[TTS]Add TTS Paddle-Lite x86 inference (#2667)
* Add export2lite, test=tts * add tts paddlelite x86 inference, test=tts * update released_model.md, test=tts * add paddlelite in setup.py * updatepull/2670/head
TianYuan
2 years ago
committed by
GitHub
parent
1c3d2cb89e
commit
0b4cf2211d
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#!/bin/bash
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train_output_path=$1
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stage=0
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stop_stage=0
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# pwgan
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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python3 ${BIN_DIR}/../lite_predict.py \
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--inference_dir=${train_output_path}/pdlite \
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--am=fastspeech2_aishell3 \
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--voc=pwgan_aishell3 \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/lite_infer_out \
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--phones_dict=dump/phone_id_map.txt \
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--speaker_dict=dump/speaker_id_map.txt \
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--spk_id=0
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fi
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# hifigan
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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python3 ${BIN_DIR}/../lite_predict.py \
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--inference_dir=${train_output_path}/pdlite \
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--am=fastspeech2_aishell3 \
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--voc=hifigan_aishell3 \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/lite_infer_out \
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--phones_dict=dump/phone_id_map.txt \
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--speaker_dict=dump/speaker_id_map.txt \
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--spk_id=0
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fi
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#!/bin/bash
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train_output_path=$1
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stage=0
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stop_stage=0
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# pwgan
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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python3 ${BIN_DIR}/../lite_predict.py \
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--inference_dir=${train_output_path}/pdlite \
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--am=speedyspeech_csmsc \
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--voc=pwgan_csmsc \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/lite_infer_out \
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--phones_dict=dump/phone_id_map.txt \
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--tones_dict=dump/tone_id_map.txt
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fi
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# for more GAN Vocoders
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# multi band melgan
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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python3 ${BIN_DIR}/../lite_predict.py \
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--inference_dir=${train_output_path}/pdlite \
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--am=speedyspeech_csmsc \
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--voc=mb_melgan_csmsc \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/lite_infer_out \
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--phones_dict=dump/phone_id_map.txt \
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--tones_dict=dump/tone_id_map.txt
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fi
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# hifigan
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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python3 ${BIN_DIR}/../lite_predict.py \
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--inference_dir=${train_output_path}/pdlite \
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--am=speedyspeech_csmsc \
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--voc=hifigan_csmsc \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/lite_infer_out \
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--phones_dict=dump/phone_id_map.txt \
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--tones_dict=dump/tone_id_map.txt
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fi
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#!/bin/bash
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train_output_path=$1
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stage=0
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stop_stage=0
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# pwgan
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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python3 ${BIN_DIR}/../lite_predict.py \
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--inference_dir=${train_output_path}/pdlite \
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--am=fastspeech2_csmsc \
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--voc=pwgan_csmsc \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/lite_infer_out \
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--phones_dict=dump/phone_id_map.txt
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fi
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# for more GAN Vocoders
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# multi band melgan
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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python3 ${BIN_DIR}/../lite_predict.py \
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--inference_dir=${train_output_path}/pdlite \
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--am=fastspeech2_csmsc \
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--voc=mb_melgan_csmsc \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/lite_infer_out \
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--phones_dict=dump/phone_id_map.txt
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fi
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# hifigan
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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python3 ${BIN_DIR}/../lite_predict.py \
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--inference_dir=${train_output_path}/pdlite \
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--am=fastspeech2_csmsc \
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--voc=hifigan_csmsc \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/lite_infer_out \
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--phones_dict=dump/phone_id_map.txt
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fi
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#!/bin/bash
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train_output_path=$1
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stage=0
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stop_stage=0
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# pwgan
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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python3 ${BIN_DIR}/../lite_predict_streaming.py \
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--inference_dir=${train_output_path}/pdlite_streaming \
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--am=fastspeech2_csmsc \
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--am_stat=dump/train/speech_stats.npy \
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--voc=pwgan_csmsc \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/lite_infer_out_streaming \
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--phones_dict=dump/phone_id_map.txt \
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--am_streaming=True
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fi
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# for more GAN Vocoders
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# multi band melgan
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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python3 ${BIN_DIR}/../lite_predict_streaming.py \
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--inference_dir=${train_output_path}/pdlite_streaming \
