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1#!/usr/bin/env bash
2python run_flax_speech_recognition_seq2seq.py \
3 --dataset_name="esc-benchmark/esc-datasets" \
4 --model_name_or_path="esc-benchmark/wav2vec2-aed-pretrained" \
5 --dataset_config_name="librispeech" \
6 --output_dir="./" \
7 --wandb_name="wav2vec2-aed-librispeech" \
8 --wandb_project="wav2vec2-aed" \
9 --per_device_train_batch_size="8" \
10 --per_device_eval_batch_size="2" \
11 --learning_rate="1e-4" \
12 --warmup_steps="500" \
13 --logging_steps="25" \
14 --max_steps="50001" \
15 --eval_steps="10000" \
16 --save_steps="10000" \
17 --generation_max_length="40" \
18 --generation_num_beams="1" \
19 --final_generation_max_length="300" \
20 --final_generation_num_beams="12" \
21 --generation_length_penalty="1.6" \
22 --hidden_dropout="0.2" \
23 --activation_dropout="0.2" \
24 --feat_proj_dropout="0.2" \
25 --overwrite_output_dir \
26 --gradient_checkpointing \
27 --freeze_feature_encoder \
28 --predict_with_generate \
29 --do_eval \
30 --do_train \
31 --do_predict \
32 --push_to_hub \
33 --use_auth_token
34