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123 lines
5.5 KiB
123 lines
5.5 KiB
#!/bin/bash
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set -e
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source path.sh
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gpus=0,1
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stage=0
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stop_stage=100
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conf_path=conf/cnndecoder.yaml
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train_output_path=exp/cnndecoder
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ckpt_name=snapshot_iter_153.pdz
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# with the following command, you can choose the stage range you want to run
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# such as `./run.sh --stage 0 --stop-stage 0`
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# this can not be mixed use with `$1`, `$2` ...
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source ${MAIN_ROOT}/utils/parse_options.sh || exit 1
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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# prepare data
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./local/preprocess.sh ${conf_path} || exit -1
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fi
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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# train model, all `ckpt` under `train_output_path/checkpoints/` dir
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CUDA_VISIBLE_DEVICES=${gpus} ./local/train.sh ${conf_path} ${train_output_path} || exit -1
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fi
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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# synthesize, vocoder is pwgan
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CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
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fi
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# synthesize_e2e non-streaming
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if [ ${stage} -le 3 ] && [ ${stop_stage} -ge 3 ]; then
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# synthesize_e2e, vocoder is pwgan by default
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CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_e2e.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
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fi
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# inference non-streaming
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if [ ${stage} -le 4 ] && [ ${stop_stage} -ge 4 ]; then
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# inference with static model, vocoder is pwgan by default
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CUDA_VISIBLE_DEVICES=${gpus} ./local/inference.sh ${train_output_path} || exit -1
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fi
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# synthesize_e2e streaming
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if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
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# synthesize_e2e, vocoder is pwgan by default
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CUDA_VISIBLE_DEVICES=${gpus} ./local/synthesize_streaming.sh ${conf_path} ${train_output_path} ${ckpt_name} || exit -1
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fi
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# inference streaming
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if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
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# inference with static model, vocoder is pwgan by default
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CUDA_VISIBLE_DEVICES=${gpus} ./local/inference_streaming.sh ${train_output_path} || exit -1
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fi
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# paddle2onnx non streaming
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if [ ${stage} -le 7 ] && [ ${stop_stage} -ge 7 ]; then
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# install paddle2onnx
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pip install paddle2onnx --upgrade
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./local/paddle2onnx.sh ${train_output_path} inference inference_onnx fastspeech2_csmsc
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# considering the balance between speed and quality, we recommend that you use hifigan as vocoder
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./local/paddle2onnx.sh ${train_output_path} inference inference_onnx pwgan_csmsc
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# ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx mb_melgan_csmsc
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# ./local/paddle2onnx.sh ${train_output_path} inference inference_onnx hifigan_csmsc
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fi
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# onnxruntime non streaming
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if [ ${stage} -le 8 ] && [ ${stop_stage} -ge 8 ]; then
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./local/ort_predict.sh ${train_output_path}
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fi
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# paddle2onnx streaming
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if [ ${stage} -le 9 ] && [ ${stop_stage} -ge 9 ]; then
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# install paddle2onnx
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pip install paddle2onnx --upgrade
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# streaming acoustic model
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./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming fastspeech2_csmsc_am_encoder_infer
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./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming fastspeech2_csmsc_am_decoder
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./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming fastspeech2_csmsc_am_postnet
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# considering the balance between speed and quality, we recommend that you use hifigan as vocoder
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./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming pwgan_csmsc
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# ./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming mb_melgan_csmsc
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# ./local/paddle2onnx.sh ${train_output_path} inference_streaming inference_onnx_streaming hifigan_csmsc
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fi
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# onnxruntime streaming
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if [ ${stage} -le 10 ] && [ ${stop_stage} -ge 10 ]; then
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./local/ort_predict_streaming.sh ${train_output_path}
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fi
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# must run after stage 3 (which stage generated static models)
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if [ ${stage} -le 11 ] && [ ${stop_stage} -ge 11 ]; then
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./local/export2lite.sh ${train_output_path} inference pdlite fastspeech2_csmsc x86
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./local/export2lite.sh ${train_output_path} inference pdlite pwgan_csmsc x86
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# ./local/export2lite.sh ${train_output_path} inference pdlite mb_melgan_csmsc x86
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# ./local/export2lite.sh ${train_output_path} inference pdlite hifigan_csmsc x86
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fi
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if [ ${stage} -le 12 ] && [ ${stop_stage} -ge 12 ]; then
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CUDA_VISIBLE_DEVICES=${gpus} ./local/lite_predict.sh ${train_output_path} || exit -1
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fi
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# must run after stage 5 (which stage generated static models)
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if [ ${stage} -le 13 ] && [ ${stop_stage} -ge 13 ]; then
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# streaming acoustic model
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./local/export2lite.sh ${train_output_path} inference_streaming pdlite_streaming fastspeech2_csmsc_am_encoder_infer x86
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./local/export2lite.sh ${train_output_path} inference_streaming pdlite_streaming fastspeech2_csmsc_am_decoder x86
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./local/export2lite.sh ${train_output_path} inference_streaming pdlite_streaming fastspeech2_csmsc_am_postnet x86
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./local/export2lite.sh ${train_output_path} inference_streaming pdlite_streaming pwgan_csmsc x86
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# ./local/export2lite.sh ${train_output_path} inference_streaming pdlite_streaming mb_melgan_csmsc x86
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# ./local/export2lite.sh ${train_output_path} inference_streaming pdlite_streaming hifigan_csmsc x86
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fi
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if [ ${stage} -le 14 ] && [ ${stop_stage} -ge 14 ]; then
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CUDA_VISIBLE_DEVICES=${gpus} ./local/lite_predict_streaming.sh ${train_output_path} || exit -1
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fi
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# PTQ_static
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if [ ${stage} -le 15 ] && [ ${stop_stage} -ge 15 ]; then
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CUDA_VISIBLE_DEVICES=${gpus} ./local/PTQ_static.sh ${train_output_path} fastspeech2_csmsc || exit -1
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fi |