readme: ---
  language:
  - en
  tags:
  - esb
  datasets:
  - esb/datasets
  - revdotcom/earnings22
  ---
  To reproduce this run, first install NVIDIA NeMo according to the [official instructions](https://github.com/NVIDIA/NeMo#installation), then execute: 
  ```python 
  #!/usr/bin/env bash
  CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_rnnt.py \
          --config_path="conf/conformer_transducer_bpe_xlarge.yaml" \
          --model_name_or_path="stt_en_conformer_transducer_xlarge" \
          --dataset_name="esb/datasets" \
          --tokenizer_path="tokenizer" \
          --vocab_size="1024" \
          --max_steps="100000" \
          --dataset_config_name="earnings22" \
          --output_dir="./" \
          --run_name="conformer-rnnt-earnings22" \
          --wandb_project="rnnt" \
          --per_device_train_batch_size="8" \
          --per_device_eval_batch_size="4" \
          --logging_steps="50" \
          --learning_rate="1e-4" \
          --warmup_steps="500" \
          --save_strategy="steps" \
          --save_steps="20000" \
          --evaluation_strategy="steps" \
          --eval_steps="20000" \
          --report_to="wandb" \
          --preprocessing_num_workers="4" \
          --fused_batch_size="4" \
          --length_column_name="input_lengths" \
          --fuse_loss_wer \
          --group_by_length \
          --overwrite_output_dir \
          --do_train \
          --do_eval \
          --do_predict \
          --use_auth_token
  
  ```
  