Pipeline Configuration

In the simplest use case, you can deploy an ASR pipeline to be used with the StreamingRecognize API call (refer to riva/proto/riva_asr.proto) without any language model as follows:

riva-build speech_recognition \
    /servicemaker-dev/<rmir_filename>:<encryption_key>  \
    /servicemaker-dev/<riva_filename>:<encryption_key> \
    --name=<pipeline_name> \
    --wfst_tokenizer_model=<wfst_tokenizer_model> \
    --wfst_verbalizer_model=<wfst_verbalizer_model> \
    --decoder_type=greedy 

where:

• <rmir_filename> is the Riva rmir file that is generated

• <riva_filename> is the name of the riva file to use as input

• <encryption_key> is the encryption key used during the export of the .riva file

• <name>,<acoustic_model_name> and <featurizer_name> are optional user-defined names for the components in the model repository.

• <wfst_tokenizer_model> is the name of the WFST tokenizer model file to use for inverse text normalization of ASR transcripts. Refer to Inverse Text Normalization for more details.

• <wfst_verbalizer_model> is the name of the WFST verbalizer model file to use for inverse text normalization of ASR transcripts. Refer to Inverse Text Normalization for more details.

• decoder_type is the type of decoder to use. Valid values are flashlight, os2s, greedy. We recommend using flashlight. Refer to Decoder Hyper-Parameters for more details.

Upon successful completion of this command, a file named <rmir_filename> is created in the /servicemaker-dev/ folder. Since no language model is specified, the Riva greedy decoder is used to predict the transcript based on the output of the acoustic model. If your .riva archives are encrypted you need to include :<encryption_key> at the end of the RMIR filename and Riva filename. Otherwise, this is unnecessary.

For embedded platforms, using a batch size of 1 is recommended since it achieves the lowest memory footprint. To use a batch size of 1, refer to the riva-build-optional-parameters section and set the various max_batch_size and max_execution_batch_size parameters to 1 while executing the riva-build command.

The following summary lists the riva-build commands used to generate the RMIR files from the Quick Start scripts for different models, modes, and their limitations:

Citrinet-1024Conformer-CTCCitrinet-256

EnglishSpanishGermanRussianMandarin

Streaming, low-latencyStreaming, high-throughputOffline

riva-build speech_recognition \
   <rmir_filename>:<key> <riva_filename>:<key> \
   --wfst_tokenizer_model=tokenize_and_classify.far \
   --wfst_verbalizer_model=verbalize.far \
   --name=citrinet-1024-en-US-asr-streaming \
   --ms_per_timestep=80 \
   --featurizer.use_utterance_norm_params=False \
   --featurizer.precalc_norm_time_steps=0 \
   --featurizer.precalc_norm_params=False \
   --vad.residue_blanks_at_start=-2 \
   --chunk_size=0.16 \
   --left_padding_size=1.92 \
   --right_padding_size=1.92 \
   --decoder_type=flashlight \
   --flashlight_decoder.asr_model_delay=-1 \
   --decoding_language_model_binary=<lm_binary> \
   --decoding_vocab=<decoder_vocab_file> \
   --flashlight_decoder.lm_weight=0.2 \
   --flashlight_decoder.word_insertion_score=0.2 \
   --flashlight_decoder.beam_threshold=20. \
   --language_code=en-US

You can easily customize your deployment by specifying your own language model and your own decoder lexicon files for example.

The .riva model, language model, lexicon vocabulary, and WFST files used to generate the RMIRs in the Quick Start scripts can be found at the following NGC locations:

Language	Acoustic Model (AM)	Language Model (LM) and Lexicon	Inverse Text Norm (ITN)
English	Citrinet-1024 Conformer Citrinet-256	n-gram LM (files riva_asr_train_datasets_3gram.binary and flashlight_decoder_vocab.txt)	ITN Grammars
Spanish	Citrinet-1024	n-gram LM	ITN Grammar
German	Citrinet-1024 Conformer	n-gram LM	ITN Grammar
Russian	Citrinet-1024	n-gram LM	n/a
Mandarin	Citrinet-1024	n-gram LM	n/a

For details about the parameters passed to riva-build to customize the ASR pipeline, run:

riva-build <pipeline> -h

Note

For information about deploying the now deprecated Jasper or QuartzNet models in Riva, refer to the Riva ASR Pipeline Configuration section.

Streaming/Offline Recognition

The Riva ASR pipeline can be configured for both streaming and offline recognition use cases. When using the StreamingRecognize API call (refer to riva/proto/riva_asr.proto), we recommend the following riva-build parameters for low-latency streaming recognition:

riva-build speech_recognition \
    /servicemaker-dev/<rmir_filename>:<encryption_key> \
    /servicemaker-dev/<riva_filename>:<encryption_key> \
    --name=<pipeline_name> \
    --wfst_tokenizer_model=<wfst_tokenizer_model> \
    --wfst_verbalizer_model=<wfst_verbalizer_model> \
    --decoder_type=greedy \
    --chunk_size=0.16 \
    --padding_size=1.92 \
    --ms_per_timestep=80 \
    --greedy_decoder.asr_model_delay=-1 \
    --vad.residue_blanks_at_start=-2 \
    --featurizer.use_utterance_norm_params=False \
    --featurizer.precalc_norm_time_steps=0 \
    --featurizer.precalc_norm_params=False

For high throughput streaming recognition with the StreamingRecognize API call, chunk_size and padding_size can be set as follows:

riva-build speech_recognition \
    /servicemaker-dev/<rmir_filename>:<encryption_key> \
    /servicemaker-dev/<riva_filename>:<encryption_key> \
    --name=<pipeline_name> \
    --wfst_tokenizer_model=<wfst_tokenizer_model> \
    --wfst_verbalizer_model=<wfst_verbalizer_model> \
    --decoder_type=greedy \
    --chunk_size=0.8 \
    --padding_size=1.6 \
    --ms_per_timestep=80 \
    --greedy_decoder.asr_model_delay=-1 \
    --vad.residue_blanks_at_start=-2 \
    --featurizer.use_utterance_norm_params=False \
    --featurizer.precalc_norm_time_steps=0 \
    --featurizer.precalc_norm_params=False

Finally, to configure the ASR pipeline for offline recognition with the Recognize API call (refer to riva/proto/riva_asr.proto), we recommend the following settings:

riva-build speech_recognition \
    /servicemaker-dev/<rmir_filename>:<encryption_key> \
    /servicemaker-dev/<riva_filename>:<encryption_key> \
    --offline \
    --name=<pipeline_name> \
    --wfst_tokenizer_model=<wfst_tokenizer_model> \
    --wfst_verbalizer_model=<wfst_verbalizer_model> \
    --decoder_type=greedy \
    --chunk_size=900. \
    --padding_size=0. \
    --ms_per_timestep=80 \
    --greedy_decoder.asr_model_delay=-1 \
    --vad.residue_blanks_at_start=-2 \
    --featurizer.use_utterance_norm_params=False \
    --featurizer.precalc_norm_time_steps=0 \
    --featurizer.precalc_norm_params=False

Note

When deploying the offline ASR models with riva-deploy, TensorRT warnings indicating that memory requirements of format conversion cannot be satisfied might appear in the logs. These warnings should not affect functionality and can be ignored.

In offline mode, the riva-build command allows transcribing audio files up to 900 seconds (15 minutes) long. It is possible to increase the chunk_size parameter to larger values, however, this increases the GPU memory usage of the offline ASR pipeline when deployed. Ensure you tune this value based on the number of models deployed, the memory capacity of the GPU used, and the maximum duration of the audio to transcribe in offline mode. To transcribe audio files that are longer than the maximum allowable duration in offline mode, revert to using the high throughput streaming recognition and the StreamingRecognize API. This offline mode limitation will be addressed in a future version of Riva.

Language Models

Riva ASR supports decoding with an n-gram language model. The n-gram language model can be provided in a few different ways.

1. A .riva file exported from TAO Toolkit.

2. A .arpa format file.

3. A KenLM binary format file.

For more information on building language models, refer to the Training Language Models section.

TAO Toolkit n-gram Language Model

When using a language model exported from TAO Toolkit, specify the language model by running the following riva-build command:

riva-build speech_recognition \
    /servicemaker-dev/<jmir_filename>:<encryption_key> \
    /servicemaker-dev/<riva_filename>:<encryption_key> \
    /servicemaker-dev/<n_gram_riva_filename>:<encryption_key> \
    --name=<pipeline_name> \
    --wfst_tokenizer_model=<wfst_tokenizer_model> \
    --wfst_verbalizer_model=<wfst_verbalizer_model> \
    --decoder_type=flashlight \
    --chunk_size=0.16 \
    --padding_size=1.92 \
    --ms_per_timestep=80 \
    --flashlight_decoder.asr_model_delay=-1 \
    --vad.residue_blanks_at_start=-2 \
    --featurizer.use_utterance_norm_params=False \
    --featurizer.precalc_norm_time_steps=0 \
    --featurizer.precalc_norm_params=False \
    --decoding_vocab=<decoder_vocab_file>

where decoder_vocab_file is the vocabulary used by the Flashlight lexicon decoder. The vocabulary file must contain one vocabulary word per line. A sample vocabulary file (flashlight_decoder_vocab.txt) can be downloaded from Riva ASR English LM.