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--am=fastspeech2_csmsc \
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--am_stat=dump/train/speech_stats.npy \
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--voc=mb_melgan_csmsc \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/lite_infer_out_streaming \
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--phones_dict=dump/phone_id_map.txt \
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--am_streaming=True
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fi
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# hifigan
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if [ ${stage} -le 2 ] && [ ${stop_stage} -ge 2 ]; then
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python3 ${BIN_DIR}/../lite_predict_streaming.py \
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--inference_dir=${train_output_path}/pdlite_streaming \
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--am=fastspeech2_csmsc \
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--am_stat=dump/train/speech_stats.npy \
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--voc=hifigan_csmsc \
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--text=${BIN_DIR}/../sentences.txt \
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--output_dir=${train_output_path}/lite_infer_out_streaming \
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--phones_dict=dump/phone_id_map.txt \
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--am_streaming=True
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fi
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#!/bin/bash
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train_output_path=$1
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stage=0
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stop_stage=0
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# pwgan
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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python3 ${BIN_DIR}/../lite_predict.py \
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--inference_dir=${train_output_path}/pdlite \
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--am=fastspeech2_ljspeech \
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--voc=pwgan_ljspeech \
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--text=${BIN_DIR}/../sentences_en.txt \
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--output_dir=${train_output_path}/lite_infer_out \
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--phones_dict=dump/phone_id_map.txt \
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--lang=en
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fi
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# hifigan
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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python3 ${BIN_DIR}/../lite_predict.py \
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--inference_dir=${train_output_path}/pdlite \
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--am=fastspeech2_ljspeech \
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--voc=hifigan_ljspeech \
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--text=${BIN_DIR}/../sentences_en.txt \
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--output_dir=${train_output_path}/lite_infer_out \
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--phones_dict=dump/phone_id_map.txt \
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--lang=en
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fi
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#!/bin/bash
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train_output_path=$1
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stage=0
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stop_stage=0
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# pwgan
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if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
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python3 ${BIN_DIR}/../lite_predict.py \
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--inference_dir=${train_output_path}/pdlite \
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--am=fastspeech2_vctk \
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--voc=pwgan_vctk \
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--text=${BIN_DIR}/../sentences_en.txt \
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--output_dir=${train_output_path}/lite_infer_out \
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--phones_dict=dump/phone_id_map.txt \
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--speaker_dict=dump/speaker_id_map.txt \
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--spk_id=0 \
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--lang=en
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fi
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# hifigan
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if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
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python3 ${BIN_DIR}/../lite_predict.py \
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--inference_dir=${train_output_path}/pdlite \
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--am=fastspeech2_vctk \
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--voc=hifigan_vctk \
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--text=${BIN_DIR}/../sentences_en.txt \
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--output_dir=${train_output_path}/lite_infer_out \
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--phones_dict=dump/phone_id_map.txt \
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--speaker_dict=dump/speaker_id_map.txt \
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--spk_id=0 \
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--lang=en
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fi
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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from pathlib import Path
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import soundfile as sf
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from timer import timer
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from paddlespeech.t2s.exps.syn_utils import get_frontend
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from paddlespeech.t2s.exps.syn_utils import get_lite_am_output
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from paddlespeech.t2s.exps.syn_utils import get_lite_predictor
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from paddlespeech.t2s.exps.syn_utils import get_lite_voc_output
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from paddlespeech.t2s.exps.syn_utils import get_sentences
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def parse_args():
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parser = argparse.ArgumentParser(
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description="Paddle Infernce with acoustic model & vocoder.")
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# acoustic model
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parser.add_argument(
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'--am',
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type=str,
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default='fastspeech2_csmsc',
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choices=[
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'speedyspeech_csmsc',
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'fastspeech2_csmsc',
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'fastspeech2_aishell3',
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'fastspeech2_ljspeech',
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'fastspeech2_vctk',
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'fastspeech2_mix',
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],
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help='Choose acoustic model type of tts task.')
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parser.add_argument(
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"--phones_dict", type=str, default=None, help="phone vocabulary file.")
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parser.add_argument(
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"--tones_dict", type=str, default=None, help="tone vocabulary file.")
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parser.add_argument(
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"--speaker_dict", type=str, default=None, help="speaker id map file.")
