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#!/bin/bash
MODEL_PATH=/data1/speech/anhnmt2/Speech2Speech/LLaMA-Omni/models/llm/Qwen2.5-3B-Instruct
SPEECH_ENCODER=/data1/speech/anhnmt2/Speech2Speech/LLaMA-Omni/models/speech_encoder/whisper-medium
PROMPT_VERSION=qwen
DATA_PATH=/data1/speech/anhnmt2/dataset/s2s/english/asr/dataset/train_asr_eng_5M.jsonl
DEV_PATH=/data1/speech/anhnmt2/dataset/s2s/english/asr/dataset/dev_asr_libri_spgi.jsonl
CACHE_DIR="../output/cached_asr_full"
AUGMENT_PATH="/data1/speech/anhnmt2/dataset/s2s/augment/noise_list_non_speech.txt"
deepspeed ../omni_speech/train/train_mem.py \
--deepspeed zero2.json \
--model_name_or_path $MODEL_PATH \
--version $PROMPT_VERSION \
--data_path $DATA_PATH \
--dev_path $DEV_PATH \
--cache_dir $CACHE_DIR \
--speech_encoder $SPEECH_ENCODER \
--mel_size 80 \
--speech_encoder_hidden_size 1024 \
--speech_encoder_type whisper \
--bf16 True \
--output_dir ../checkpoints/omni_whisper-medium_Qwen2.5-3B_pretrained-asr-5M \
--num_train_epochs 4 \
--tune_speech_projector True \
--per_device_train_batch_size 16 \
--per_device_eval_batch_size 4 \
--gradient_accumulation_steps 2 \
--evaluation_strategy "steps" \
--save_strategy "steps" \
--eval_steps 2000 \
--save_steps 2000 \
--save_total_limit 1 \
--learning_rate 1e-3 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--tf32 True \
--model_max_length 4096 \
--gradient_checkpointing True \
--dataloader_num_workers 8
# --augment_prob 0.2 \
# --augment_path $AUGMENT_PATH \
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