ARPA Format Language Model

To configure the Riva ASR pipeline to use an n-gram language model stored in arpa format, run:

riva-build speech_recognition \
    /servicemaker-dev/<jmir_filename>:<encryption_key> \
    /servicemaker-dev/<riva_filename>:<encryption_key> \
    --name=<pipeline_name> \
    --wfst_tokenizer_model=<wfst_tokenizer_model> \
    --wfst_verbalizer_model=<wfst_verbalizer_model> \
    --decoder_type=flashlight \
    --chunk_size=0.16 \
    --padding_size=1.92 \
    --ms_per_timestep=80 \
    --flashlight_decoder.asr_model_delay=-1 \
    --vad.residue_blanks_at_start=-2 \
    --featurizer.use_utterance_norm_params=False \
    --featurizer.precalc_norm_time_steps=0 \
    --featurizer.precalc_norm_params=False \
    --decoding_language_model_arpa=<arpa_filename> \
    --decoding_vocab=<decoder_vocab_file>

KenLM Binary Language Model

To generate the Riva RMIR file when using a KenLM binary file to specify the language model, run:

riva-build speech_recognition \
    /servicemaker-dev/<jmir_filename>:<encryption_key> \
    /servicemaker-dev/<riva_filename>:<encryption_key> \
    --name=<pipeline_name> \
    --wfst_tokenizer_model=<wfst_tokenizer_model> \
    --wfst_verbalizer_model=<wfst_verbalizer_model> \
    --decoder_type=flashlight \
    --chunk_size=0.16 \
    --padding_size=1.92 \
    --ms_per_timestep=80 \
    --flashlight_decoder.asr_model_delay=-1 \
    --vad.residue_blanks_at_start=-2 \
    --featurizer.use_utterance_norm_params=False \
    --featurizer.precalc_norm_time_steps=0 \
    --featurizer.precalc_norm_params=False \
    --decoding_language_model_binary=<KENLM_binary_filename> \
    --decoding_vocab=<decoder_vocab_file>

Decoder Hyper-Parameters

The decoder language model hyper-parameters can also be specified from the riva-build command.

You can specify the Flashlight decoder hyper-parameters beam_size, beam_size_token, beam_threshold, lm_weight and word_insertion_score as follows:

riva-build speech_recognition \
    /servicemaker-dev/<jmir_filename>:<encryption_key> \
    /servicemaker-dev/<riva_filename>:<encryption_key> \
    --name=<pipeline_name> \
    --wfst_tokenizer_model=<wfst_tokenizer_model> \
    --wfst_verbalizer_model=<wfst_verbalizer_model> \
    --decoder_type=flashlight \
    --chunk_size=0.16 \
    --padding_size=1.92 \
    --ms_per_timestep=80 \
    --flashlight_decoder.asr_model_delay=-1 \
    --vad.residue_blanks_at_start=-2 \
    --featurizer.use_utterance_norm_params=False \
    --featurizer.precalc_norm_time_steps=0 \
    --featurizer.precalc_norm_params=False \
    --decoding_language_model_binary=<arpa_filename> \
    --decoding_vocab=<decoder_vocab_file> \
    --flashlight_decoder.beam_size=<beam_size> \
    --flashlight_decoder.beam_size_token=<beam_size_token> \
    --flashlight_decoder.beam_threshold=<beam_threshold> \
    --flashlight_decoder.lm_weight=<lm_weight> \
    --flashlight_decoder.word_insertion_score=<word_insertion_score>

where:

• beam_size is the maximum number of hypothesis the decoder holds at each step

• beam_size_token is the maximum number of tokens the decoder considers at each step

• beam_threshold is the threshold to prune hypothesis

• lm_weight is the weight of the language model used when scoring hypothesis

• word_insertion_score is the word insertion score used when scoring hypothesis

For advanced users, additional decoder hyper-parameters can also be specified. Refer to Riva-build Optional Parameters for a list of those parameters and their description.

Flashlight Decoder Lexicon

The Flashlight decoder used in Riva is a lexicon-based decoder and only emits words that are present in the decoder vocabulary file passed to the riva-build command. The decoder vocabulary file used to generate the ASR pipelines in the Quick Start scripts include words that cover a wide range of domains and should provide accurate transcripts for most applications.

It is also possible to build an ASR pipeline using your own decoder vocabulary file by using the parameter --decoding_vocab of the riva-build command. For example, you could start with the riva-build commands used to generate the ASR pipelines in our Quick Start scripts from section Pipeline Configuration and provide your own lexicon decoder vocabulary file. You will need to ensure that words of interest are in the decoder vocabulary file. The Riva ServiceMaker automatically tokenizes the words in the decoder vocabulary file. The number of tokenization for each word in the decoder vocabulary file can be controlled with the --flashlight_decoder.num_tokenization parameter.

(Advanced) Manually Adding Additional Tokenizations of Words in Lexicon

It is also possible to manually add additional tokenizations for the words in the decoder vocabulary by performing the following steps:

The riva-build and riva-deploy commands provided in the previous section store the lexicon in the /data/models/citrinet-1024-en-US-asr-streaming-ctc-decoder-cpu-streaming/1/lexicon.txt file of the Triton model repository.

To add additional tokenizations to the lexicon, copy the lexicon file:

cp /data/models/citrinet-1024-en-US-asr-streaming-ctc-decoder-cpu-streaming/1/lexicon.txt decoding_lexicon.txt

and add the SentencePiece tokenization for the word of interest. For example, you could add:

manu ▁ma n u
manu ▁man n n ew
manu ▁man n ew

to the decoding_lexicon.txt file so that the word manu is generated in the transcript if the acoustic model predicts those tokens. You will need to ensure that the new lines follow the indentation/space pattern like the rest of the file and that the tokens used are part of the tokenizer model. After this is done, regenerate the model repository using the new decoding lexicon by passing --decoding_lexicon=decoding_lexicon.txt to riva-build instead of --decoding_vocab=decoding_vocab.txt.

Beginning/End of Utterance Detection

Riva ASR uses an algorithm that detects the beginning and end of utterances. This algorithm is used to reset the ASR decoder state, and to trigger a call to the punctuator model. By default, the beginning of an utterance is flagged when 20% of the frames in a 300ms window has nonblank characters. The end of an utterance is flagged when 98% of the frames in a 800ms window are blank characters. You can tune those values for their particular use case by using the following riva-build parameters:

  --vad.vad_start_history=300 \
  --vad.vad_start_th=0.2 \
  --vad.vad_stop_history=800 \
  --vad.vad_stop_th=0.98

Additionally, it is possible to disable the beginning/end of utterance detection by passing --vad.vad_type=none to riva-build.

Note that in this case, the decoder state resets after the full audio signal has been sent by the client. Similarly, the punctuator model is only called once.

Generating Multiple Transcript Hypotheses

By default, the Riva ASR pipeline is configured to only generate the best transcript hypothesis for each utterance. It is possible to generate multiple transcript hypotheses by passing the parameter --max_supported_transcripts=N to the riva-build command, where N is the maximum number of hypotheses to generate. With these changes, the client application can retrieve the multiple hypotheses by setting the max_alternatives field of RecognitionConfig to values greater than 1.

(Advanced) Sharing Acoustic and Feature Extractor Models Across Multiple ASR Pipelines

It is possible to configure the Riva ASR service such that multiple ASR pipelines share the same feature extractor and acoustic models, thus allowing to reduce GPU memory usage. This option can be used, for example, to deploy multiple ASR pipelines where each pipeline uses a different language model, but share the same acoustic model and feature extractor. This can be achieved by specifying the parameters acoustic_model_name and featurizer_name in the riva-build command:

riva-build speech_recognition \
    /servicemaker-dev/<rmir_filename>:<encryption_key>  \
    /servicemaker-dev/<riva_filename>:<encryption_key> \
    --name=<pipeline_name> \
    --acoustic_model_name=<acoustic_model_name> \
    --featurizer_name=<featurizer_name> \
    --wfst_tokenizer_model=<wfst_tokenizer_model> \
    --wfst_verbalizer_model=<wfst_verbalizer_model> \
    --decoder_type=greedy 

where:

• <acoustic_model_name> is the user-defined name for the acoustic model component of the ASR pipeline

and

• <featurizer_name> is the user-defined name for the feature extractor component of the ASR pipeline

If multiple ASR pipelines are built, each with a different name, but with the same acoustic_model_name and featurizer_name, they will share the same acoustic and feature extractor models.

