#!/usr/bin/env bash

# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
export PYTHONPATH=$PYTHONPATH:/workspace/slam/icefall_omni
set -eou pipefail

stage=$1
stop_stage=$2
# All files generated by this script are saved in "data".
# You can safely remove "data" and rerun this script to regenerate it.
mkdir -p data

log() {
  # This function is from espnet
  local fname=${BASH_SOURCE[1]##*/}
  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
}


if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
  log "stage 0: "
  pip uninstall lhotse
  cd /workspace/slam/lhotse
  git config --global --add safe.directory /workspace/slam/lhotse
  pip install -e '.[dev]'
  cd -
  pip install -r slam_omni/requirements.txt
fi

if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
  log "stage 1: Download whisper-large-v2 multi-hans-zh fbank feature from huggingface"

  python3 local/compute_whisper_fbank.py
fi


if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
  log "Stage 2: Combine features"
  manifest_dir=data/fbank
  if [ ! -f $manifest_dir/cuts_belle_00001-01600.jsonl.gz ]; then
    pieces=$(find $manifest_dir -name "cuts_belle.*.jsonl.gz" | sort)
    # # remove cust_belle_00000.jsonl.gz from pieces
    # pieces=$(echo $pieces | sed 's/cuts_belle.00000.jsonl.gz//g')
    echo $pieces | wc 
    lhotse combine $pieces data/fbank/cuts_belle_00001-01600.jsonl.gz
    cd $manifest_dir && ln -s cuts_belle_00001-01600.jsonl.gz cuts_belle_train.jsonl.gz && cd -
  fi
fi


if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
  log "stage 3: "
  exp_dir=./slam_omni/exp_speech2speech_rerun
  python3 ./slam_omni/decode.py \
    --max-duration 1 \
    --exp-dir $exp_dir \
    --speech-encoder-path-or-name models/whisper/v1.1/whisper-large-v2-multi-hans-zh-epoch-3-avg-10.pt  \
    --llm-path-or-name models/Qwen2.5-0.5B-Instruct \
    --epoch 997 --avg 1 \
    --manifest-dir data/fbank \
    --use-flash-attn True \
    --method small_test_speech2speech_rerun \
    --enable-speech-output True \
    --use-lora True # --on-the-fly-feats True

fi


if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
  log "stage 4: "
  ngpu=8
torchrun --nproc_per_node $ngpu ./slam_omni/train.py \
  --max-duration 80 \
  --enable-musan False \
  --exp-dir ./slam_omni/exp_speech2text \
  --speech-encoder-path-or-name models/whisper/v1.1/whisper-large-v2-multi-hans-zh-epoch-3-avg-10.pt \
  --llm-path-or-name models/Qwen2.5-0.5B-Instruct \
  --manifest-dir data/fbank \
  --deepspeed \
  --deepspeed_config ./slam_omni/ds_config_zero1.json \
  --use-flash-attn True \
  --pretrained-model-path slam_omni/exp_speech2text/epoch-1-checkpoint-5000.pt/pytorch_model.bin \
  --sampler-state-dict-path slam_omni/exp_speech2text/epoch-1-checkpoint-5000-sampler.pt \
  --use-lora True --unfreeze-llm True
fi


if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
  log "stage 5: "
  ngpu=8
  exp_dir=./slam_omni/exp_speech2speech_rerun
  # exp_dir_new=./slam_omni/exp_s2s
  torchrun --nproc_per_node $ngpu ./slam_omni/train.py \
    --max-duration 50 \
    --enable-musan False \
    --exp-dir $exp_dir \
    --speech-encoder-path-or-name models/whisper/v1.1/whisper-large-v2-multi-hans-zh-epoch-3-avg-10.pt \
    --llm-path-or-name models/Qwen2.5-0.5B-Instruct \
    --manifest-dir data/fbank \
    --deepspeed \
    --deepspeed_config ./slam_omni/ds_config_zero1.json \
    --use-flash-attn True \
    --pretrained-model-path $exp_dir/epoch-1-checkpoint-15000.pt/pytorch_model.bin \
    --sampler-state-dict-path $exp_dir/epoch-1-checkpoint-15000-sampler.pt \
    --use-lora True --unfreeze-llm True --unfreeze-speech-projector True --enable-speech-output True
    # --pretrained-model-path slam_omni/exp_speech2text/epoch-1-checkpoint-5000.pt/pytorch_model.bin \
    #   --sampler-state-dict-path $exp_dir/epoch-1-checkpoint-35000-sampler.pt \

fi