#!/usr/bin/env bash
torchrun --nproc_per_node 3 examples/pytorch/speech-recognition/run_speech_recognition_ctc.py
          --dataset_name="librispeech_asr" \
          --model_name_or_path="facebook/w2v-bert-2.0" \   
          --dataset_config_name="clean" \
          --eval_split_name="test" \
          --train_split_name="train.100" \        
          --output_dir="./wav2vec2-bert-CV16-en-libri" \ 
          --num_train_epochs="7" \       
          --per_device_train_batch_size="12" \ 
          --gradient_accumulation_steps="2" \          
          --per_device_eval_batch_size="12" \      
          --learning_rate="3e-5" \      
          --warmup_steps="10000" \  
          --evaluation_strategy="steps" \        
          --text_column_name="text" \      
          --save_steps="500" \     
          --eval_steps="250" \            
          --save_total_limit="3" \ 
          --gradient_checkpointing \               
          --chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” \
          --fp16  --push_to_hub \         
          --do_train --do_eval  \  
          --eval_metrics "wer" "cer" \             
          --freeze_feature_encoder false  --logging_steps "5" \
          --add_adapter true \     
          --preprocessing_num_workers "32" \           
          --mask_time_prob="0.0"      --mask_feature_prob="0.0" \
          --tokenizer_name_or_path "jonatasgrosman/wav2vec2-large-xlsr-53-english" \
          --eval_accumulation_steps "2"     --group_by_length --length_column_name="input_length" \
          --layerdrop="0.0" \       
          --hidden_dropout="0.05" --activation_dropout="0.05" --feat_proj_dropout="0.05"