Note

<acoustic_model_name> and <featurizer_name> are global and can conflict across model pipelines. Override this only in cases when you know what other models will be deployed and you want to share the featurizer and/or acoustic models across different ASR pipelines. When specifying <acoustic_model_name> you should make sure that there will not be any incompatibilities in acoustic model weights or input shapes. Similary, when specifying <featurizer_name>, you should make sure that that all ASR pipelines with the same <featurizer_name> use the same feature extractor parameters.

Riva-build Optional Parameters

For details about the parameters passed to riva-build to customize the ASR pipeline, issue:

riva-build speech_recognition -h

The following list includes descriptions for all optional parameters currently recognized by riva-build:

usage: riva-build speech_recognition [-h] [-f] [-v]
                                     [--language_code LANGUAGE_CODE]
                                     [--max_batch_size MAX_BATCH_SIZE]
                                     [--acoustic_model_name ACOUSTIC_MODEL_NAME]
                                     [--featurizer_name FEATURIZER_NAME]
                                     [--name NAME] [--streaming STREAMING]
                                     [--offline] [--chunk_size CHUNK_SIZE]
                                     [--padding_factor PADDING_FACTOR]
                                     [--left_padding_size LEFT_PADDING_SIZE]
                                     [--right_padding_size RIGHT_PADDING_SIZE]
                                     [--padding_size PADDING_SIZE]
                                     [--max_supported_transcripts MAX_SUPPORTED_TRANSCRIPTS]
                                     [--ms_per_timestep MS_PER_TIMESTEP]
                                     [--lattice_beam LATTICE_BEAM]
                                     [--decoding_language_model_arpa DECODING_LANGUAGE_MODEL_ARPA]
                                     [--decoding_language_model_binary DECODING_LANGUAGE_MODEL_BINARY]
                                     [--decoding_language_model_fst DECODING_LANGUAGE_MODEL_FST]
                                     [--decoding_language_model_words DECODING_LANGUAGE_MODEL_WORDS]
                                     [--rescoring_language_model_arpa RESCORING_LANGUAGE_MODEL_ARPA]
                                     [--decoding_language_model_carpa DECODING_LANGUAGE_MODEL_CARPA]
                                     [--rescoring_language_model_carpa RESCORING_LANGUAGE_MODEL_CARPA]
                                     [--decoding_lexicon DECODING_LEXICON]
                                     [--decoding_vocab DECODING_VOCAB]
                                     [--tokenizer_model TOKENIZER_MODEL]
                                     [--decoder_type DECODER_TYPE]
                                     [--wfst_tokenizer_model WFST_TOKENIZER_MODEL]
                                     [--wfst_verbalizer_model WFST_VERBALIZER_MODEL]
                                     [--featurizer.max_sequence_idle_microseconds FEATURIZER.MAX_SEQUENCE_IDLE_MICROSECONDS]
                                     [--featurizer.max_batch_size FEATURIZER.MAX_BATCH_SIZE]
                                     [--featurizer.min_batch_size FEATURIZER.MIN_BATCH_SIZE]
                                     [--featurizer.opt_batch_size FEATURIZER.OPT_BATCH_SIZE]
                                     [--featurizer.preferred_batch_size FEATURIZER.PREFERRED_BATCH_SIZE]
                                     [--featurizer.batching_type FEATURIZER.BATCHING_TYPE]
                                     [--featurizer.preserve_ordering FEATURIZER.PRESERVE_ORDERING]
                                     [--featurizer.instance_group_count FEATURIZER.INSTANCE_GROUP_COUNT]
                                     [--featurizer.max_queue_delay_microseconds FEATURIZER.MAX_QUEUE_DELAY_MICROSECONDS]
                                     [--featurizer.max_execution_batch_size FEATURIZER.MAX_EXECUTION_BATCH_SIZE]
                                     [--featurizer.gain FEATURIZER.GAIN]
                                     [--featurizer.dither FEATURIZER.DITHER]
                                     [--featurizer.stddev_floor FEATURIZER.STDDEV_FLOOR]
                                     [--featurizer.use_utterance_norm_params FEATURIZER.USE_UTTERANCE_NORM_PARAMS]
                                     [--featurizer.precalc_norm_time_steps FEATURIZER.PRECALC_NORM_TIME_STEPS]
                                     [--featurizer.precalc_norm_params FEATURIZER.PRECALC_NORM_PARAMS]
                                     [--featurizer.norm_per_feature FEATURIZER.NORM_PER_FEATURE]
                                     [--featurizer.mean FEATURIZER.MEAN]
                                     [--featurizer.stddev FEATURIZER.STDDEV]
                                     [--featurizer.transpose FEATURIZER.TRANSPOSE]
                                     [--featurizer.padding_size FEATURIZER.PADDING_SIZE]
                                     [--nn.max_sequence_idle_microseconds NN.MAX_SEQUENCE_IDLE_MICROSECONDS]
                                     [--nn.max_batch_size NN.MAX_BATCH_SIZE]
                                     [--nn.min_batch_size NN.MIN_BATCH_SIZE]
                                     [--nn.opt_batch_size NN.OPT_BATCH_SIZE]
                                     [--nn.preferred_batch_size NN.PREFERRED_BATCH_SIZE]
                                     [--nn.batching_type NN.BATCHING_TYPE]
                                     [--nn.preserve_ordering NN.PRESERVE_ORDERING]
                                     [--nn.instance_group_count NN.INSTANCE_GROUP_COUNT]
                                     [--nn.max_queue_delay_microseconds NN.MAX_QUEUE_DELAY_MICROSECONDS]
                                     [--nn.trt_max_workspace_size NN.TRT_MAX_WORKSPACE_SIZE]
                                     [--nn.use_onnx_runtime]
                                     [--nn.use_trt_fp32]
                                     [--nn.fp16_needs_obey_precision_pass]
                                     [--vad.max_sequence_idle_microseconds VAD.MAX_SEQUENCE_IDLE_MICROSECONDS]
                                     [--vad.max_batch_size VAD.MAX_BATCH_SIZE]
                                     [--vad.min_batch_size VAD.MIN_BATCH_SIZE]
                                     [--vad.opt_batch_size VAD.OPT_BATCH_SIZE]
                                     [--vad.preferred_batch_size VAD.PREFERRED_BATCH_SIZE]
                                     [--vad.batching_type VAD.BATCHING_TYPE]
                                     [--vad.preserve_ordering VAD.PRESERVE_ORDERING]
                                     [--vad.instance_group_count VAD.INSTANCE_GROUP_COUNT]
                                     [--vad.max_queue_delay_microseconds VAD.MAX_QUEUE_DELAY_MICROSECONDS]
                                     [--vad.ms_per_timestep VAD.MS_PER_TIMESTEP]
                                     [--vad.vad_start_history VAD.VAD_START_HISTORY]
                                     [--vad.vad_stop_history VAD.VAD_STOP_HISTORY]
                                     [--vad.vad_start_th VAD.VAD_START_TH]
                                     [--vad.vad_stop_th VAD.VAD_STOP_TH]
                                     [--vad.vad_type VAD.VAD_TYPE]
                                     [--vad.residue_blanks_at_start VAD.RESIDUE_BLANKS_AT_START]
                                     [--vad.residue_blanks_at_end VAD.RESIDUE_BLANKS_AT_END]
                                     [--vad.vocab_file VAD.VOCAB_FILE]
                                     [--flashlight_decoder.max_sequence_idle_microseconds FLASHLIGHT_DECODER.MAX_SEQUENCE_IDLE_MICROSECONDS]
                                     [--flashlight_decoder.max_batch_size FLASHLIGHT_DECODER.MAX_BATCH_SIZE]
                                     [--flashlight_decoder.min_batch_size FLASHLIGHT_DECODER.MIN_BATCH_SIZE]
                                     [--flashlight_decoder.opt_batch_size FLASHLIGHT_DECODER.OPT_BATCH_SIZE]
                                     [--flashlight_decoder.preferred_batch_size FLASHLIGHT_DECODER.PREFERRED_BATCH_SIZE]
                                     [--flashlight_decoder.batching_type FLASHLIGHT_DECODER.BATCHING_TYPE]
                                     [--flashlight_decoder.preserve_ordering FLASHLIGHT_DECODER.PRESERVE_ORDERING]
                                     [--flashlight_decoder.instance_group_count FLASHLIGHT_DECODER.INSTANCE_GROUP_COUNT]
                                     [--flashlight_decoder.max_queue_delay_microseconds FLASHLIGHT_DECODER.MAX_QUEUE_DELAY_MICROSECONDS]
                                     [--flashlight_decoder.max_execution_batch_size FLASHLIGHT_DECODER.MAX_EXECUTION_BATCH_SIZE]
                                     [--flashlight_decoder.decoder_type FLASHLIGHT_DECODER.DECODER_TYPE]
                                     [--flashlight_decoder.padding_size FLASHLIGHT_DECODER.PADDING_SIZE]
                                     [--flashlight_decoder.max_supported_transcripts FLASHLIGHT_DECODER.MAX_SUPPORTED_TRANSCRIPTS]
                                     [--flashlight_decoder.asr_model_delay FLASHLIGHT_DECODER.ASR_MODEL_DELAY]
                                     [--flashlight_decoder.ms_per_timestep FLASHLIGHT_DECODER.MS_PER_TIMESTEP]
                                     [--flashlight_decoder.vocab_file FLASHLIGHT_DECODER.VOCAB_FILE]
                                     [--flashlight_decoder.decoder_num_worker_threads FLASHLIGHT_DECODER.DECODER_NUM_WORKER_THREADS]
                                     [--flashlight_decoder.language_model_file FLASHLIGHT_DECODER.LANGUAGE_MODEL_FILE]
                                     [--flashlight_decoder.lexicon_file FLASHLIGHT_DECODER.LEXICON_FILE]
                                     [--flashlight_decoder.beam_size FLASHLIGHT_DECODER.BEAM_SIZE]
                                     [--flashlight_decoder.beam_size_token FLASHLIGHT_DECODER.BEAM_SIZE_TOKEN]
                                     [--flashlight_decoder.beam_threshold FLASHLIGHT_DECODER.BEAM_THRESHOLD]
                                     [--flashlight_decoder.lm_weight FLASHLIGHT_DECODER.LM_WEIGHT]
                                     [--flashlight_decoder.blank_token FLASHLIGHT_DECODER.BLANK_TOKEN]
                                     [--flashlight_decoder.sil_token FLASHLIGHT_DECODER.SIL_TOKEN]
                                     [--flashlight_decoder.word_insertion_score FLASHLIGHT_DECODER.WORD_INSERTION_SCORE]
                                     [--flashlight_decoder.forerunner_beam_size FLASHLIGHT_DECODER.FORERUNNER_BEAM_SIZE]
                                     [--flashlight_decoder.forerunner_beam_size_token FLASHLIGHT_DECODER.FORERUNNER_BEAM_SIZE_TOKEN]
                                     [--flashlight_decoder.forerunner_beam_threshold FLASHLIGHT_DECODER.FORERUNNER_BEAM_THRESHOLD]
