What does this PR do ?

Implements NGPU-LM (N-Gram LM on GPU) and transducer (RNN-T, TDT) greedy decoding with NGPU-LM.
Paper: "NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding" https://arxiv.org/abs/2505.22857

Results for nvidia/parakeet-rnnt-1.1b and nvidia/parakeet-tdt-1.1b on SLURP (out-of-domain):

*LM weight - optimal on SLURP dev set with step 0.1.

Batch size 32, high fp32 matmul precision, sorted manifest, A6000 GPU.
6-gram LM is built on SLURP train texts.

Overhead for using LM - less than 2% (with CUDA graphs).

Collection: [ASR]

Changelog

• Add specific line by line info of high level changes in this PR.

Usage

Step 1: Build LM for ASR Model (tokenizer-dependent) on domain texts

python nemo/scripts/asr_language_modeling/ngram_lm/train_kenlm.py \
      nemo_model_file="nvidia/parakeet-rnnt-1.1b" \
      train_paths=["<train_manifest>"] \
      kenlm_bin_path=$KENLM_BIN_PATH \
      kenlm_model_file=parakeet-rnnt-1.1b_lm-o6.arpa \
      ngram_length=6 \
      preserve_arpa=true \
      save_nemo=true

Step 2: Run Decoding with LM

python examples/asr/speech_to_text_eval.py \
   pretrained_name="nvidia/parakeet-rnnt-1.1b" \
   dataset_manifest=<dataset_manifest>  \
   batch_size=32 \
   output_filename=decoded.jsonl \
   rnnt_decoding.strategy="greedy_batch" \
   rnnt_decoding.greedy.ngram_lm_model="parakeet-rnnt-1.1b_lm-o6.arpa.nemo" \
   rnnt_decoding.greedy.ngram_lm_alpha=0.5

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