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parser.add_argument(
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'--spk_id',
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type=int,
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default=0,
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help='spk id for multi speaker acoustic model')
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# voc
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parser.add_argument(
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'--voc',
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type=str,
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default='pwgan_csmsc',
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choices=[
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'pwgan_csmsc',
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'pwgan_aishell3',
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'pwgan_ljspeech',
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'pwgan_vctk',
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'mb_melgan_csmsc',
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'hifigan_csmsc',
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'hifigan_aishell3',
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'hifigan_ljspeech',
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'hifigan_vctk',
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],
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help='Choose vocoder type of tts task.')
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# other
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parser.add_argument(
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'--lang',
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type=str,
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default='zh',
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help='Choose model language. zh or en or mix')
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parser.add_argument(
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"--text",
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type=str,
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help="text to synthesize, a 'utt_id sentence' pair per line")
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parser.add_argument(
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"--inference_dir", type=str, help="dir to save inference models")
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parser.add_argument("--output_dir", type=str, help="output dir")
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args, _ = parser.parse_known_args()
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return args
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# only inference for models trained with csmsc now
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def main():
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args = parse_args()
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# frontend
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frontend = get_frontend(
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lang=args.lang,
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phones_dict=args.phones_dict,
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tones_dict=args.tones_dict)
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# am_predictor
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am_predictor = get_lite_predictor(
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model_dir=args.inference_dir, model_file=args.am + "_x86.nb")
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# model: {model_name}_{dataset}
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am_dataset = args.am[args.am.rindex('_') + 1:]
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# voc_predictor
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voc_predictor = get_lite_predictor(
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model_dir=args.inference_dir, model_file=args.voc + "_x86.nb")
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output_dir = Path(args.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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sentences = get_sentences(text_file=args.text, lang=args.lang)
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merge_sentences = True
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fs = 24000 if am_dataset != 'ljspeech' else 22050
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# warmup
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for utt_id, sentence in sentences[:3]:
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with timer() as t:
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mel = get_lite_am_output(
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input=sentence,
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am_predictor=am_predictor,
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am=args.am,
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frontend=frontend,
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lang=args.lang,
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merge_sentences=merge_sentences,
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speaker_dict=args.speaker_dict,
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spk_id=args.spk_id, )
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wav = get_lite_voc_output(voc_predictor=voc_predictor, input=mel)
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speed = wav.size / t.elapse
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rtf = fs / speed
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print(
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f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
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)
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print("warm up done!")
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N = 0
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T = 0
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for utt_id, sentence in sentences:
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with timer() as t:
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mel = get_lite_am_output(
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input=sentence,
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am_predictor=am_predictor,
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am=args.am,
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frontend=frontend,
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lang=args.lang,
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merge_sentences=merge_sentences,
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speaker_dict=args.speaker_dict,
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spk_id=args.spk_id, )
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wav = get_lite_voc_output(voc_predictor=voc_predictor, input=mel)
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N += wav.size
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T += t.elapse
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speed = wav.size / t.elapse
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rtf = fs / speed
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sf.write(output_dir / (utt_id + ".wav"), wav, samplerate=fs)
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print(
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f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
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)
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print(f"{utt_id} done!")
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print(f"generation speed: {N / T}Hz, RTF: {fs / (N / T) }")
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if __name__ == "__main__":
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main()
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@ -0,0 +1,230 @@
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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from pathlib import Path
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import numpy as np
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import soundfile as sf
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from timer import timer
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from paddlespeech.t2s.exps.syn_utils import denorm
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from paddlespeech.t2s.exps.syn_utils import get_chunks
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from paddlespeech.t2s.exps.syn_utils import get_frontend
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from paddlespeech.t2s.exps.syn_utils import get_lite_am_sublayer_output
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from paddlespeech.t2s.exps.syn_utils import get_lite_predictor
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from paddlespeech.t2s.exps.syn_utils import get_lite_streaming_am_output
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from paddlespeech.t2s.exps.syn_utils import get_lite_voc_output
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from paddlespeech.t2s.exps.syn_utils import get_sentences
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from paddlespeech.t2s.exps.syn_utils import run_frontend
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from paddlespeech.t2s.utils import str2bool
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def parse_args():
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parser = argparse.ArgumentParser(
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description="Paddle Infernce with acoustic model & vocoder.")