                                     [--flashlight_decoder.smearing_mode FLASHLIGHT_DECODER.SMEARING_MODE]
                                     [--flashlight_decoder.forerunner_use_lm FLASHLIGHT_DECODER.FORERUNNER_USE_LM]
                                     [--flashlight_decoder.num_tokenization FLASHLIGHT_DECODER.NUM_TOKENIZATION]
                                     [--greedy_decoder.max_sequence_idle_microseconds GREEDY_DECODER.MAX_SEQUENCE_IDLE_MICROSECONDS]
                                     [--greedy_decoder.max_batch_size GREEDY_DECODER.MAX_BATCH_SIZE]
                                     [--greedy_decoder.min_batch_size GREEDY_DECODER.MIN_BATCH_SIZE]
                                     [--greedy_decoder.opt_batch_size GREEDY_DECODER.OPT_BATCH_SIZE]
                                     [--greedy_decoder.preferred_batch_size GREEDY_DECODER.PREFERRED_BATCH_SIZE]
                                     [--greedy_decoder.batching_type GREEDY_DECODER.BATCHING_TYPE]
                                     [--greedy_decoder.preserve_ordering GREEDY_DECODER.PRESERVE_ORDERING]
                                     [--greedy_decoder.instance_group_count GREEDY_DECODER.INSTANCE_GROUP_COUNT]
                                     [--greedy_decoder.max_queue_delay_microseconds GREEDY_DECODER.MAX_QUEUE_DELAY_MICROSECONDS]
                                     [--greedy_decoder.max_execution_batch_size GREEDY_DECODER.MAX_EXECUTION_BATCH_SIZE]
                                     [--greedy_decoder.decoder_type GREEDY_DECODER.DECODER_TYPE]
                                     [--greedy_decoder.padding_size GREEDY_DECODER.PADDING_SIZE]
                                     [--greedy_decoder.max_supported_transcripts GREEDY_DECODER.MAX_SUPPORTED_TRANSCRIPTS]
                                     [--greedy_decoder.asr_model_delay GREEDY_DECODER.ASR_MODEL_DELAY]
                                     [--greedy_decoder.ms_per_timestep GREEDY_DECODER.MS_PER_TIMESTEP]
                                     [--greedy_decoder.vocab_file GREEDY_DECODER.VOCAB_FILE]
                                     [--greedy_decoder.decoder_num_worker_threads GREEDY_DECODER.DECODER_NUM_WORKER_THREADS]
                                     [--os2s_decoder.max_sequence_idle_microseconds OS2S_DECODER.MAX_SEQUENCE_IDLE_MICROSECONDS]
                                     [--os2s_decoder.max_batch_size OS2S_DECODER.MAX_BATCH_SIZE]
                                     [--os2s_decoder.min_batch_size OS2S_DECODER.MIN_BATCH_SIZE]
                                     [--os2s_decoder.opt_batch_size OS2S_DECODER.OPT_BATCH_SIZE]
                                     [--os2s_decoder.preferred_batch_size OS2S_DECODER.PREFERRED_BATCH_SIZE]
                                     [--os2s_decoder.batching_type OS2S_DECODER.BATCHING_TYPE]
                                     [--os2s_decoder.preserve_ordering OS2S_DECODER.PRESERVE_ORDERING]
                                     [--os2s_decoder.instance_group_count OS2S_DECODER.INSTANCE_GROUP_COUNT]
                                     [--os2s_decoder.max_queue_delay_microseconds OS2S_DECODER.MAX_QUEUE_DELAY_MICROSECONDS]
                                     [--os2s_decoder.max_execution_batch_size OS2S_DECODER.MAX_EXECUTION_BATCH_SIZE]
                                     [--os2s_decoder.decoder_type OS2S_DECODER.DECODER_TYPE]
                                     [--os2s_decoder.padding_size OS2S_DECODER.PADDING_SIZE]
                                     [--os2s_decoder.max_supported_transcripts OS2S_DECODER.MAX_SUPPORTED_TRANSCRIPTS]
                                     [--os2s_decoder.asr_model_delay OS2S_DECODER.ASR_MODEL_DELAY]
                                     [--os2s_decoder.ms_per_timestep OS2S_DECODER.MS_PER_TIMESTEP]
                                     [--os2s_decoder.vocab_file OS2S_DECODER.VOCAB_FILE]
                                     [--os2s_decoder.decoder_num_worker_threads OS2S_DECODER.DECODER_NUM_WORKER_THREADS]
                                     [--os2s_decoder.language_model_file OS2S_DECODER.LANGUAGE_MODEL_FILE]
                                     [--os2s_decoder.beam_search_width OS2S_DECODER.BEAM_SEARCH_WIDTH]
                                     [--os2s_decoder.language_model_alpha OS2S_DECODER.LANGUAGE_MODEL_ALPHA]
                                     [--os2s_decoder.language_model_beta OS2S_DECODER.LANGUAGE_MODEL_BETA]
                                     [--kaldi_decoder.max_sequence_idle_microseconds KALDI_DECODER.MAX_SEQUENCE_IDLE_MICROSECONDS]
                                     [--kaldi_decoder.max_batch_size KALDI_DECODER.MAX_BATCH_SIZE]
                                     [--kaldi_decoder.min_batch_size KALDI_DECODER.MIN_BATCH_SIZE]
                                     [--kaldi_decoder.opt_batch_size KALDI_DECODER.OPT_BATCH_SIZE]
                                     [--kaldi_decoder.preferred_batch_size KALDI_DECODER.PREFERRED_BATCH_SIZE]
                                     [--kaldi_decoder.batching_type KALDI_DECODER.BATCHING_TYPE]
                                     [--kaldi_decoder.preserve_ordering KALDI_DECODER.PRESERVE_ORDERING]
                                     [--kaldi_decoder.instance_group_count KALDI_DECODER.INSTANCE_GROUP_COUNT]
                                     [--kaldi_decoder.max_queue_delay_microseconds KALDI_DECODER.MAX_QUEUE_DELAY_MICROSECONDS]
                                     [--kaldi_decoder.max_execution_batch_size KALDI_DECODER.MAX_EXECUTION_BATCH_SIZE]
                                     [--kaldi_decoder.decoder_type KALDI_DECODER.DECODER_TYPE]
                                     [--kaldi_decoder.padding_size KALDI_DECODER.PADDING_SIZE]
                                     [--kaldi_decoder.max_supported_transcripts KALDI_DECODER.MAX_SUPPORTED_TRANSCRIPTS]
                                     [--kaldi_decoder.asr_model_delay KALDI_DECODER.ASR_MODEL_DELAY]
                                     [--kaldi_decoder.ms_per_timestep KALDI_DECODER.MS_PER_TIMESTEP]
                                     [--kaldi_decoder.vocab_file KALDI_DECODER.VOCAB_FILE]
                                     [--kaldi_decoder.decoder_num_worker_threads KALDI_DECODER.DECODER_NUM_WORKER_THREADS]
                                     [--kaldi_decoder.fst_filename KALDI_DECODER.FST_FILENAME]
                                     [--kaldi_decoder.word_syms_filename KALDI_DECODER.WORD_SYMS_FILENAME]
                                     [--kaldi_decoder.default_beam KALDI_DECODER.DEFAULT_BEAM]
                                     [--kaldi_decoder.max_active KALDI_DECODER.MAX_ACTIVE]
                                     [--kaldi_decoder.acoustic_scale KALDI_DECODER.ACOUSTIC_SCALE]
                                     [--kaldi_decoder.decoder_num_copy_threads KALDI_DECODER.DECODER_NUM_COPY_THREADS]
                                     [--kaldi_decoder.determinize_lattice KALDI_DECODER.DETERMINIZE_LATTICE]
                                     [--rescorer.max_sequence_idle_microseconds RESCORER.MAX_SEQUENCE_IDLE_MICROSECONDS]
                                     [--rescorer.max_batch_size RESCORER.MAX_BATCH_SIZE]
                                     [--rescorer.min_batch_size RESCORER.MIN_BATCH_SIZE]
                                     [--rescorer.opt_batch_size RESCORER.OPT_BATCH_SIZE]
                                     [--rescorer.preferred_batch_size RESCORER.PREFERRED_BATCH_SIZE]
                                     [--rescorer.batching_type RESCORER.BATCHING_TYPE]
                                     [--rescorer.preserve_ordering RESCORER.PRESERVE_ORDERING]
                                     [--rescorer.instance_group_count RESCORER.INSTANCE_GROUP_COUNT]
                                     [--rescorer.max_queue_delay_microseconds RESCORER.MAX_QUEUE_DELAY_MICROSECONDS]
                                     [--rescorer.max_supported_transcripts RESCORER.MAX_SUPPORTED_TRANSCRIPTS]
                                     [--rescorer.score_lm_carpa_filename RESCORER.SCORE_LM_CARPA_FILENAME]
                                     [--rescorer.decode_lm_carpa_filename RESCORER.DECODE_LM_CARPA_FILENAME]
                                     [--rescorer.word_syms_filename RESCORER.WORD_SYMS_FILENAME]
                                     [--rescorer.word_insertion_penalty RESCORER.WORD_INSERTION_PENALTY]
                                     [--rescorer.num_worker_threads RESCORER.NUM_WORKER_THREADS]
                                     [--rescorer.ms_per_timestep RESCORER.MS_PER_TIMESTEP]
                                     [--rescorer.boundary_character_ids RESCORER.BOUNDARY_CHARACTER_IDS]
                                     [--rescorer.vocab_file RESCORER.VOCAB_FILE]
                                     [--lm_decoder_cpu.beam_search_width LM_DECODER_CPU.BEAM_SEARCH_WIDTH]
                                     [--lm_decoder_cpu.decoder_type LM_DECODER_CPU.DECODER_TYPE]
                                     [--lm_decoder_cpu.padding_size LM_DECODER_CPU.PADDING_SIZE]
                                     [--lm_decoder_cpu.language_model_file LM_DECODER_CPU.LANGUAGE_MODEL_FILE]
                                     [--lm_decoder_cpu.max_supported_transcripts LM_DECODER_CPU.MAX_SUPPORTED_TRANSCRIPTS]
                                     [--lm_decoder_cpu.asr_model_delay LM_DECODER_CPU.ASR_MODEL_DELAY]
                                     [--lm_decoder_cpu.language_model_alpha LM_DECODER_CPU.LANGUAGE_MODEL_ALPHA]
                                     [--lm_decoder_cpu.language_model_beta LM_DECODER_CPU.LANGUAGE_MODEL_BETA]
                                     [--lm_decoder_cpu.ms_per_timestep LM_DECODER_CPU.MS_PER_TIMESTEP]
                                     [--lm_decoder_cpu.vocab_file LM_DECODER_CPU.VOCAB_FILE]
                                     [--lm_decoder_cpu.lexicon_file LM_DECODER_CPU.LEXICON_FILE]
                                     [--lm_decoder_cpu.beam_size LM_DECODER_CPU.BEAM_SIZE]
                                     [--lm_decoder_cpu.beam_size_token LM_DECODER_CPU.BEAM_SIZE_TOKEN]
                                     [--lm_decoder_cpu.beam_threshold LM_DECODER_CPU.BEAM_THRESHOLD]
                                     [--lm_decoder_cpu.lm_weight LM_DECODER_CPU.LM_WEIGHT]
                                     [--lm_decoder_cpu.word_insertion_score LM_DECODER_CPU.WORD_INSERTION_SCORE]
                                     [--lm_decoder_cpu.forerunner_beam_size LM_DECODER_CPU.FORERUNNER_BEAM_SIZE]
                                     [--lm_decoder_cpu.forerunner_beam_size_token LM_DECODER_CPU.FORERUNNER_BEAM_SIZE_TOKEN]
                                     [--lm_decoder_cpu.forerunner_beam_threshold LM_DECODER_CPU.FORERUNNER_BEAM_THRESHOLD]
                                     [--lm_decoder_cpu.smearing_mode LM_DECODER_CPU.SMEARING_MODE]
                                     [--lm_decoder_cpu.forerunner_use_lm LM_DECODER_CPU.FORERUNNER_USE_LM]
                                     output_path source_path [source_path ...]

