readme: ---
  language:
  - en
  tags:
  - esb
  datasets:
  - esb/datasets
  - ldc/switchboard
  ---
  To reproduce this run, first install Whisper from the Transformers compatible repo [patrickvonplaten/whisper](https://github.com/patrickvonplaten/whisper):
  ```
  pip install git+https://github.com/openai/whisper.git
  ```
  Then execute the command: 
  ```python 
  #!/usr/bin/env bash
  CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \
  	--model_name_or_path="medium.en" \
    --dataset_name="esb/datasets" \
    --dataset_config_name="switchboard" \
  	--max_steps="5000" \
  	--output_dir="./" \
  	--run_name="whisper-switchboard" \
  	--max_steps="5000" \
  	--output_dir="./" \
  	--run_name="whisper-switchboard" \
  	--wandb_project="whisper" \
  	--per_device_train_batch_size="64" \
  	--per_device_eval_batch_size="16" \
  	--logging_steps="25" \
  	--learning_rate="1e-4" \
  	--warmup_steps="500" \
  	--report_to="wandb" \
  	--preprocessing_num_workers="16" \
  	--evaluation_strategy="steps" \
  	--eval_steps="1000" \
  	--save_strategy="steps" \
  	--save_steps="1000" \
  	--generation_max_length="224" \
  	--length_column_name="input_lengths" \
  	--gradient_checkpointing \
  	--group_by_length \
  	--freeze_encoder \
  	--fp16 \
  	--overwrite_output_dir \
  	--do_train \
  	--do_eval \
  	--do_predict \
  	--predict_with_generate \
  	--use_auth_token
  
  ```
  