM-forced beam search, called WFST decoding.

Note

More precisely, WFST decoding is more of a greedy N-depth search with LM. Thus, it is asymptotically worse than conventional beam search decoding algorithms, but faster.

WARNING At the moment, NeMo supports WFST decoding only for CTC models and word-based LMs.

To run WFST decoding in NeMo, one needs to provide a NeMo ASR model and either an ARPA LM or a WFST LM (advanced). An ARPA LM can be built from source text with KenLM as follows: <kenlm_bin_path>/lmplz -o <ngram_length> --arpa <out_arpa_path> --prune <ngram_prune>.

The script to evaluate an ASR model with WFST decoding and N-gram models can be found at scripts/asr_language_modeling/ngram_lm/eval_wfst_decoding_ctc.py.

This script has a large number of possible argument overrides, therefore it is advised to use python eval_wfst_decoding_ctc.py --help to see the full list of arguments.

You may evaluate an ASR model as the following:

python eval_wfst_decoding_ctc.py nemo_model_file=<path to the .nemo file of the model> \
       input_manifest=<path to the evaluation JSON manifest file> \
       arpa_model_file=<path to the ARPA LM model> \
       decoding_wfst_file=<path to the decoding WFST file> \
       beam_width=[<list of the beam widths, separated with commas>] \
       lm_weight=[<list of the LM weight multipliers, separated with commas>] \
       open_vocabulary_decoding=<whether to use open vocabulary mode for WFST decoding> \
       decoding_mode=<decoding mode, affects output. Usually "nbest"> \
       decoding_search_type=<WFST decoding library. Usually "riva"> \
       preds_output_folder=<optional folder to store the predictions> \
       probs_cache_file=null

Note

Since WFST decoding is LM-forced (the search goes over the WIDEST graph), only word sequences accepted by the WFST can appear in the decoding results. To circumvent this restriction, one can pass open_vocabulary_decoding=true (experimental feature).

Quick start example#

wget -O - https://www.openslr.org/resources/11/3-gram.pruned.1e-7.arpa.gz | \
gunzip -c | tr '[:upper:]' '[:lower:]' > 3-gram.pruned.1e-7.arpa && \
python eval_wfst_decoding_ctc.py nemo_model_file="stt_en_conformer_ctc_small_ls" \
       input_manifest="<data_dir>/Librispeech/test_other.json" \
       arpa_model_file="3-gram.pruned.1e-7.arpa" \
       decoding_wfst_file="3-gram.pruned.1e-7.fst" \
       beam_width=[8] \
       lm_weight=[0.5,0.6,0.7,0.8,0.9]

Note

Building a decoding WFST is a long process, so it is better to provide a decoding_wfst_file path even if you don’t have it. This way, the decoding WFST will be buffered to the specified file path and there will be no need to re-build it on the next run.

Context-biasing (Word Boosting) without External LM#

NeMo toolkit supports a fast context-biasing method for CTC and Transducer (RNN-T) ASR models with CTC-based Word Spotter. The method involves decoding CTC log probabilities with a context graph built for words and phrases from the context-biasing list. The spotted context-biasing candidates (with their scores and time intervals) are compared by scores with words from the greedy CTC decoding results to improve recognition accuracy and pretend false accepts of context-biasing.

A Hybrid Transducer-CTC model (a shared encoder trained together with CTC and Transducer output heads) enables the use of the CTC-WS method for the Transducer model. Context-biasing candidates obtained by CTC-WS are also filtered by the scores with greedy CTC predictions and then merged with greedy Transducer results.

Scheme of the CTC-WS method:

CTC-WS scheme

High-level overview of the context-biasing words replacement with CTC-WS method:

CTC-WS high level overview

More details about CTC-WS context-biasing can be found in the tutorial.

To use CTC-WS context-biasing, you need to create a context-biasing text file that contains words/phrases to be boosted, with its transcriptions (spellings) separated by underscore. Multiple transcriptions can be useful for abbreviations (“gpu” -> “g p u”), compound words (“nvlink” -> “nv link”), or words with common mistakes in the case of our ASR model (“nvidia” -> “n video”).

Example of the context-biasing file:

nvidia_nvidia
omniverse_omniverse
gpu_gpu_g p u
dgx_dgx_d g x_d gx
nvlink_nvlink_nv link
ray tracing_ray tracing

The main script for CTC-WS context-biasing in NeMo is:

{NEMO_DIR_PATH}/scripts/asr_context_biasing/eval_greedy_decoding_with_context_biasing.py

Context-biasing is managed by apply_context_biasing parameter [true or false]. Other important context-biasing parameters are:

  • beam_threshold - threshold for CTC-WS beam pruning.

  • context_score - per token weight for context biasing.

  • ctc_ali_token_weight - per token weight for CTC alignment (prevents false acceptances of context-biasing words).

All the context-biasing parameters are selected according to the default values in the script. You can tune them according to your data and ASR model (list all the values in the [] separated by commas) for example: beam_threshold=[7.0,8.0,9.0], context_score=[3.0,4.0,5.0], ctc_ali_token_weight=[0.5,0.6,0.7]. The script will run the recognition with all the combinations of the parameters and will select the best one based on WER value.

# Context-biasing with the CTC-WS method for CTC ASR model
python {NEMO_DIR_PATH}/scripts/asr_context_biasing/eval_greedy_decoding_with_context_biasing.py \
        nemo_model_file={ctc_model_name} \
        input_manifest={test_nemo_manifest} \
        preds_output_folder={exp_dir} \
        decoder_type="ctc" \
        acoustic_batch_size=64 \
        apply_context_biasing=true \
        context_file={cb_list_file_modified} \
        beam_threshold=[7.0] \
        context_score=[3.0] \
        ctc_ali_token_weight=[0.5]

To use Transducer head of the Hybrid Transducer-CTC model, you need to set decoder_type=rnnt.

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