Generate a Riva Model from a speech_recognition model trained with NVIDIA
NeMo.

positional arguments:
  output_path           Location to write compiled Riva pipeline
  source_path           Source file(s)

optional arguments:
  -h, --help            show this help message and exit
  -f, --force           Overwrite existing artifacts if they exist
  -v, --verbose         Verbose log outputs
  --language_code LANGUAGE_CODE
                        Language of the model
  --max_batch_size MAX_BATCH_SIZE
                        Default maximum parallel requests in a single forward
                        pass
  --acoustic_model_name ACOUSTIC_MODEL_NAME
                        name of the acoustic model
  --featurizer_name FEATURIZER_NAME
                        name of the feature extractor model
  --name NAME           name of the ASR pipeline, used to set the model names
                        in the Riva model repository
  --streaming STREAMING
                        Execute model in streaming mode
  --offline             Marking the model to be used with offline API in Riva
  --chunk_size CHUNK_SIZE
                        Size of audio chunks to use during inference. If not
                        specified, default will be selected based on
                        online/offline setting
  --padding_factor PADDING_FACTOR
                        Multiple on the chunk_size. Deprecated and will be
                        ignored
  --left_padding_size LEFT_PADDING_SIZE
                        The duration in seconds of the backward looking
                        padding to prepend to the audio chunk. The acoustic
                        model input corresponds to a duration of
                        (left_padding_size + chunk_size + right_padding_size)
                        seconds
  --right_padding_size RIGHT_PADDING_SIZE
                        The duration in seconds of the forward looking padding
                        to append to the audio chunk. The acoustic model input
                        corresponds to a duration of (left_padding_size +
                        chunk_size + right_padding_size) seconds
  --padding_size PADDING_SIZE
                        padding_size
  --max_supported_transcripts MAX_SUPPORTED_TRANSCRIPTS
                        The maximum number of hypothesized transcripts
                        generated per utterance
  --ms_per_timestep MS_PER_TIMESTEP
                        The duration in milliseconds of one timestep of the
                        acoustic model output
  --lattice_beam LATTICE_BEAM
  --decoding_language_model_arpa DECODING_LANGUAGE_MODEL_ARPA
                        Language model .arpa used during decoding
  --decoding_language_model_binary DECODING_LANGUAGE_MODEL_BINARY
                        Language model .binary used during decoding
  --decoding_language_model_fst DECODING_LANGUAGE_MODEL_FST
                        Language model fst used during decoding
  --decoding_language_model_words DECODING_LANGUAGE_MODEL_WORDS
                        Language model words used during decoding
  --rescoring_language_model_arpa RESCORING_LANGUAGE_MODEL_ARPA
                        Language model .arpa used during lattice rescoring
  --decoding_language_model_carpa DECODING_LANGUAGE_MODEL_CARPA
                        Language model .carpa used during decoding
  --rescoring_language_model_carpa RESCORING_LANGUAGE_MODEL_CARPA
                        Language model .carpa used during lattice rescoring
  --decoding_lexicon DECODING_LEXICON
                        Lexicon to use when decoding
  --decoding_vocab DECODING_VOCAB
                        File of unique words separated by white space. Only
                        used if decoding_lexicon not provided.
  --tokenizer_model TOKENIZER_MODEL
                        Sentencpiece model to use for encoding. Only include
                        if generating lexicon from vocab.
  --decoder_type DECODER_TYPE
                        Type of decoder to use. Valid entries are greedy,
                        os2s, flashlight or kaldi
  --wfst_tokenizer_model WFST_TOKENIZER_MODEL
                        Sparrowhawk model to use for tokenization and
                        classification, must be in .far format
  --wfst_verbalizer_model WFST_VERBALIZER_MODEL
                        Sparrowhawk model to use for verbalizer, must be in
                        .far format.

