#!/bin/bash

set -e
source path.sh

gpus=0,1
stage=0
stop_stage=100

conf_path=conf/default.yaml
train_output_path=exp/default
ckpt_name=snapshot_iter_153.pdz

# with the following command, you can choose the stage range you want to run
# such as `./run.sh --stage 0 --stop-stage 0`
# this can not be mixed use with `$1`, `$2` ...
source ${MAIN_ROOT}/utils/parse_options.sh || exit 1

if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
    # prepare data
    ./local/preprocess.sh ${conf_path} || exit -1
fi

if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
    # train model, all `ckpt` under `train_output_path/checkpoints/` dir
    CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
fi

if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
    # synthesize, vocoder is pwgan by default 0
    # use 1-4 to select the vocoder in {multi band melgan, style melgan, hifigan, wavernn}
    CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} 0 || exit -1
fi

if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
    # synthesize_e2e, vocoder is pwgan by default 0
    # use 1,3,4 to select the vocoder in {multi band melgan, hifigan, wavernn}
    CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_e2e.sh ${conf_path} ${train_output_path} ${ckpt_name} 0 || exit -1
fi

if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
    # inference with static model, vocoder is pwgan by default
    CUDA_VISIBLE_DEVICES=${gpus} ./local/inference.sh ${train_output_path} || exit -1
fi

# paddle2onnx, please make sure the static models are in ${train_output_path}/inference first
# we have only tested the following models so far
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
    # install paddle2onnx
    pip install paddle2onnx --upgrade
    ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_csmsc
    # considering the balance between speed and quality, we recommend that you use hifigan as vocoder
    ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx pwgan_csmsc
    # ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx mb_melgan_csmsc
    # ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx hifigan_csmsc
    
fi

# inference with onnxruntime
if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
    ./local/ort_predict.sh ${train_output_path}
fi

# must run after stage 3 (which stage generated static models)
if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
    # NOTE by yuantian 2022.11.21: please compile develop version of Paddle-Lite to export and run TTS models,
    #                   cause TTS models are supported by https://github.com/PaddlePaddle/Paddle-Lite/pull/9587 
    #                   and https://github.com/PaddlePaddle/Paddle-Lite/pull/9706
    ./local/export2lite.sh ${train_output_path} inference pdlite fastspeech2_csmsc x86
    ./local/export2lite.sh ${train_output_path} inference pdlite pwgan_csmsc x86
    # ./local/export2lite.sh ${train_output_path} inference pdlite mb_melgan_csmsc x86
    # ./local/export2lite.sh ${train_output_path} inference pdlite hifigan_csmsc x86
fi

if [ ${stage} -le 8 ] && [ ${stop_stage} -ge 8 ]; then
    CUDA_VISIBLE_DEVICES=${gpus} ./local/lite_predict.sh ${train_output_path} || exit -1
fi

# PTQ_dynamic
if [ ${stage} -le 9 ] && [ ${stop_stage} -ge 9 ]; then
    ./local/PTQ_dynamic.sh  ${train_output_path} fastspeech2_csmsc 8
    # ./local/PTQ_dynamic.sh  ${train_output_path} pwgan_csmsc 8
    # ./local/PTQ_dynamic.sh  ${train_output_path} mb_melgan_csmsc 8
    # ./local/PTQ_dynamic.sh  ${train_output_path} hifigan_csmsc 8
fi

# PTQ_static
if [ ${stage} -le 10 ] && [ ${stop_stage} -ge 10 ]; then
    CUDA_VISIBLE_DEVICES=${gpus} ./local/PTQ_static.sh  ${train_output_path} fastspeech2_csmsc || exit -1
fi
