#!/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 \