Create run.sh

run.sh

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+python -m torch.distributed.launch \
+    --nproc_per_node=8 \
+    run_xtreme_s.py \
+    --model_name_or_path="facebook/wav2vec2-xls-r-300m" \
+    --task="fleurs-asr" \
+    --language="en_us" \
+    --language_group="western_european_we" \
+    --output_dir="xtreme_s_xlsr_300m_fleurs_asr_western_european_nomask" \
+    --overwrite_output_dir \
+    --num_train_epochs=20 \
+    --per_device_train_batch_size=8 \
+    --per_device_eval_batch_size=1 \
+    --gradient_accumulation_steps=1 \
+    --eval_accumulation_steps=10 \
+    --learning_rate="3e-4" \
+    --ctc_zero_infinity \
+    --warmup_steps=1000 \
+    --evaluation_strategy="steps" \
+    --max_duration_in_seconds=20 \
+    --preprocessing_num_workers=16 \
+    --save_steps=500 \
+    --eval_steps=500 \
+    --logging_steps=1 \
+    --layerdrop=0.0 \
+    --mask_time_prob=0.05 \
+    --mask_time_length=10 \
+    --mask_feature_prob=0.05 \
+    --mask_feature_length=64 \
+    --freeze_feature_encoder \
+    --gradient_checkpointing \
+    --fp16 \
+    --fp16_full_eval \
+    --group_by_length \
+    --do_train \
+    --do_eval \
+    --do_predict \
+    --metric_for_best_model="wer" \
+    --greater_is_better=False \
+    --load_best_model_at_end \
+    --push_to_hub