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# acoustic model
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||||
parser.add_argument(
|
||||
'--am',
|
||||
type=str,
|
||||
default='fastspeech2_csmsc',
|
||||
choices=['fastspeech2_csmsc'],
|
||||
help='Choose acoustic model type of tts task.')
|
||||
parser.add_argument(
|
||||
"--am_stat",
|
||||
type=str,
|
||||
default=None,
|
||||
help="mean and standard deviation used to normalize spectrogram when training acoustic model."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--phones_dict", type=str, default=None, help="phone vocabulary file.")
|
||||
parser.add_argument(
|
||||
"--tones_dict", type=str, default=None, help="tone vocabulary file.")
|
||||
parser.add_argument(
|
||||
"--speaker_dict", type=str, default=None, help="speaker id map file.")
|
||||
parser.add_argument(
|
||||
'--spk_id',
|
||||
type=int,
|
||||
default=0,
|
||||
help='spk id for multi speaker acoustic model')
|
||||
# voc
|
||||
parser.add_argument(
|
||||
'--voc',
|
||||
type=str,
|
||||
default='pwgan_csmsc',
|
||||
choices=['pwgan_csmsc', 'mb_melgan_csmsc', 'hifigan_csmsc'],
|
||||
help='Choose vocoder type of tts task.')
|
||||
# other
|
||||
parser.add_argument(
|
||||
'--lang',
|
||||
type=str,
|
||||
default='zh',
|
||||
help='Choose model language. zh or en')
|
||||
parser.add_argument(
|
||||
"--text",
|
||||
type=str,
|
||||
help="text to synthesize, a 'utt_id sentence' pair per line")
|
||||
parser.add_argument(
|
||||
"--inference_dir", type=str, help="dir to save inference models")
|
||||
parser.add_argument("--output_dir", type=str, help="output dir")
|
||||
# inference
|
||||
|
||||
# streaming related
|
||||
parser.add_argument(
|
||||
"--am_streaming",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="whether use streaming acoustic model")
|
||||
parser.add_argument(
|
||||
"--block_size", type=int, default=42, help="block size of am streaming")
|
||||
parser.add_argument(
|
||||
"--pad_size", type=int, default=12, help="pad size of am streaming")
|
||||
|
||||
args, _ = parser.parse_known_args()
|
||||
return args
|
||||
|
||||
|
||||
# only inference for models trained with csmsc now
|
||||
def main():
|
||||
args = parse_args()
|
||||
|
||||
# frontend
|
||||
frontend = get_frontend(
|
||||
lang=args.lang,
|
||||
phones_dict=args.phones_dict,
|
||||
tones_dict=args.tones_dict)
|
||||
|
||||
# am_predictor
|
||||
am_encoder_infer_predictor = get_lite_predictor(
|
||||
model_dir=args.inference_dir,
|
||||
model_file=args.am + "_am_encoder_infer" + "_x86.nb")
|
||||
am_decoder_predictor = get_lite_predictor(
|
||||
model_dir=args.inference_dir,
|
||||
model_file=args.am + "_am_decoder" + "_x86.nb")
|
||||
am_postnet_predictor = get_lite_predictor(
|
||||
model_dir=args.inference_dir,
|
||||
model_file=args.am + "_am_postnet" + "_x86.nb")
|
||||
am_mu, am_std = np.load(args.am_stat)
|
||||
# model: {model_name}_{dataset}
|
||||
am_dataset = args.am[args.am.rindex('_') + 1:]
|
||||
|
||||
# voc_predictor
|
||||
voc_predictor = get_lite_predictor(
|
||||
model_dir=args.inference_dir, model_file=args.voc + "_x86.nb")
|
||||
|
||||
output_dir = Path(args.output_dir)
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
sentences = get_sentences(text_file=args.text, lang=args.lang)
|
||||
|
||||
merge_sentences = True
|
||||
|
||||
fs = 24000 if am_dataset != 'ljspeech' else 22050
|
||||
# warmup
|
||||
for utt_id, sentence in sentences[:3]:
|
||||
with timer() as t:
|
||||
normalized_mel = get_lite_streaming_am_output(
|
||||
input=sentence,
|
||||
am_encoder_infer_predictor=am_encoder_infer_predictor,
|
||||
am_decoder_predictor=am_decoder_predictor,
|
||||
am_postnet_predictor=am_postnet_predictor,
|
||||
frontend=frontend,
|
||||
lang=args.lang,
|
||||
merge_sentences=merge_sentences, )
|
||||
mel = denorm(normalized_mel, am_mu, am_std)
|
||||
wav = get_lite_voc_output(voc_predictor=voc_predictor, input=mel)
|
||||
speed = wav.size / t.elapse
|
||||
rtf = fs / speed
|
||||
print(
|
||||
f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
|
||||
)
|
||||
|
||||
print("warm up done!")
