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run_cnndecoder.sh
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run_cnndecoder.sh
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#!/bin/bash
set -e
source path.sh
gpus=0,1
stage=0
stop_stage=100
conf_path=conf/cnndecoder.yaml
train_output_path=exp/cnndecoder
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 ] && [ ${stop_stage} -ge 2 ]; then
# synthesize, vocoder is pwgan
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
fi
# synthesize_e2e non-streaming
if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
# synthesize_e2e, vocoder is pwgan by default
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_e2e.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
fi
# inference non-streaming
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
# synthesize_e2e streaming
if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
# synthesize_e2e, vocoder is pwgan by default
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_streaming.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
fi
# inference streaming
if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
# inference with static model, vocoder is pwgan by default
CUDA_VISIBLE_DEVICES=${gpus} ./local/inference_streaming.sh ${train_output_path} || exit -1
fi
# paddle2onnx non streaming
if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; 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
# onnxruntime non streaming
if [ ${stage} -le 8 ] && [ ${stop_stage} -ge 8 ]; then
./local/ort_predict.sh ${train_output_path}
fi
# paddle2onnx streaming
if [ ${stage} -le 9 ] && [ ${stop_stage} -ge 9 ]; then
# install paddle2onnx
pip install paddle2onnx --upgrade
# streaming acoustic model
./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming fastspeech2_csmsc_am_encoder_infer
./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming fastspeech2_csmsc_am_decoder
./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming fastspeech2_csmsc_am_postnet
# considering the balance between speed and quality, we recommend that you use hifigan as vocoder
./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming pwgan_csmsc
# ./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming mb_melgan_csmsc
# ./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming hifigan_csmsc
fi
# onnxruntime streaming
if [ ${stage} -le 10 ] && [ ${stop_stage} -ge 10 ]; then
./local/ort_predict_streaming.sh ${train_output_path}
fi
# must run after stage 3 (which stage generated static models)
if [ ${stage} -le 11 ] && [ ${stop_stage} -ge 11 ]; then
./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 12 ] && [ ${stop_stage} -ge 12 ]; then
CUDA_VISIBLE_DEVICES=${gpus} ./local/lite_predict.sh ${train_output_path} || exit -1
fi
# must run after stage 5 (which stage generated static models)
if [ ${stage} -le 13 ] && [ ${stop_stage} -ge 13 ]; then
# streaming acoustic model
./local/export2lite.sh ${train_output_path} inference_streaming pdlite_streaming fastspeech2_csmsc_am_encoder_infer x86
./local/export2lite.sh ${train_output_path} inference_streaming pdlite_streaming fastspeech2_csmsc_am_decoder x86
./local/export2lite.sh ${train_output_path} inference_streaming pdlite_streaming fastspeech2_csmsc_am_postnet x86
./local/export2lite.sh ${train_output_path} inference_streaming pdlite_streaming pwgan_csmsc x86
# ./local/export2lite.sh ${train_output_path} inference_streaming pdlite_streaming mb_melgan_csmsc x86
# ./local/export2lite.sh ${train_output_path} inference_streaming pdlite_streaming hifigan_csmsc x86
fi
if [ ${stage} -le 14 ] && [ ${stop_stage} -ge 14 ]; then
CUDA_VISIBLE_DEVICES=${gpus} ./local/lite_predict_streaming.sh ${train_output_path} || exit -1
fi
# PTQ_static
if [ ${stage} -le 15 ] && [ ${stop_stage} -ge 15 ]; then
CUDA_VISIBLE_DEVICES=${gpus} ./local/PTQ_static.sh ${train_output_path} fastspeech2_csmsc || exit -1
fi