#!/bin/bash
# Copyright 2024 Alibaba Inc. All Rights Reserved.

stage=-1
stop_stage=3

data_url=www.openslr.org/resources/60
data_dir=data
pretrained_model_dir=./pretrained_models/CosyVoice2-0.5B

if [ ${stage} -le -1 ] && [ ${stop_stage} -ge -1 ]; then
  echo "Data Download"
  for part in test-clean; do
    local/download_and_untar.sh ${data_dir} ${data_url} ${part}
  done
fi

# if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
#   echo "Data preparation, prepare wav.scp/text/utt2spk/spk2utt"
#   for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do
#     mkdir -p data/$x
#     python local/prepare_data.py --src_dir $data_dir/LibriTTS/$x --des_dir data/$x
#   done
# fi

# if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
#   echo "Extract campplus speaker embedding, you will get spk2embedding.pt and utt2embedding.pt in data/$x dir"
#   for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do
#     tools/extract_embedding.py --dir data/$x \
#       --onnx_path $pretrained_model_dir/campplus.onnx
#   done
# fi

# if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
#   echo "Extract discrete speech token, you will get utt2speech_token.pt in data/$x dir"
#   for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do
#     tools/extract_speech_token.py --dir data/$x \
#       --onnx_path $pretrained_model_dir/speech_tokenizer_v2.onnx
#   done
# fi

# if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
#   echo "Prepare required parquet format data, you should have prepared wav.scp/text/utt2spk/spk2utt/utt2embedding.pt/spk2embedding.pt/utt2speech_token.pt"
#   for x in train-clean-100 train-clean-360 train-other-500 dev-clean dev-other test-clean test-other; do
#     mkdir -p data/$x/parquet
#     tools/make_parquet_list.py --num_utts_per_parquet 1000 \
#       --num_processes 10 \
#       --src_dir data/$x \
#       --des_dir data/$x/parquet
#   done
# fi

# # train llm
# export CUDA_VISIBLE_DEVICES="0,1,2,3"
# num_gpus=$(echo $CUDA_VISIBLE_DEVICES | awk -F "," '{print NF}')
# job_id=1986
# dist_backend="nccl"
# num_workers=2
# prefetch=100
# train_engine=torch_ddp
# if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
#   echo "Run train. We only support llm traning for now. If your want to train from scratch, please use conf/cosyvoice.fromscratch.yaml"
#   if [ $train_engine == 'deepspeed' ]; then
#     echo "Notice deepspeed has its own optimizer config. Modify conf/ds_stage2.json if necessary"
#   fi
#   cat data/{train-clean-100,train-clean-360,train-other-500}/parquet/data.list > data/train.data.list
#   cat data/{dev-clean,dev-other}/parquet/data.list > data/dev.data.list
#   # NOTE will update llm/hift training later
#   for model in llm flow hifigan; do
#     torchrun --nnodes=1 --nproc_per_node=$num_gpus \
#         --rdzv_id=$job_id --rdzv_backend="c10d" --rdzv_endpoint="localhost:1234" \
#       cosyvoice/bin/train.py \
#       --train_engine $train_engine \
#       --config conf/cosyvoice2.yaml \
#       --train_data data/train.data.list \
#       --cv_data data/dev.data.list \
#       --qwen_pretrain_path $pretrained_model_dir/CosyVoice-BlankEN \
#       --model $model \
#       --checkpoint $pretrained_model_dir/$model.pt \
#       --model_dir `pwd`/exp/cosyvoice2/$model/$train_engine \
#       --tensorboard_dir `pwd`/tensorboard/cosyvoice2/$model/$train_engine \
#       --ddp.dist_backend $dist_backend \
#       --num_workers ${num_workers} \
#       --prefetch ${prefetch} \
#       --pin_memory \
#       --use_amp \
#       --deepspeed_config ./conf/ds_stage2.json \
#       --deepspeed.save_states model+optimizer
#   done
# fi

# # average model
# average_num=5
# if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
#   for model in llm flow hifigan; do
#     decode_checkpoint=`pwd`/exp/cosyvoice/$model/$train_engine/${model}.pt
#     echo "do model average and final checkpoint is $decode_checkpoint"
#     python cosyvoice/bin/average_model.py \
#       --dst_model $decode_checkpoint \
#       --src_path `pwd`/exp/cosyvoice/$model/$train_engine  \
#       --num ${average_num} \
#       --val_best
#   done
# fi

# if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
#   echo "Export your model for inference speedup. Remember copy your llm or flow model to model_dir"
#   python cosyvoice/bin/export_jit.py --model_dir $pretrained_model_dir
#   python cosyvoice/bin/export_onnx.py --model_dir $pretrained_model_dir
# fi