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#!/usr/bin/env bash |
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declare -a learning_rates=("1e-5" "3e-5" "1e-4" "3e-4" "1e-3") |
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declare -a batch_sizes=("8" "12" "14" "16") |
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declare -a gradient_accumulation_step_sizes=("2" "4" "8") |
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for learning_rate in "${learning_rates[@]}"; do |
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for batch_size in "${batch_sizes[@]}"; do |
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for gradient_accumulation_steps in "${gradient_accumulation_step_sizes[@]}"; do |
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python create_model.py |
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CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_seq2seq.py \ |
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--dataset_name="librispeech_asr" \ |
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--model_name_or_path="./" \ |
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--tokenizer_name="./" \ |
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--dataset_config_name="clean" \ |
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--train_split_name="train.100" \ |
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--eval_split_name="validation" \ |
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--output_dir="./" \ |
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--preprocessing_num_workers="1" \ |
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--length_column_name="input_length" \ |
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--overwrite_output_dir \ |
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--num_train_epochs="1" \ |
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--per_device_train_batch_size=$batch_size \ |
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--per_device_eval_batch_size=$batch_size \ |
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--gradient_accumulation_steps=$gradient_accumulation_steps \ |
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--generation_max_length="40" \ |
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--generation_num_beams="1" \ |
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--learning_rate=$learning_rate \ |
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--warmup_steps="500" \ |
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--evaluation_strategy="steps" \ |
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--text_column_name="text" \ |
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--save_steps="500" \ |
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--eval_steps="500" \ |
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--logging_steps="1" \ |
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--save_total_limit="1" \ |
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--freeze_feature_encoder \ |
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--gradient_checkpointing \ |
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--fp16 \ |
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--group_by_length \ |
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--predict_with_generate \ |
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--do_lower_case \ |
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--do_train \ |
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--do_eval \ |
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--push_to_hub \ |
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--use_auth_token |
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done |
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done |
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done |
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