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Saving best state, step 500, val wer 10.171
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#!/usr/bin/env bash
accelerate launch run_distillation.py \
--model_name_or_path "./end-of-training-weights" \
--teacher_model_name_or_path "NbAiLab/nb-whisper-large" \
--train_dataset_name "NbAiLab/annotated_distil_raw_ncc_speech_v7_large" \
--train_dataset_config_name "" \
--train_split_name "train" \
--eval_dataset_name "NbAiLab/ncc_speech_v7" \
--eval_dataset_config_name "" \
--eval_split_name "validation_norwegian_fleurs" \
--eval_steps 500 \
--save_steps 1000 \
--warmup_steps 1000 \
--learning_rate 0.00015 \
--lr_scheduler_type "linear" \
--timestamp_probability 0.2 \
--condition_on_prev_probability 0.2 \
--language "no" \
--task "transcribe" \
--logging_steps 200 \
--save_total_limit 1 \
--max_steps 50000 \
--wer_threshold 10 \
--per_device_train_batch_size 32 \
--per_device_eval_batch_size 32 \
--dataloader_num_workers 8 \
--preprocessing_num_workers 8 \
--ddp_timeout 7200 \
--dtype "bfloat16" \
--attn_implementation "sdpa" \
--output_dir "./" \
--do_train \
--do_eval \
--gradient_checkpointing \
--overwrite_output_dir \
--predict_with_generate \
--freeze_encoder \
--freeze_embed_positions \
--streaming True \
--wandb_project "nb-distil-whisper-large-fleurseval" \
--wandb_name "pytorch_lr3e4_wer10" \
--hub_model_id "NbAiLab/nb-distil-whisper-large-pytorch-wer1-recover33k-38-5k-decay-last50k" \
--push_to_hub