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export TRANSFORMERS_CACHE=/workspace/cache
export HF_HOME=/workspace/data_cache
python run_speech_recognition_seq2seq_streaming.py \
--model_name_or_path="openai/whisper-large-v2" \
--dataset_name="google/fleurs" \
--dataset_config_name="he_il" \
--language="Hebrew" \
--train_split_name="train+validation" \
--eval_split_name="test" \
--model_index_name="Whisper Large V2 Hebrew" \
--max_steps="200" \
--output_dir="./" \
--per_device_train_batch_size="64" \
--gradient_accumulation_steps="4" \
--per_device_eval_batch_size="16" \
--logging_steps="25" \
--learning_rate="1e-5" \
--warmup_steps="10" \
--evaluation_strategy="steps" \
--eval_steps="50" \
--save_strategy="steps" \
--save_steps="50" \
--generation_max_length="225" \
--length_column_name="input_length" \
--max_duration_in_seconds="30" \
--text_column_name="transcription" \
--freeze_feature_encoder="False" \
--report_to="tensorboard" \
--metric_for_best_model="wer" \
--greater_is_better="False" \
--load_best_model_at_end \
--gradient_checkpointing \
--fp16 \
--overwrite_output_dir \
--do_train \
--do_eval \
--predict_with_generate \
--do_normalize_eval \
--streaming \
--do_remove_punctuation \
--use_auth_token \
--push_to_hub |