featurizer:
  --featurizer.max_sequence_idle_microseconds FEATURIZER.MAX_SEQUENCE_IDLE_MICROSECONDS
                        Global timeout, in ms
  --featurizer.max_batch_size FEATURIZER.MAX_BATCH_SIZE
                        Default maximum parallel requests in a single forward
                        pass
  --featurizer.min_batch_size FEATURIZER.MIN_BATCH_SIZE
  --featurizer.opt_batch_size FEATURIZER.OPT_BATCH_SIZE
  --featurizer.preferred_batch_size FEATURIZER.PREFERRED_BATCH_SIZE
                        Preferred batch size, must be smaller than Max batch
                        size
  --featurizer.batching_type FEATURIZER.BATCHING_TYPE
  --featurizer.preserve_ordering FEATURIZER.PRESERVE_ORDERING
                        Preserve ordering
  --featurizer.instance_group_count FEATURIZER.INSTANCE_GROUP_COUNT
                        How many instances in a group
  --featurizer.max_queue_delay_microseconds FEATURIZER.MAX_QUEUE_DELAY_MICROSECONDS
                        Maximum amount of time to allow requests to queue to
                        form a batch in microseconds
  --featurizer.max_execution_batch_size FEATURIZER.MAX_EXECUTION_BATCH_SIZE
                        Maximum Batch Size
  --featurizer.gain FEATURIZER.GAIN
                        Adjust input signal with this gain multiplier prior to
                        feature extraction
  --featurizer.dither FEATURIZER.DITHER
                        Augment signal with gaussian noise with this gain to
                        prevent quantization artifacts
  --featurizer.stddev_floor FEATURIZER.STDDEV_FLOOR
                        Add this value to computed features standard
                        deviation. Higher values help reduce spurious
                        transcripts with low energy signals.
  --featurizer.use_utterance_norm_params FEATURIZER.USE_UTTERANCE_NORM_PARAMS
                        Apply normalization at utterance level
  --featurizer.precalc_norm_time_steps FEATURIZER.PRECALC_NORM_TIME_STEPS
                        Weight of the precomputed normalization parameters, in
                        timesteps. Setting to 0 will disable use of
                        precalculated normalization parameters.
  --featurizer.precalc_norm_params FEATURIZER.PRECALC_NORM_PARAMS
                        Boolean that controls if precalculated Normalization
                        Parameters should be used
  --featurizer.norm_per_feature FEATURIZER.NORM_PER_FEATURE
                        Normalize Per Feature
  --featurizer.mean FEATURIZER.MEAN
                        Pre-computed mean values
  --featurizer.stddev FEATURIZER.STDDEV
                        Pre-computed Std Dev Values
  --featurizer.transpose FEATURIZER.TRANSPOSE
                        Take transpose of output features
  --featurizer.padding_size FEATURIZER.PADDING_SIZE
                        padding_size

nn:
  --nn.max_sequence_idle_microseconds NN.MAX_SEQUENCE_IDLE_MICROSECONDS
                        Global timeout, in ms
  --nn.max_batch_size NN.MAX_BATCH_SIZE
                        Default maximum parallel requests in a single forward
                        pass
  --nn.min_batch_size NN.MIN_BATCH_SIZE
  --nn.opt_batch_size NN.OPT_BATCH_SIZE
  --nn.preferred_batch_size NN.PREFERRED_BATCH_SIZE
                        Preferred batch size, must be smaller than Max batch
                        size
  --nn.batching_type NN.BATCHING_TYPE
  --nn.preserve_ordering NN.PRESERVE_ORDERING
                        Preserve ordering
  --nn.instance_group_count NN.INSTANCE_GROUP_COUNT
                        How many instances in a group
  --nn.max_queue_delay_microseconds NN.MAX_QUEUE_DELAY_MICROSECONDS
                        Maximum amount of time to allow requests to queue to
                        form a batch in microseconds
  --nn.trt_max_workspace_size NN.TRT_MAX_WORKSPACE_SIZE
                        Maximum workspace size (in MB) to use for model export
                        to TensorRT
  --nn.use_onnx_runtime
                        Use ONNX runtime instead of TensorRT
  --nn.use_trt_fp32     Use TensorRT engine with FP32 instead of FP16
  --nn.fp16_needs_obey_precision_pass
                        Flag to explicitly mark layers as float when parsing
                        the ONNX network

vad:
  --vad.max_sequence_idle_microseconds VAD.MAX_SEQUENCE_IDLE_MICROSECONDS
                        Global timeout, in ms
  --vad.max_batch_size VAD.MAX_BATCH_SIZE
                        Default maximum parallel requests in a single forward
                        pass
  --vad.min_batch_size VAD.MIN_BATCH_SIZE
  --vad.opt_batch_size VAD.OPT_BATCH_SIZE
  --vad.preferred_batch_size VAD.PREFERRED_BATCH_SIZE
                        Preferred batch size, must be smaller than Max batch
                        size
  --vad.batching_type VAD.BATCHING_TYPE
  --vad.preserve_ordering VAD.PRESERVE_ORDERING
                        Preserve ordering
  --vad.instance_group_count VAD.INSTANCE_GROUP_COUNT
                        How many instances in a group
  --vad.max_queue_delay_microseconds VAD.MAX_QUEUE_DELAY_MICROSECONDS
                        Maximum amount of time to allow requests to queue to
                        form a batch in microseconds
  --vad.ms_per_timestep VAD.MS_PER_TIMESTEP
  --vad.vad_start_history VAD.VAD_START_HISTORY
                        Size of the window, in milliseconds, to use to detect
                        start of utterance. If (vad_start_th) of
                        (vad_start_history) ms of the acoustic model output
                        have non-blank tokens, start of utterance is detected.
  --vad.vad_stop_history VAD.VAD_STOP_HISTORY
                        Size of the window, in milliseconds, to use to detect
                        end of utterance. If (vad_stop_th) of
                        (vad_stop_history) ms of the acoustic model output
                        have non-blank tokens, end of utterance is detected.
  --vad.vad_start_th VAD.VAD_START_TH
                        Percentage threshold to use to detect start of
                        utterance. If (vad_start_th) of (vad_start_history) ms
                        of the acoustic model output have non-blank tokens,
                        start of utterance is detected.
  --vad.vad_stop_th VAD.VAD_STOP_TH
                        Percentage threshold to use to detect end of
                        utterance. If (vad_stop_th) of (vad_stop_history) ms
                        of the acoustic model output have non-blank tokens,
                        end of utterance is detected.
  --vad.vad_type VAD.VAD_TYPE
                        Type of voice activity detection algorithm to use. Set
                        to none to disable VAD.
  --vad.residue_blanks_at_start VAD.RESIDUE_BLANKS_AT_START
                        (Advanced) Number of time steps to ignore at the
                        beginning of the acoustic model output when trying to
                        detect start/end of speech
  --vad.residue_blanks_at_end VAD.RESIDUE_BLANKS_AT_END
                        (Advanced) Number of time steps to ignore at the end
                        of the acoustic model output when trying to detect
                        start/end of speech
  --vad.vocab_file VAD.VOCAB_FILE
                        Vocab file to be used with decoder

