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#!/usr/bin/env bash
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log_path=${LOG_PATH_INDEX_DIR:-$(pwd)} # benchmark系统指定该参数,不需要跑profile时,log_path指向存speed的目录
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stage=0
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stop_stage=100
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sed -i '/set\ -xe/d' run_benchmark.sh
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# 提供可稳定复现性能的脚本，默认在标准docker环境内py37执行： paddlepaddle/paddle:latest-gpu-cuda10.1-cudnn7 paddle=2.1.2 py=37
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# 执行目录：需说明
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cd ../../../
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# 1 安装该模型需要的依赖 (如需开启优化策略请注明)
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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sudo apt-get install libsndfile1
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pip install -e .
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pushd examples/csmsc/voc1
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source path.sh
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popd
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fi
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# 2 拷贝该模型需要数据、预训练模型
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# 下载 baker 数据集到 home 目录下并解压缩到 home 目录下
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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wget https://weixinxcxdb.oss-cn-beijing.aliyuncs.com/gwYinPinKu/BZNSYP.rar
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mkdir BZNSYP
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unrar x BZNSYP.rar BZNSYP
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wget https://paddlespeech.bj.bcebos.com/Parakeet/benchmark/durations.txt
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fi
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# 数据预处理
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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python paddlespeech/t2s/exps/gan_vocoder/preprocess.py --rootdir=BZNSYP/ --dumpdir=dump --num-cpu=20 --cut-sil=True --dur-file=durations.txt --config=examples/csmsc/voc1/conf/default.yaml
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python utils/compute_statistics.py --metadata=dump/train/raw/metadata.jsonl --field-name="feats"
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python paddlespeech/t2s/exps/gan_vocoder/normalize.py --metadata=dump/train/raw/metadata.jsonl --dumpdir=dump/train/norm --stats=dump/train/feats_stats.npy
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python paddlespeech/t2s/exps/gan_vocoder/normalize.py --metadata=dump/dev/raw/metadata.jsonl --dumpdir=dump/dev/norm --stats=dump/train/feats_stats.npy
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python paddlespeech/t2s/exps/gan_vocoder/normalize.py --metadata=dump/test/raw/metadata.jsonl --dumpdir=dump/test/norm --stats=dump/train/feats_stats.npy
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fi
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# 3 批量运行（如不方便批量，1，2需放到单个模型中）
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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model_mode_list=(pwgan)
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fp_item_list=(fp32)
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# 满 bs 是 26
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bs_item_list=(6)
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for model_mode in ${model_mode_list[@]}; do
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for fp_item in ${fp_item_list[@]}; do
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for bs_item in ${bs_item_list[@]}; do
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log_name=speech_${model_mode}_bs${bs_item}_${fp_item} # 如:clas_MobileNetv1_mp_bs32_fp32_8
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echo "index is speed, 1gpus, begin, ${log_name}"
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run_mode=sp
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CUDA_VISIBLE_DEVICES=0 bash tests/benchmark/pwgan/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 100 ${model_mode} | tee ${log_path}/${log_name}_speed_1gpus 2>&1 # (5min)
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sleep 60
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log_name=speech_${model_mode}_bs${bs_item}_${fp_item} # 如:clas_MobileNetv1_mp_bs32_fp32_8
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echo "index is speed, 8gpus, run_mode is multi_process, begin, ${log_name}"
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run_mode=mp
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CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash tests/benchmark/pwgan/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 100 ${model_mode} | tee ${log_path}/${log_name}_speed_8gpus8p 2>&1 #
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sleep 60
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done
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done
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done
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fi
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