What does this PR do ?

Batched beam search for CTC with NGPU-LM (n-gram LM on GPU).

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

Decoding Method	Beam Size	Beam Beta	LM Weight*	SLURP Test WER (%) ↓	RTFx ↑
greedy	–	–	–	19.93	491
beam_batch	12	1.0	0.7	16.15	456

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

Batch size 32, high fp32 matmul precision, sorted manifest, A5000 GPU. 6-gram LM is built on SLURP train texts.

Collection: [ASR]

Changelog

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

Usage

Usage without LM

python examples/asr/speech_to_text_eval.py \
   pretrained_name="nvidia/parakeet-ctc-1.1b" \
   dataset_manifest=<dataset_manifest>  \
   batch_size=32 \
   output_filename=decoded.jsonl \
   ctc_decoding.strategy="beam_batch" \
   ctc_decoding.beam.beam_size=12 \
   ctc_decoding.beam.beam_beta=0.3

Usage with LM

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-ctc-1.1b" \
      train_paths=["<train_manifest>"] \
      kenlm_bin_path=$KENLM_BIN_PATH \
      kenlm_model_file=parakeet-ctc-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-ctc-1.1b" \
   dataset_manifest=<dataset_manifest>  \
   batch_size=32 \
   output_filename=decoded.jsonl \
   ctc_decoding.strategy="beam_batch" \
   ctc_decoding.beam.ngram_lm_model="parakeet-ctc-1.1b_lm-o6.arpa.nemo" \
   ctc_decoding.beam.ngram_lm_alpha=0.4 \
   ctc_decoding.beam.beam_beta=0.4 \
   ctc_decoding.beam.beam_size=12

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Additional Information

Related to # (issue)

Apr 29 '25 21:04 lilithgrigoryan

There’s a known error, let me know if you want to proceed with merging this PR