|
||||
|
||||
N = 0
|
||||
T = 0
|
||||
block_size = args.block_size
|
||||
pad_size = args.pad_size
|
||||
get_tone_ids = False
|
||||
for utt_id, sentence in sentences:
|
||||
with timer() as t:
|
||||
# frontend
|
||||
frontend_dict = run_frontend(
|
||||
frontend=frontend,
|
||||
text=sentence,
|
||||
merge_sentences=merge_sentences,
|
||||
get_tone_ids=get_tone_ids,
|
||||
lang=args.lang)
|
||||
phone_ids = frontend_dict['phone_ids']
|
||||
phones = phone_ids[0].numpy()
|
||||
# acoustic model
|
||||
orig_hs = get_lite_am_sublayer_output(
|
||||
am_encoder_infer_predictor, input=phones)
|
||||
|
||||
if args.am_streaming:
|
||||
hss = get_chunks(orig_hs, block_size, pad_size)
|
||||
chunk_num = len(hss)
|
||||
mel_list = []
|
||||
for i, hs in enumerate(hss):
|
||||
am_decoder_output = get_lite_am_sublayer_output(
|
||||
am_decoder_predictor, input=hs)
|
||||
am_postnet_output = get_lite_am_sublayer_output(
|
||||
am_postnet_predictor,
|
||||
input=np.transpose(am_decoder_output, (0, 2, 1)))
|
||||
am_output_data = am_decoder_output + np.transpose(
|
||||
am_postnet_output, (0, 2, 1))
|
||||
normalized_mel = am_output_data[0]
|
||||
|
||||
sub_mel = denorm(normalized_mel, am_mu, am_std)
|
||||
# clip output part of pad
|
||||
if i == 0:
|
||||
sub_mel = sub_mel[:-pad_size]
|
||||
elif i == chunk_num - 1:
|
||||
# 最后一块的右侧一定没有 pad 够
|
||||
sub_mel = sub_mel[pad_size:]
|
||||
else:
|
||||
# 倒数几块的右侧也可能没有 pad 够
|
||||
sub_mel = sub_mel[pad_size:(block_size + pad_size) -
|
||||
sub_mel.shape[0]]
|
||||
mel_list.append(sub_mel)
|
||||
mel = np.concatenate(mel_list, axis=0)
|
||||
|
||||
else:
|
||||
am_decoder_output = get_lite_am_sublayer_output(
|
||||
am_decoder_predictor, input=orig_hs)
|
||||
am_postnet_output = get_lite_am_sublayer_output(
|
||||
am_postnet_predictor,
|
||||
input=np.transpose(am_decoder_output, (0, 2, 1)))
|
||||
am_output_data = am_decoder_output + np.transpose(
|
||||
am_postnet_output, (0, 2, 1))
|
||||
normalized_mel = am_output_data[0]
|
||||
mel = denorm(normalized_mel, am_mu, am_std)
|
||||
# vocoder
|
||||
wav = get_lite_voc_output(voc_predictor=voc_predictor, input=mel)
|
||||
|
||||
N += wav.size
|
||||
T += t.elapse
|
||||
speed = wav.size / t.elapse
|
||||
rtf = fs / speed
|
||||
|
||||
sf.write(output_dir / (utt_id + ".wav"), wav, samplerate=24000)
|
||||
print(
|
||||
f"{utt_id}, mel: {mel.shape}, wave: {wav.shape}, time: {t.elapse}s, Hz: {speed}, RTF: {rtf}."
|
||||
)
|
||||
|
||||
print(f"{utt_id} done!")
|
||||
print(f"generation speed: {N / T}Hz, RTF: {fs / (N / T) }")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Loading…
Reference in new issue