flashlight_decoder:
  --flashlight_decoder.max_sequence_idle_microseconds FLASHLIGHT_DECODER.MAX_SEQUENCE_IDLE_MICROSECONDS
                        Global timeout, in ms
  --flashlight_decoder.max_batch_size FLASHLIGHT_DECODER.MAX_BATCH_SIZE
                        Default maximum parallel requests in a single forward
                        pass
  --flashlight_decoder.min_batch_size FLASHLIGHT_DECODER.MIN_BATCH_SIZE
  --flashlight_decoder.opt_batch_size FLASHLIGHT_DECODER.OPT_BATCH_SIZE
  --flashlight_decoder.preferred_batch_size FLASHLIGHT_DECODER.PREFERRED_BATCH_SIZE
                        Preferred batch size, must be smaller than Max batch
                        size
  --flashlight_decoder.batching_type FLASHLIGHT_DECODER.BATCHING_TYPE
  --flashlight_decoder.preserve_ordering FLASHLIGHT_DECODER.PRESERVE_ORDERING
                        Preserve ordering
  --flashlight_decoder.instance_group_count FLASHLIGHT_DECODER.INSTANCE_GROUP_COUNT
                        How many instances in a group
  --flashlight_decoder.max_queue_delay_microseconds FLASHLIGHT_DECODER.MAX_QUEUE_DELAY_MICROSECONDS
                        Maximum amount of time to allow requests to queue to
                        form a batch in microseconds
  --flashlight_decoder.max_execution_batch_size FLASHLIGHT_DECODER.MAX_EXECUTION_BATCH_SIZE
  --flashlight_decoder.decoder_type FLASHLIGHT_DECODER.DECODER_TYPE
  --flashlight_decoder.padding_size FLASHLIGHT_DECODER.PADDING_SIZE
                        padding_size
  --flashlight_decoder.max_supported_transcripts FLASHLIGHT_DECODER.MAX_SUPPORTED_TRANSCRIPTS
  --flashlight_decoder.asr_model_delay FLASHLIGHT_DECODER.ASR_MODEL_DELAY
                        (Advanced) Number of time steps by which the acoustic
                        model output should be shifted when computing
                        timestamps. This parameter must be tuned since the CTC
                        model is not guaranteed to predict correct alignment.
  --flashlight_decoder.ms_per_timestep FLASHLIGHT_DECODER.MS_PER_TIMESTEP
  --flashlight_decoder.vocab_file FLASHLIGHT_DECODER.VOCAB_FILE
                        Vocab file to be used with decoder
  --flashlight_decoder.decoder_num_worker_threads FLASHLIGHT_DECODER.DECODER_NUM_WORKER_THREADS
                        Number of threads to use for CPU decoders. If < 1,
                        maximum hardware concurrency is used.
  --flashlight_decoder.language_model_file FLASHLIGHT_DECODER.LANGUAGE_MODEL_FILE
                        Language model file in binary format to be used by
                        KenLM
  --flashlight_decoder.lexicon_file FLASHLIGHT_DECODER.LEXICON_FILE
                        Lexicon file to be used with decoder
  --flashlight_decoder.beam_size FLASHLIGHT_DECODER.BEAM_SIZE
                        Maximum number of hypothesis the decoder holds after
                        each step
  --flashlight_decoder.beam_size_token FLASHLIGHT_DECODER.BEAM_SIZE_TOKEN
                        Maximum number of tokens the decoder considers at each
                        step
  --flashlight_decoder.beam_threshold FLASHLIGHT_DECODER.BEAM_THRESHOLD
                        Threshold to prune hypothesis
  --flashlight_decoder.lm_weight FLASHLIGHT_DECODER.LM_WEIGHT
                        Weight of language model
  --flashlight_decoder.blank_token FLASHLIGHT_DECODER.BLANK_TOKEN
                        Blank token
  --flashlight_decoder.sil_token FLASHLIGHT_DECODER.SIL_TOKEN
                        Silence token
  --flashlight_decoder.word_insertion_score FLASHLIGHT_DECODER.WORD_INSERTION_SCORE
                        Word insertion score
  --flashlight_decoder.forerunner_beam_size FLASHLIGHT_DECODER.FORERUNNER_BEAM_SIZE
                        Maximum number of hypothesis the decoder holds after
                        each step, for forerunner transcript
  --flashlight_decoder.forerunner_beam_size_token FLASHLIGHT_DECODER.FORERUNNER_BEAM_SIZE_TOKEN
                        Maximum number of tokens the decoder considers at each
                        step, for forerunner transcript
  --flashlight_decoder.forerunner_beam_threshold FLASHLIGHT_DECODER.FORERUNNER_BEAM_THRESHOLD
                        Threshold to prune hypothesis, for forerunner
                        transcript
  --flashlight_decoder.smearing_mode FLASHLIGHT_DECODER.SMEARING_MODE
                        Decoder smearing mode. Can be logadd, max or none
  --flashlight_decoder.forerunner_use_lm FLASHLIGHT_DECODER.FORERUNNER_USE_LM
                        Bool that controls if the forerunner decoder should
                        use a language model
  --flashlight_decoder.num_tokenization FLASHLIGHT_DECODER.NUM_TOKENIZATION
                        Number of tokenizations to generate for each word in
                        the lexicon

greedy_decoder:
  --greedy_decoder.max_sequence_idle_microseconds GREEDY_DECODER.MAX_SEQUENCE_IDLE_MICROSECONDS
                        Global timeout, in ms
  --greedy_decoder.max_batch_size GREEDY_DECODER.MAX_BATCH_SIZE
                        Default maximum parallel requests in a single forward
                        pass
  --greedy_decoder.min_batch_size GREEDY_DECODER.MIN_BATCH_SIZE
  --greedy_decoder.opt_batch_size GREEDY_DECODER.OPT_BATCH_SIZE
  --greedy_decoder.preferred_batch_size GREEDY_DECODER.PREFERRED_BATCH_SIZE
                        Preferred batch size, must be smaller than Max batch
                        size
  --greedy_decoder.batching_type GREEDY_DECODER.BATCHING_TYPE
  --greedy_decoder.preserve_ordering GREEDY_DECODER.PRESERVE_ORDERING
                        Preserve ordering
  --greedy_decoder.instance_group_count GREEDY_DECODER.INSTANCE_GROUP_COUNT
                        How many instances in a group
  --greedy_decoder.max_queue_delay_microseconds GREEDY_DECODER.MAX_QUEUE_DELAY_MICROSECONDS
                        Maximum amount of time to allow requests to queue to
                        form a batch in microseconds
  --greedy_decoder.max_execution_batch_size GREEDY_DECODER.MAX_EXECUTION_BATCH_SIZE
  --greedy_decoder.decoder_type GREEDY_DECODER.DECODER_TYPE
  --greedy_decoder.padding_size GREEDY_DECODER.PADDING_SIZE
                        padding_size
  --greedy_decoder.max_supported_transcripts GREEDY_DECODER.MAX_SUPPORTED_TRANSCRIPTS
  --greedy_decoder.asr_model_delay GREEDY_DECODER.ASR_MODEL_DELAY
                        (Advanced) Number of time steps by which the acoustic
                        model output should be shifted when computing
                        timestamps. This parameter must be tuned since the CTC
                        model is not guaranteed to predict correct alignment.
  --greedy_decoder.ms_per_timestep GREEDY_DECODER.MS_PER_TIMESTEP
  --greedy_decoder.vocab_file GREEDY_DECODER.VOCAB_FILE
                        Vocab file to be used with decoder
  --greedy_decoder.decoder_num_worker_threads GREEDY_DECODER.DECODER_NUM_WORKER_THREADS
                        Number of threads to use for CPU decoders. If < 1,
                        maximum hardware concurrency is used.

os2s_decoder:
  --os2s_decoder.max_sequence_idle_microseconds OS2S_DECODER.MAX_SEQUENCE_IDLE_MICROSECONDS
                        Global timeout, in ms
  --os2s_decoder.max_batch_size OS2S_DECODER.MAX_BATCH_SIZE
                        Default maximum parallel requests in a single forward
                        pass
  --os2s_decoder.min_batch_size OS2S_DECODER.MIN_BATCH_SIZE
  --os2s_decoder.opt_batch_size OS2S_DECODER.OPT_BATCH_SIZE
  --os2s_decoder.preferred_batch_size OS2S_DECODER.PREFERRED_BATCH_SIZE
                        Preferred batch size, must be smaller than Max batch
                        size
  --os2s_decoder.batching_type OS2S_DECODER.BATCHING_TYPE
  --os2s_decoder.preserve_ordering OS2S_DECODER.PRESERVE_ORDERING
                        Preserve ordering
  --os2s_decoder.instance_group_count OS2S_DECODER.INSTANCE_GROUP_COUNT
                        How many instances in a group
  --os2s_decoder.max_queue_delay_microseconds OS2S_DECODER.MAX_QUEUE_DELAY_MICROSECONDS
                        Maximum amount of time to allow requests to queue to
                        form a batch in microseconds
  --os2s_decoder.max_execution_batch_size OS2S_DECODER.MAX_EXECUTION_BATCH_SIZE
  --os2s_decoder.decoder_type OS2S_DECODER.DECODER_TYPE
  --os2s_decoder.padding_size OS2S_DECODER.PADDING_SIZE
                        padding_size
  --os2s_decoder.max_supported_transcripts OS2S_DECODER.MAX_SUPPORTED_TRANSCRIPTS
  --os2s_decoder.asr_model_delay OS2S_DECODER.ASR_MODEL_DELAY
                        (Advanced) Number of time steps by which the acoustic
                        model output should be shifted when computing
                        timestamps. This parameter must be tuned since the CTC
                        model is not guaranteed to predict correct alignment.
  --os2s_decoder.ms_per_timestep OS2S_DECODER.MS_PER_TIMESTEP
  --os2s_decoder.vocab_file OS2S_DECODER.VOCAB_FILE
                        Vocab file to be used with decoder
  --os2s_decoder.decoder_num_worker_threads OS2S_DECODER.DECODER_NUM_WORKER_THREADS
                        Number of threads to use for CPU decoders. If < 1,
                        maximum hardware concurrency is used.
  --os2s_decoder.language_model_file OS2S_DECODER.LANGUAGE_MODEL_FILE
                        Language model file in binary format to be used by
                        KenLM
  --os2s_decoder.beam_search_width OS2S_DECODER.BEAM_SEARCH_WIDTH
  --os2s_decoder.language_model_alpha OS2S_DECODER.LANGUAGE_MODEL_ALPHA
  --os2s_decoder.language_model_beta OS2S_DECODER.LANGUAGE_MODEL_BETA

