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PaddleSpeech/examples/ljspeech/tts3/run.sh

69 lines
2.7 KiB

#!/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_201.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 ] && [ ${stop_stage} -ge 2 ]; then
# synthesize, vocoder is pwgan by default
CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
fi
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
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_ljspeech
# 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_ljspeech
# ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx hifigan_ljspeech
fi
# inference with onnxruntime, use fastspeech2 + pwgan by default
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
./local/export2lite.sh ${train_output_path} inference pdlite fastspeech2_ljspeech x86
./local/export2lite.sh ${train_output_path} inference pdlite pwgan_ljspeech x86
# ./local/export2lite.sh ${train_output_path} inference pdlite hifigan_ljspeech 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