kaldi_decoder:
  --kaldi_decoder.max_sequence_idle_microseconds KALDI_DECODER.MAX_SEQUENCE_IDLE_MICROSECONDS
                        Global timeout, in ms
  --kaldi_decoder.max_batch_size KALDI_DECODER.MAX_BATCH_SIZE
                        Default maximum parallel requests in a single forward
                        pass
  --kaldi_decoder.min_batch_size KALDI_DECODER.MIN_BATCH_SIZE
  --kaldi_decoder.opt_batch_size KALDI_DECODER.OPT_BATCH_SIZE
  --kaldi_decoder.preferred_batch_size KALDI_DECODER.PREFERRED_BATCH_SIZE
                        Preferred batch size, must be smaller than Max batch
                        size
  --kaldi_decoder.batching_type KALDI_DECODER.BATCHING_TYPE
  --kaldi_decoder.preserve_ordering KALDI_DECODER.PRESERVE_ORDERING
                        Preserve ordering
  --kaldi_decoder.instance_group_count KALDI_DECODER.INSTANCE_GROUP_COUNT
                        How many instances in a group
  --kaldi_decoder.max_queue_delay_microseconds KALDI_DECODER.MAX_QUEUE_DELAY_MICROSECONDS
                        Maximum amount of time to allow requests to queue to
                        form a batch in microseconds
  --kaldi_decoder.max_execution_batch_size KALDI_DECODER.MAX_EXECUTION_BATCH_SIZE
  --kaldi_decoder.decoder_type KALDI_DECODER.DECODER_TYPE
  --kaldi_decoder.padding_size KALDI_DECODER.PADDING_SIZE
                        padding_size
  --kaldi_decoder.max_supported_transcripts KALDI_DECODER.MAX_SUPPORTED_TRANSCRIPTS
  --kaldi_decoder.asr_model_delay KALDI_DECODER.ASR_MODEL_DELAY
                        (Advanced) Number of time steps by which the acoustic
                        model output should be shifted when computing
                        timestamps. This parameter must be tuned since the CTC
                        model is not guaranteed to predict correct alignment.
  --kaldi_decoder.ms_per_timestep KALDI_DECODER.MS_PER_TIMESTEP
  --kaldi_decoder.vocab_file KALDI_DECODER.VOCAB_FILE
                        Vocab file to be used with decoder
  --kaldi_decoder.decoder_num_worker_threads KALDI_DECODER.DECODER_NUM_WORKER_THREADS
                        Number of threads to use for CPU decoders. If < 1,
                        maximum hardware concurrency is used.
  --kaldi_decoder.fst_filename KALDI_DECODER.FST_FILENAME
                        Fst file to use during decoding
  --kaldi_decoder.word_syms_filename KALDI_DECODER.WORD_SYMS_FILENAME
  --kaldi_decoder.default_beam KALDI_DECODER.DEFAULT_BEAM
  --kaldi_decoder.max_active KALDI_DECODER.MAX_ACTIVE
  --kaldi_decoder.acoustic_scale KALDI_DECODER.ACOUSTIC_SCALE
  --kaldi_decoder.decoder_num_copy_threads KALDI_DECODER.DECODER_NUM_COPY_THREADS
  --kaldi_decoder.determinize_lattice KALDI_DECODER.DETERMINIZE_LATTICE

rescorer:
  --rescorer.max_sequence_idle_microseconds RESCORER.MAX_SEQUENCE_IDLE_MICROSECONDS
                        Global timeout, in ms
  --rescorer.max_batch_size RESCORER.MAX_BATCH_SIZE
                        Default maximum parallel requests in a single forward
                        pass
  --rescorer.min_batch_size RESCORER.MIN_BATCH_SIZE
  --rescorer.opt_batch_size RESCORER.OPT_BATCH_SIZE
  --rescorer.preferred_batch_size RESCORER.PREFERRED_BATCH_SIZE
                        Preferred batch size, must be smaller than Max batch
                        size
  --rescorer.batching_type RESCORER.BATCHING_TYPE
  --rescorer.preserve_ordering RESCORER.PRESERVE_ORDERING
                        Preserve ordering
  --rescorer.instance_group_count RESCORER.INSTANCE_GROUP_COUNT
                        How many instances in a group
  --rescorer.max_queue_delay_microseconds RESCORER.MAX_QUEUE_DELAY_MICROSECONDS
                        Maximum amount of time to allow requests to queue to
                        form a batch in microseconds
  --rescorer.max_supported_transcripts RESCORER.MAX_SUPPORTED_TRANSCRIPTS
  --rescorer.score_lm_carpa_filename RESCORER.SCORE_LM_CARPA_FILENAME
  --rescorer.decode_lm_carpa_filename RESCORER.DECODE_LM_CARPA_FILENAME
  --rescorer.word_syms_filename RESCORER.WORD_SYMS_FILENAME
  --rescorer.word_insertion_penalty RESCORER.WORD_INSERTION_PENALTY
  --rescorer.num_worker_threads RESCORER.NUM_WORKER_THREADS
  --rescorer.ms_per_timestep RESCORER.MS_PER_TIMESTEP
  --rescorer.boundary_character_ids RESCORER.BOUNDARY_CHARACTER_IDS
  --rescorer.vocab_file RESCORER.VOCAB_FILE
                        Vocab file to be used with decoder

lm_decoder_cpu:
  --lm_decoder_cpu.beam_search_width LM_DECODER_CPU.BEAM_SEARCH_WIDTH
  --lm_decoder_cpu.decoder_type LM_DECODER_CPU.DECODER_TYPE
  --lm_decoder_cpu.padding_size LM_DECODER_CPU.PADDING_SIZE
                        padding_size
  --lm_decoder_cpu.language_model_file LM_DECODER_CPU.LANGUAGE_MODEL_FILE
                        Language model file in binary format to be used by
                        KenLM
  --lm_decoder_cpu.max_supported_transcripts LM_DECODER_CPU.MAX_SUPPORTED_TRANSCRIPTS
  --lm_decoder_cpu.asr_model_delay LM_DECODER_CPU.ASR_MODEL_DELAY
                        (Advanced) Number of time steps by which the acoustic
                        model output should be shifted when computing
                        timestamps. This parameter must be tuned since the CTC
                        model is not guaranteed to predict correct alignment.
  --lm_decoder_cpu.language_model_alpha LM_DECODER_CPU.LANGUAGE_MODEL_ALPHA
  --lm_decoder_cpu.language_model_beta LM_DECODER_CPU.LANGUAGE_MODEL_BETA
  --lm_decoder_cpu.ms_per_timestep LM_DECODER_CPU.MS_PER_TIMESTEP
  --lm_decoder_cpu.vocab_file LM_DECODER_CPU.VOCAB_FILE
                        Vocab file to be used with decoder
  --lm_decoder_cpu.lexicon_file LM_DECODER_CPU.LEXICON_FILE
                        Lexicon file to be used with decoder
  --lm_decoder_cpu.beam_size LM_DECODER_CPU.BEAM_SIZE
                        Maximum number of hypothesis the decoder holds after
                        each step
  --lm_decoder_cpu.beam_size_token LM_DECODER_CPU.BEAM_SIZE_TOKEN
                        Maximum number of tokens the decoder considers at each
                        step
  --lm_decoder_cpu.beam_threshold LM_DECODER_CPU.BEAM_THRESHOLD
                        Threshold to prune hypothesis
  --lm_decoder_cpu.lm_weight LM_DECODER_CPU.LM_WEIGHT
                        Weight of language model
  --lm_decoder_cpu.word_insertion_score LM_DECODER_CPU.WORD_INSERTION_SCORE
                        Word insertion score
  --lm_decoder_cpu.forerunner_beam_size LM_DECODER_CPU.FORERUNNER_BEAM_SIZE
                        Maximum number of hypothesis the decoder holds after
                        each step, for forerunner transcript
  --lm_decoder_cpu.forerunner_beam_size_token LM_DECODER_CPU.FORERUNNER_BEAM_SIZE_TOKEN
                        Maximum number of tokens the decoder considers at each
                        step, for forerunner transcript
  --lm_decoder_cpu.forerunner_beam_threshold LM_DECODER_CPU.FORERUNNER_BEAM_THRESHOLD
                        Threshold to prune hypothesis, for forerunner
                        transcript
  --lm_decoder_cpu.smearing_mode LM_DECODER_CPU.SMEARING_MODE
                        Decoder smearing mode. Can be logadd, max or none
  --lm_decoder_cpu.forerunner_use_lm LM_DECODER_CPU.FORERUNNER_USE_LM
                        Bool that controls if the forerunner decoder should
                        use a language model